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This study examines how antitrust law adoptions affect horizontal merger and acquisition outcomes. Using the staggered introduction of competition laws in 20 countries, we find antitrust regulation decreases acquirers' 5-day cumulative abnormal returns surrounding horizontal merger announcements. A decrease in deal value, target book assets, and industry peers' announcement returns are consistent with the market power hypothesis. Exploiting antitrust law adoptions addresses a downward bias to an estimated effect of antitrust enforcement. The potential bias from heterogeneous treatment effects does not nullify our results. Overall, antitrust policies seem to deter post-merger monopolistic gains, potentially improving customer welfare. [ABSTRACT FROM AUTHOR]

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ABSTRACT The puzzling negative relation between liquidity uncertainty and asset returns, originally put forward by Chordia, Subrahmanyam, and Anshuman (2001) and confirmed by the subsequent empirical literature up to date, is neither robust to the aggregation period, nor to the observation frequency used to compute the volatility of trading volume. We demonstrate that their procedure involves an estimation bias due to the persistence and skewness of volumes. When using an alternative approach based on high‐frequency data to measure liquidity uncertainty, the relationship turns out to be positive. However, portfolio strategies based on liquidity uncertainty do not appear to be profitable. [ABSTRACT FROM AUTHOR]

Localising effects in space, time and other dimensions is a fundamental goal of magneto‐ and electroencephalography (EEG) research. A popular exploratory approach applies mass‐univariate statistics followed by cluster‐sum inferences, an effective way to correct for multiple comparisons while preserving high statistical power by pooling together neighbouring effects. Yet, these cluster‐based methods have an important limitation: each cluster is associated with a unique p‐value, such that there is no error control at individual timepoints, and one must be cautious about interpreting when and where effects start and end. Sassenhagen and Draschkow (2019) provided an important reminder of this limitation. They also reported results from a simulation, suggesting that onsets estimated from EEG data are both positively biased and very variable. However, the simulation lacked comparisons to other methods. Here, I report such comparisons in a new simulation, replicating the positive bias of the cluster‐sum method, but also demonstrating that it performs relatively well, in terms of bias and variability, compared to other methods that provide pointwise p‐values: two methods that control the false discovery rate and two methods that control the familywise error rate (cluster‐depth and maximum statistic methods). I also present several strategies to reduce estimation bias, including group calibration, group comparison and using binary segmentation, a simple change point detection algorithm that outperformed mass‐univariate methods in simulations. Finally, I demonstrate how to generate onset hierarchical bootstrap confidence intervals that integrate variability over trials and participants, a substantial improvement over standard group approaches that ignore measurement uncertainty. [ABSTRACT FROM AUTHOR]

Purpose: [18F]SynVesT-1, a positron emission tomography (PET) radiotracer for the synaptic vesicle glycoprotein 2A (SV2A), demonstrates kinetics similar to [11C]UCB-J, with high brain uptake, fast kinetics fitting well with the one-tissue compartment (1TC) model, and excellent test-retest reproducibility. Challenges arise due to the similarity between k2 and (efflux rate of the reference region), when applying the simplified reference tissue model (SRTM) and related methods in [11C]UCB-J studies to accurately estimate . This study evaluated the suitability of these methods to estimate [18F]SynVesT-1 binding using centrum semiovale (CS) or cerebellum (CER) as reference regions. Method: Seven healthy participants underwent 120-min PET scans on the HRRT scanner with [18F]SynVesT-1. Six participants underwent test and retest scans. Arterial blood sampling and metabolite analysis provided input functions for the 1TC model, serving as the gold standard for kinetic parameters values. SRTM, coupled SRTM (SRTMC) and SRTM2 estimated were applied to estimate (ref: CS) and DVRCER(ref: CER) values. For SRTM2, the population average of was determined from the 1TC model applied to the reference region. Test-retest variability and minimum scan time were also calculated. Results: The 1TC k2 (1/min) values for CS and CER were 0.031 ± 0.004 and 0.021 ± 0.002, respectively. Although SRTMC was much higher than 1TC , SRTMC underestimated BPND(ref: CS) and DVRCER by an average of 3% and 1% across regions, respectively, due to similar bias in k2 and estimation. SRTM underestimated BPND(ref: CS) by an average of 3%, but with the CER as reference region, SRTM estimation was unstable and DVRCER underestimation varied by region (mean 10%). Using population average values, SRTM2 BPND and DVRCER showed the best agreement with 1TC estimates. Conclusion: Our findings support the use of population value in SRTM2 with [18F]SynVesT-1 for the estimation of or DVRCER, regardless of the choice of reference region. [ABSTRACT FROM AUTHOR]

Purpose: To compare the performance of multi‐echo (ME) and time‐division multiplexing (TDM) sequences for accelerated relaxation‐diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. Method: The ME, TDM, and the reference single‐echo (SE) sequences with six TEs were implemented using Pulseq with single‐band (SB) and multi‐band 2 (MB2) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized RMS error (NRMSE). Shinnar–Le Roux (SLR) pulses were implemented for the SB‐ME and SB‐SE sequences to investigate the impact of slice profiles on ME sequences. For the in‐vivo brain scan, besides the image intensity comparison and T2‐estimates, different methods were used to assess sequence‐related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum‐entropy relaxation diffusion distribution (MaxEnt‐RDD). Results: TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3–4× larger NRMSEs for phantom, 2× for in‐vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB‐ME exhibited a 60% larger bias in the map and on average 3.5× larger bias in the covariance between relaxation‐diffusion coefficients. Conclusion: Our analysis demonstrates that TDM provides a more accurate estimation of relaxation‐diffusion measurements while accelerating the acquisitions by a factor of 2 to 3. [ABSTRACT FROM AUTHOR]

A facing-towards bias is commonly reported when observers are asked to judge the motion directions of others. However, it remains unclear just how accurately observers are able to estimate the motion direction of others and whether the gender of the observer and the walker affects the direction estimation. Here, we asked male and female participants to estimate the direction of a point-light walker (PLW) in three experiments. The gender of PLWs was neutral (Experiment 1, 96 participants), clearly male or female (Experiment 2, 72 participants), or more subtlety male or female (Experiment 3, 98 participants). We found that female PLWs showed a stronger reference repulsion bias (RRB) than male PLWs. That is, for female PLWs, the estimates of facing directions were biased away from the boundaries of facing-towards, facing-away, and lateral (left/right)-motion directions. Interestingly, RRBs differed depending on whether the observer was male or female. When the PLW gender difference was clear, the RRB was stronger for female participants than male participants; when the PLW gender difference was reduced, the trend disappeared or was reversed. Finally, the perceived PLW direction was biased towards the previously seen PLW direction, showing serial dependence that was not affected by the PLW and observer genders. In conclusion, the current study shows that observers can accurately estimate PLW directions, but that judgments are curiously affected by both the observers' and PLWs' genders. [ABSTRACT FROM AUTHOR]

ABSTRACT Probability surveys are challenged by increasing nonresponse rates, resulting in biased statistical inference. Auxiliary information about populations can be used to reduce bias in estimation. Often continuous auxiliary variables in administrative records are first discretized before releasing to the public to avoid confidentiality breaches. This may weaken the utility of the administrative records in improving survey estimates, particularly when there is a strong relationship between continuous auxiliary information and the survey outcome. In this paper, we propose a two‐step strategy, where the confidential continuous auxiliary data in the population are first utilized to estimate the response propensity score of the survey sample by statistical agencies, which is then included in a modified population data for data users. In the second step, data users who do not have access to confidential continuous auxiliary data conduct predictive survey inference by including discretized continuous variables and the propensity score as predictors using splines in a Bayesian model. We show by simulation that the proposed method performs well, yielding more efficient estimates of population means with 95% credible intervals providing better coverage than alternative approaches. We illustrate the proposed method using the Ohio Army National Guard Mental Health Initiative (OHARNG‐MHI). The methods developed in this work are readily available in the R package AuxSurvey. [ABSTRACT FROM AUTHOR]

Sensorimotor synchronization (SMS) is the mainly specifically human ability to move in sync with a periodic external stimulus, as in keeping pace with music. The most common experimental paradigm to study its largely unknown underlying mechanism is the paced finger-tapping task, where a participant taps to a periodic sequence of brief stimuli. Contrary to reaction time, this task involves temporal prediction because the participant needs to trigger the motor action in advance for the tap and the stimulus to occur simultaneously, then an error-correction mechanism takes past performance as input to adjust the following prediction. In a different, simpler task, it has been shown that exposure to a distribution of individual temporal intervals creates a "temporal context" that can bias the estimation/production of a single target interval. As temporal estimation and production are also involved in SMS, we asked whether a paced finger-tapping task with period perturbations would show any time-related context effect. In this work we show that a perturbation context can indeed be generated by exposure to period perturbations during paced finger tapping, affecting the shape and size of the resynchronization curve. Response asymmetry is also affected, thus evidencing an interplay between context and intrinsic nonlinearities of the correction mechanism. We conclude that perturbation context calibrates the underlying error-correction mechanism in SMS. [ABSTRACT FROM AUTHOR]

In this article, we propose methods to minimize bias due to unit nonresponse. We consider a two-phase sampling design where the second phase is a probability subsample of nonrespondents from the first phase. In this context, we propose three weighting procedures to estimate the total when not all units in the subsample respond. The weighting is based on the response homogeneity group (RHG) model. Given the RHG model, theoretical results on bias and variance estimation are obtained for all estimators. In a simulation study, we evaluate the empirical properties of the three estimators as well as of estimators based on two commonly used procedures to handle unit nonresponse in single-phase sampling design. These two procedures include: (i) nonresponse calibration weighting, also known as the one-step approach, and (ii) nonresponse probability weighting followed by calibration, also known as the two-step approach. Our results indicate that when there is significant deviation from the assumed RHG model, the nonresponse follow-up estimators perform better in terms of bias and coverage. [ABSTRACT FROM AUTHOR]

Nuclear proliferation literature typically differentiates supply-side and demand-side factors influencing the spread of nuclear weapons. These distinct approaches to the proliferation puzzle raise the following empirical questions: Does nuclear supply stimulate states' demand for nuclear weapons? Conversely, does the demand for nuclear weapons really facilitate the acquisition of nuclear supply? If such endogeneity exists between the demand-side and supply-side determinants, how would it cause empirical bias in the estimation of their effects on nuclear proliferation? This article aims to unpack endogenous mechanisms of nuclear demand and nuclear supply over the course of nuclear proliferation. In particular, it examines two potential sources of endogeneity: (1) simultaneous interactions between states' nuclear development decisions and nuclear technological capability and (2) selection bias in nuclear development. To address each source of endogeneity, simultaneous equation models and the duration models with selection are estimated, respectively. Contrary to what recent supply-side literature suggests, the empirical analyses reveal that states' nuclear demand is primarily driven by external security threats instead of their existing nuclear technology, and that their successful acquisition of nuclear technology mainly follows as the result of nuclear development efforts but does not necessarily depend on individual supply-side factors. This article addresses the typical inference issues in nuclear proliferation research and contributes to our synthetic understanding of proliferation mechanisms. [ABSTRACT FROM AUTHOR]

In the widely used predictive regression model, any possible serial correlation in innovations leads to estimation bias and statistical inference distortions. Hence, it is important to pretest the existence of such serial correlation. Nevertheless, in the presence of embedded endogeneity, which is a common problem in the predictive regression setting, traditional serial correlation tests such as Box–Pierce (BP) and Ljung–Box (LB) tests are found to perform poorly. Motivated by this, we develop a new portmanteau test in this article as a pretest for serial correlation in predictive regression under possible embedded endogeneity. This test is based on the sample splitting idea and the jackknife empirical likelihood method. The asymptotic distribution of the proposed test has been derived, and the Monte Carlo simulations confirm good finite sample performances. As an illustration, we apply our proposed test in pretesting the serial correlation in predictive regression, where financial variables are used to predict the excess return of S&P 500. [ABSTRACT FROM AUTHOR]

Purpose: Foreign subsidiaries of multinational enterprises (MNEs) operate in complex and competitive international environments, implement market and non-market strategies, manage resources and value-added activities and contribute to the overall performance of their parent firms. Thus, the research question on the determinants of MNE foreign subsidiaries' performance is of interest to managers and academic researchers. The empirical literature has flourished over the recent decades; however, the domains are fragmented, and the findings are inclusive. The purpose of this study is to systematically review, analyse and synthesize the empirical articles in this area, identify research gaps and suggest a future research agenda. Design/methodology/approach: This study uses the qualitative content analysis method in reviewing and analysing 150 articles published in 24 scholarly journals during the period 2000–2023. Findings: The literature uses a variety of theoretical perspectives to examine the key determinants of subsidiary performance which can be grouped into six major domains, namely, home- and host country-level factors; distance between home and host countries; the characteristics of parent firms and of subsidiaries; and governance mechanisms (the establishment modes and ownership strategy, subsidiary autonomy and the use of home country expatriates for transferring knowledge from the headquarters and controlling foreign subsidiaries). A range of objective and subjective indicators are used to measure subsidiary performance. Yet, the research shows a lack of broader integration of theories and presents inconsistent theoretical predictions, inconclusive empirical findings and estimation bias, which hinder our understanding of how the determinants independently and jointly shape the performance of foreign subsidiaries. Originality/value: This study provides a comprehensive, nuanced and systematic review that synthesizes and clarifies the determinants of subsidiary performance, offers deeper insights from both theoretical, methodological and empirical aspects and proposes some promising avenues for future research directions. [ABSTRACT FROM AUTHOR]

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4966. [ABSTRACT FROM AUTHOR]

Whereas users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this privacy paradox is that, when an individual shares data, it is not just the individual's privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this paper, we study the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We design an incentive-compatible mechanism that optimizes the worst case trade-off between bias and variance of the estimation subject to a budget constraint, with which the worst case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and nonmonotonicity properties of the marketplace. Funding: This work is supported by the National Natural Science Foundation of China [Grants 62202512 and 62271434], Shenzhen Science and Technology Program [Grant JCYJ20210324120011032], Guangdong Basic and Applied Basic Research Foundation [Grant 2021B1515120008], Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network [Grant ZDSYS20220606100601002], and the Shenzhen Institute of Artificial Intelligence and Robotics for Society. This work is also supported by the National Science Foundation [Grants CNS-2146814, CPS-2136197, CNS-2106403, and NGSDI-2105648]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0022. [ABSTRACT FROM AUTHOR]

Parameters of differential equations are essential to characterize intrinsic behaviours of dynamic systems. Numerous methods for estimating parameters in dynamic systems are computationally and/or statistically inadequate, especially for complex systems with general-order differential operators, such as motion dynamics. This article presents Green's matching, a computationally tractable and statistically efficient two-step method, which only needs to approximate trajectories in dynamic systems but not their derivatives due to the inverse of differential operators by Green's function. This yields a statistically optimal guarantee for parameter estimation in general-order equations, a feature not shared by existing methods, and provides an efficient framework for broad statistical inferences in complex dynamic systems. [ABSTRACT FROM AUTHOR]

Wild populations are increasingly threatened by human-mediated climate change and land use changes. As populations decline, the probability of inbreeding increases, along with the potential for negative effects on individual fitness. Detecting and characterizing runs of homozygosity (ROHs) is a popular strategy for assessing the extent of individual inbreeding present in a population and can also shed light on the genetic mechanisms contributing to inbreeding depression. Here, we analyze simulated and empirical datasets to demonstrate the downstream effects of program selection and long-term demographic history on ROH inference, leading to context-dependent biases in the results. Through a sensitivity analysis we evaluate how various parameter values impact on ROH-calling results, highlighting its utility as a tool for parameter exploration. Our results indicate that ROH inferences are sensitive to factors such as sequencing depth and ROH length distribution, and with bias direction and magnitude varying with demographic history and the programs used. Estimation biases are particularly pronounced at lower sequencing depths, potentially leading to either underestimation or overestimation of inbreeding. These results are particularly important for the management of endangered species, as underestimating inbreeding signals in the genome can substantially undermine conservation initiatives. We also found that small true ROHs can be incorrectly lumped together and called as longer ROHs, leading to erroneous inference of recent inbreeding. To address these challenges, we suggest using a combination of ROH detection tools and ROH length-specific inferences, along with sensitivity analysis, to generate robust and context-appropriate population inferences regarding inbreeding history. We outline these recommendations for ROH estimation at multiple levels of sequencing effort, which are typical of conservation genomics studies. Author summary: In this study, we explored how various factors affect the accuracy of detecting runs of homozygosity (ROH). ROHs are long sections of the genome where both inherited DNA strands are identical due to shared ancestry in the maternal and paternal lines. Because these sections reflect shared ancestry, ROHs are useful for measuring inbreeding in populations, which can negatively impact individual fitness and species survival. We analyzed simulated and empirical data to examine how factors such as demographic history and sequencing depth influence ROH detection. Our results show that these estimates of inbreeding are highly sensitive to the analysis software and data quality. Misidentifying ROHs can lead to significant overestimates or underestimates of inbreeding, potentially complicating conservation efforts. To improve accuracy, we recommend using multiple analysis methods and conducting sensitivity analyses to account for data variability. These findings are particularly relevant for managing endangered species, where precise genetic information is critical for effective conservation planning. [ABSTRACT FROM AUTHOR]

The Priority Arbiter-based Physical Unclonable Function (PA-PUF) is a one-way function implemented in hardware, where output or response bits, corresponding to a given set of challenge bits, are influenced by the device-specific parameters. An n -length PA-PUF can be represented as an n -variable Boolean function. The research we conducted demonstrates the exploration of Boolean functions obtained by implementing a PA-PUF construction with randomly selected delay parameters. We observe that the set of Boolean functions generated from PA-PUF is significantly larger than the set of Boolean functions generated from classical Arbiter-based PUF. Further, we look into the bias estimation of the response bit generated from PA-PUF towards the impact of changing specific bits in the challenge input. Finally, we perform a comparative analysis on PA-PUF to study its cryptographic properties and a detailed analysis of the machine learning attacks. [ABSTRACT FROM AUTHOR]

Most statistical and machine learning models used for binary data modeling and classification assume that the data are balanced. However, this assumption can lead to poor predictive performance and bias in parameter estimation when there is an imbalance in the data due to the threshold election for the binary classification. To address this challenge, several authors suggest using asymmetric link functions in binary regression, instead of the traditional symmetric functions such as logit or probit, aiming to highlight characteristics that would help the classification task. Therefore, this study aims to introduce new classification functions based on the Lomax distribution (and its variations; including power and reverse versions). The proposed Bayesian functions have proven asymmetry and were implemented in a Stan program into the R workflow. Additionally, these functions showed promising results in real-world data applications, outperforming classical link functions in terms of metrics. For instance, in the first example, comparing the reverse power double Lomax (RPDLomax) with the logit link showed that, regardless of the data imbalance, the RPDLomax model assigns effectively lower mean posterior predictive probabilities to failure and higher probabilities to success (21.4% and 63.7%, respectively), unlike Logistic regression, which does not clearly distinguish between the mean posterior predictive probabilities for these two classes (36.0% and 39.5% for failure and success, respectively). That is, the proposed asymmetric Lomax approach is a competitive model for differentiating binary data classification in imbalanced tasks against the Logistic approach. [ABSTRACT FROM AUTHOR]

AbstractScale in multiscale geographically weighted regression (MGWR) directly impacts the accuracy of coefficient estimates and shapes the comprehensive evaluation of the intensity of spatially non-stationary relationships. Presently, MGWR primarily utilizes back-fitting for sequentially optimizing multiple scales (MGWR-BF). However, the set of individual optima obtained through sequential optimization may not necessarily represent the global optimum. To address this issue, this paper proposes a multi-scale cooperative optimization within MGWR (MGWR-GA) model. Specifically, MGWR-GA employs a genetic algorithm to simultaneously input potential scale combinations, each comprising P scales. Subsequently, it introduces a dedicated overall estimation algorithm designed for these P scales, ultimately determining the optimal scale combinations based on the AICc. Simulation experiments have shown that, at least for global stationarity, the scales obtained by MGWR-GA approximate the true values across twelve different test environments. Additionally, the coefficient estimation bias of MGWR-GA is lower than that of MGWR-BF, especially in low signal-to-noise ratio settings. Empirical experiments further confirm the effectiveness of MGWR-GA in identifying both globally stationary and locally non-stationary scales. Furthermore, MGWR-GA outperforms MGWR-BF in terms of goodness-of-fit, adjusted goodness-of-fit, AICc and spatial autocorrelation of residuals. These findings indicate that MGWR-GA can serve as a valuable tool for modeling spatially non-stationary relationships. [ABSTRACT FROM AUTHOR]

Summary Sketching technologies have recently emerged as a promising solution for real-time, large-scale phylogenetic analysis. However, existing sketching-based phylogenetic tools exhibit drawbacks, including platform restrictions, deficiencies in tree visualization, and inherent distance estimation bias. These limitations collectively impede the overall convenience and efficiency of the analysis. In this study, we introduce Kssdtree, an interactive Python package designed to address these challenges. Kssdtree surpasses other sketching-based tools by demonstrating superior performance in terms of both accuracy and time efficiency on comprehensive benchmarking datasets. Notably, Kssdtree offers key advantages such as intra-species phylogenomic analysis and GTDB-based phylogenetic placement analysis, significantly enhancing the scope and depth of phylogenetic investigations. Through extensive evaluations and comparisons, Kssdtree stands out as an efficient and versatile method for real-time, large-scale phylogenetic analysis. Availability and implementation The Kssdtree Python package is freely accessible at https://pypi.org/project/kssdtree and source code is available at https://github.com/yhlink/kssdtree. The documentation and instantiation for the software is available at https://kssdtree.readthedocs.io/en/latest. The video tutorial is available at https://youtu.be/_6hg59Yn-Ws. [ABSTRACT FROM AUTHOR]

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information–theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of n = 3 variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller–Maddow method is derived for the O-information. We find that for systems of n = 3 variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information. [ABSTRACT FROM AUTHOR]

The lifetime data collected from the field are usually heavily censored, in which case, getting an accurate reliability evaluation based on heavily censored data is challenging. For heavily Type‐II censored data, the parameters estimation bias of traditional methods (i.e., maximum likelihood estimation (MLE) and least squares estimation (LSE)) are still large, and Bayesian methods are hard to specify the priors in practice. Therefore, considering the existing range of shape parameter for Weibull distribution, this study proposes two novel parameter estimation methods, the three‐step MLE method and the hybrid estimation method. For the three‐step MLE method, the initial estimates of shape and scale parameters are first respectively derived using MLE, then are updated by the single parameter MLE method with the range constraint of shape parameter. For the hybrid estimation method, the shape parameter is estimated by the LSE method with the existing range constraint of shape parameter, then the scale parameter estimate can be obtained by MLE. On this basis, two numerical examples are performed to demonstrate the consistency and effectiveness of the proposed methods. Finally, a case study on turbine engines is given to verify the effectiveness and applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

Aim: This study evaluated the bias and accuracy of the CKD‐EPI/CKiD and EKFC equations compared with the reference exogenous tracer‐based assessment of glomerular filtration rate (GFR) in adult and pediatric patients according to their renal transplant status. Methods: We assessed the bias and P30 accuracy of the CKD‐EPI/CKiD and EKFC equations compared with iohexol‐based GFR measurement. Results: In the overall population (n = 59), the median age was 29 years (IQR, 16.0–46.0) and the median measured GFR was 73.9 mL/min/1.73m2 (IQR, 57.3–84.6). Among non‐kidney transplant patients, the median was 77.7 mL/min/1.73m2 (IQR, 59.3–86.5), while among kidney transplant patients, it was 60.5 mL/min/1.73m2 (IQR, 54.2–66.8). The bias associated with the EKFC and CKD‐EPI/CKiD equations was significantly higher among kidney transplant patients than among non‐kidney transplant patients, with a difference between medians (Hodges–Lehmann) of +10.4 mL/min/1.73m2 (95% CI, 2.2–18.9; p =.02) for the EKFC and +12.1 mL/min/1.73m2 (95% CI, 4.2–21.4; p =.006) for the CKD‐EPI/CKiD equations. In multivariable analysis, kidney transplant status emerged as an independent factor associated with a bias of >3.4 mL/min/1.73m2 (odds ratio, 7.7; 95% CI, 1.4–43.3; p =.02) for the EKFC equation and a bias of >13.4 mL/min/1.73m2 (odds ratio, 15.0; 95% CI, 2.6–85.7; p =.002) for the CKD‐EPI/CKiD equations. Conclusion: In our study, which included adolescent and young adult kidney transplant patients, both the CKD‐EPI/CKiD and EKFC equations tended to overestimate the measured glomerular filtration rate, with the EKFC equation exhibiting less bias. Renal transplant status significantly influenced the degree of estimation bias. [ABSTRACT FROM AUTHOR]

Analysis of binary matched pairs data is problematic due to infinite maximum likelihood estimates of the log odds ratio and potentially biased estimates, especially for small samples. We propose a penalised version of the log-likelihood function based on adjusted responses which always results in a finite estimator of the log odds ratio. The probability limit of the adjusted log-likelihood estimator is derived and it is shown that in certain settings the maximum likelihood, conditional and modified profile log-likelihood estimators drop out as special cases of the former estimator. We implement indirect inference to the adjusted log-likelihood estimator. It is shown, through a complete enumeration study, that the indirect inference estimator is competitive in terms of bias and variance in comparison to the maximum likelihood, conditional, modified profile log-likelihood and Firth's penalised log-likelihood estimators. [ABSTRACT FROM AUTHOR]

Abstract A facing-towards bias is commonly reported when observers are asked to judge the motion directions of others. However, it remains unclear just how accurately observers are able to estimate the motion direction of others and whether the gender of the observer and the walker affects the direction estimation. Here, we asked male and female participants to estimate the direction of a point-light walker (PLW) in three experiments. The gender of PLWs was neutral (Experiment 1, 96 participants), clearly male or female (Experiment 2, 72 participants), or more subtlety male or female (Experiment 3, 98 participants). We found that female PLWs showed a stronger reference repulsion bias (RRB) than male PLWs. That is, for female PLWs, the estimates of facing directions were biased away from the boundaries of facing-towards, facing-away, and lateral (left/right)-motion directions. Interestingly, RRBs differed depending on whether the observer was male or female. When the PLW gender difference was clear, the RRB was stronger for female participants than male participants; when the PLW gender difference was reduced, the trend disappeared or was reversed. Finally, the perceived PLW direction was biased towards the previously seen PLW direction, showing serial dependence that was not affected by the PLW and observer genders. In conclusion, the current study shows that observers can accurately estimate PLW directions, but that judgments are curiously affected by both the observers’ and PLWs’ genders.

Purpose: This study aims to assess how T2 heterogeneity biases IMPULSED‐derived metrics of tissue microstructure in solid tumors and evaluate the potential of estimating multi‐compartmental T2 and microstructural parameters simultaneously. Methods: This study quantifies the impact of T2 relaxation on IMPULSED‐derived microstructural parameters using computer simulations and in vivo multi‐TE IMPULSED MRI in five tumor models, including brain, breast, prostate, melanoma, and colon cancer. A comprehensive T2 + IMPULSED method was developed to fit multi‐compartmental T2 and microstructural parameters simultaneously. A Bayesian model selection approach was carried out voxel‐wisely to determine if the T2 heterogeneity needs to be included in IMPULSED MRI in cancer. Results: Simulations suggest that T2 heterogeneity has a minor effect on the estimation of d in tissues with intermediate or high cell density, but significantly biases the estimation of vin$$ {v}_{in} $$ with low cell density. For the in vivo animal experiments, all IMPULSED metrics except vin$$ {v}_{in} $$ are statistically independent on TE. For B16 tumors, the IMPULSED‐derived vin$$ {v}_{in} $$ exhibited a notable increase with longer TEs. For MDA‐MB‐231 tumors, IMPULSED‐derived vin$$ {v}_{in} $$ showed a significant increase with increasing TEs. The T2 + IMPULSED‐derived T2in$$ {T}_2^{in} $$ of all five tumor models are consistently smaller than T2ex$$ {T}_2^{ex} $$. Conclusions: The findings from this study highlight two key observations: (i) TE has a negligible impact on IMPULSED‐derived cell sizes, and (ii) the TE‐dependence of IMPULSED‐derived intracellular volume fractions used in T2 + IMPULSED modeling to estimate T2in$$ {T}_2^{in} $$ and T2ex$$ {T}_2^{ex} $$. These insights contribute to the ongoing development and refinement of non‐invasive MRI techniques for measuring cell sizes. [ABSTRACT FROM AUTHOR]

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Trauma is an extreme event that offers us clues about individual differences in cognitive processes, including the development and maintenance of cognitive biases such as exaggerated probability estimates toward future traumatic events. The current study investigated probability estimate biases toward future traumatic events in 445 undergraduate students (93.9% Hispanic) who experienced one of the following traumatic events or no trauma: childhood interpersonal trauma, adult interpersonal trauma, accident, a family member's death due to COVID-19, or no trauma. Participants completed online questionnaires assessing probability estimates for two types of future traumas (interpersonal violence traumas and accidental traumas) and posttraumatic stress symptoms. The childhood trauma group generally showed the strongest probability estimate biases for future traumatic events among the four trauma-exposed groups. Similarly elevated probability estimates for future traumatic events were found in the other three trauma-exposed groups. Limited associations between levels of posttraumatic stress symptoms and probability estimate biases for future traumatic events were found. This study was the first to reveal the presence of biased probability estimates for future traumatic events in Hispanics exposed to a traumatic event. The results suggest that probability estimation biases developed in response to a specific traumatic event might become generalized to other types of traumatic events. [ABSTRACT FROM AUTHOR]

The estimation of the extreme value index (EVI) is a crucial task in the field of statistics of extremes, as it provides valuable insights into the tail behavior of a distribution. For models with a Pareto-type tail, the Hill estimator is a popular choice. However, this estimator is susceptible to bias, which can lead to inaccurate estimations of the EVI, impacting the reliability of risk assessments and decision-making processes. This paper introduces a novel reduced-bias generalized Hill estimator, which aims to enhance the accuracy of EVI estimation by mitigating the bias. [ABSTRACT FROM AUTHOR]

The Priestley–Taylor (PT) coefficient (α) is generally set as a constant value or is fitted as an empirical function of environmental variables, and it can bias the evaporation estimation or hydrological projections under global warming. By using an atmospheric boundary layer model, this study derives a theoretical and parameter-free equation for estimating α as a function of air temperature (T) and specific humidity (Q). With observations from several waterbodies and non-water-limited land sites, we demonstrate that, in addition to estimating the value of α well, the derived expressions can also capture the sensitivity of α to T and Q , that is, dα/dT and dα/dQ. α is generally negatively associated with T and Q , in which regard T plays a more fundamental role in controlling α behaviors. Based on climate model data, we further show that this negative relationship between α and T is of great importance for long-term hydrological predictions. We also provide a lookup graph for practical and broad uses to directly find the values of dα/dT and dα/dQ under specific conditions. Overall, the derived expression gives a physically clear and straightforward approach to quantify changes in α , which is essential for PT-based hydrological simulation and projections. [ABSTRACT FROM AUTHOR]

People naturally seek an interpersonal distance that feels comfortable, striking a balance between not being too close or too far from others until reaching a state of equilibrium. Previous studies on interpersonal distance preferences among autistic individuals have yielded inconsistent results. Some show a preference for greater distance, while others indicate a preference for shorter distances, or reveal higher variance in preferences among autistic individuals. In a related vein, previous studies have also investigated the way autistics accurately judge distance, and these studies have received inconsistent results, with some showing superior spatial abilities and others indicating biases in distance estimations. However, the link between distance estimation and preference has never been examined. To address this gap, our study measured interpersonal distance preferences and estimations and tested the correlation between the two factors. The results indicate greater variance among autistic people in both the preference of distance and the ability to estimate distance accurately, suggesting that inconsistencies in previous studies originate from greater individual differences among autistics. Furthermore, only among autistic individuals were interpersonal distance preference and estimation bias associated in a manner that violated equilibrium. Underestimation bias (judging others as closer than they are) was linked to a preference for closer proximity, while overestimation bias (judging others as further away) was associated with a preference for maintaining a greater distance. This connection suggests that biases in the estimation of interpersonal distance contribute to extreme preferences (being too close or too far away). Taken together, the findings suggest that biases in the estimation of interpersonal distance are associated with socially inappropriate distance preferences among autistics. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a rise in the popularity of information integration without sharing of raw data. By leveraging and incorporating summary information from external sources, internal studies can achieve enhanced estimation efficiency and prediction accuracy. However, a noteworthy challenge in utilizing summary-level information is accommodating the inherent heterogeneity across diverse data sources. In this study, we delve into the issue of prior probability shift between two cohorts, wherein the difference of two data distributions depends on the outcome. We introduce a novel semi-parametric constrained optimization-based approach to integrate information within this framework, which has not been extensively explored in existing literature. Our proposed method tackles the prior probability shift by introducing the outcome-dependent selection function and effectively addresses the estimation uncertainty associated with summary information from the external source. Our approach facilitates valid inference even in the absence of a known variance-covariance estimate from the external source. Through extensive simulation studies, we observe the superiority of our method over existing ones, showcasing minimal estimation bias and reduced variance for both binary and continuous outcomes. We further demonstrate the utility of our method through its application in investigating risk factors related to essential hypertension, where the reduced estimation variability is observed after integrating summary information from an external data. [ABSTRACT FROM AUTHOR]

Accurately estimating effective population size (Ne) is essential for understanding evolutionary processes and guiding conservation efforts. This study investigates Ne estimation methods in spatially structured populations using a population of moor frog (Rana arvalis) as a case study. We assessed the behaviour of Ne estimates derived from the linkage disequilibrium (LD) method as we changed the spatial configuration of samples. Moor frog eggs were sampled from 25 breeding patches (i.e., separate vernal ponds, ditches or parts of larger fens) within a single population, revealing an isolation‐by‐distance pattern and a local spatial genetic structure. Varying buffer sizes around each patch were used to examine the impact of sampling window size on the estimation of effective number of breeders (Nb). Our results indicate a downward bias in LD Nb estimates with increasing buffer size, suggesting an underestimation of Nb. The observed bias is attributed to LD resulting from including genetically divergent individuals (mixture‐LD) confounding LD due to drift. This emphasises the significance of considering even subtle spatial genetic patterns. The implications of these findings are discussed, emphasising the need to account for spatial genetic structure to accurately assess population viability and inform conservation efforts. This study contributes to our understanding of the challenges associated with Ne estimation in spatially structured populations and underscores the importance of refining methodologies to address population‐specific spatial dynamics for effective conservation planning and management. [ABSTRACT FROM AUTHOR]

Objective: Due to inconsistent subjective assessment criteria and excessively long calculation responses, there is a high risk of mistakenly discarding suspected hemolytic plasma and inappropriately discarding suspected non-hemolytic plasma during plasma preparation, posing significant risks to patient safety and leading to waste. This study aims to resolue these problems. Methods: A thresholding method that integrates deep learning with color-changing ink concepts was developed. By employing an enhanced U-Net architecture for image segmentation, the study introduces an advanced attention mechanism, batch normalization, and a padding module to tackle issues such as mean estimation bias, computational inefficiencies, and limited receptive field sizes in spatial mapping relationships. The model was validated and compared using a self-collected sample dataset. Results: This study employed the color-changing ink boundary method for classification, enhancing the computational efficiency of plasma discrimination and reducing discrimination time, while ensuring the accuracy of plasma sample identification. The accuracy rate of the experimental results is 99.52%. Conclusion: The results indicate that the plasma discrimination accuracy of this model is superior to other discrimination models, and it is expected to be applied in clinical practice. [ABSTRACT FROM AUTHOR]

Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors to improve the goodness-of-fit of the model to observed data, and deconfound the mediators and outcome simultaneously. One major advantage of the proposed framework is that it uses the causal pathway structure from cause to outcome via multiple mediators to debias the causal effect without requiring external information on latent confounders. In addition, the proposed framework is flexible in terms of integrating powerful nonparametric prediction algorithms while retaining interpretable mediation effects. In theory, we establish the identification of both causal and mediation effects based on the proposed deconfounding method. Numerical experiments on both simulation settings and a normative aging study indicate that the proposed approach reduces the estimation bias of both causal and mediation effects. for this article are available online. [ABSTRACT FROM AUTHOR]

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99 % probability. The average AoI quantity is maintained within the 5–10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties. [ABSTRACT FROM AUTHOR]

Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of "anchor" observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement: This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data. [ABSTRACT FROM AUTHOR]

Although Q-learning (QL) is widely used in reinforcement learning, it suffers from overestimation bias, which can lead to poor performance in stochastic environments due to its susceptibility to maximization bias. To address this problem, various bias correction mechanisms have been proposed. However, while these mechanisms may reduce overestimation bias, some of them introduce underestimation bias, which is undesirable in some environments. To leverage both overestimation and underestimation biases, we introduce an underestimation mechanism called the min estimator, followed by our proposed Max_Mix_Min Q-learning (M3QL) method, which incorporates a balance parameter β . Our method also considers the number of N Q-functions. Initially, we theoretically analyze why our method benefits from both overestimation and underestimation bias under the assumption of different bias distributions and how sample size N affects the performance of our method. Additionally, we visualize the theoretic analysis results on Meta-chain MDP example. Theoretical analysis demonstrates that M3QL achieves bias reduction compared to QL and the underestimation mechanism. Furthermore, we theoretically prove that M3QL is unbiased on certain values of β . In experimental comparisons with states of arts on Atari benchmark problems, our method consistently outperforms them. We also compare M3QL with the underestimation mechanism and Deep Q-learning (DQN) on these benchmark problems, revealing that M3QL improves the performance of underestimation mechanism and DQN on most of the benchmark problems. [ABSTRACT FROM AUTHOR]

Single-index model is one of the most popular semiparametric models in applied econometrics. Estimation of the derivative function is often of crucial importance, as studying "marginal effects" serves as a cornerstone of microeconomics. A prerequisite for the successful application of nonparametric/semiparametric kernel estimation methods is to select smoothing parameters properly to balance the estimation squared bias and variance. Henderson et al. (2015) propose a novel method for selecting the smoothing parameters optimally for derivative function estimation. However, their method suffers from the "curse of diemnsionality" problem in a multivariate nonparametric regression model. In this article, we extend the work of Henderson et al. (2015) to estimation of the derivative function of a single-index model. Specifically, we propose a data-driven method to select smoothing parameters optimally for single-index model derivative function estimation. We also derive the asymptotic distribution of the resulting local linear estimator of the derivative function. Both simulations and empirical applications show that the proposed method works well in practice. [ABSTRACT FROM AUTHOR]

Changes in the thermospheric space environment directly affect the movement of Low-Earth-Orbit space targets. The public release of the new generation empirical model NRLMSIS 2.1 provides a new option for estimating thermospheric mass density. This paper analyzes the mass density estimation performance of NRLMSIS 2.1 model using GRACE-A and SWARM-C observations during 2011–2017. First, the results show that compared with the NRLMSISE-00, NRLMSIS 2.1 systematically underestimated mass density by around 10 %. Second, the performance of the models decreased in the middle of most years, which may be due to the lack of a detailed expression of the models for the phenomenon of global density reduction during this period. Third, the biases of the model estimations do not show obvious change characteristics with changing of latitude, but the correlation with satellite observations shows a less downward trend with increasing latitude. Fourth, during the selected periods of 90 space weather events from 2011 to 2017, the estimation performance of NRLMSIS 2.1 faced varying degrees of challenges. Taking satellite observations as references, in extreme cases, the relative deviation of the NRLMSISE-00 model exceed 70 %, while that of NRLMSIS 2.1 is about 50 %. The results indicate that technological iteration and the addition of new data enhance the overall stability of the NRLMSIS 2.1 model. [ABSTRACT FROM AUTHOR]

After the 1990s, global trade has seen a sudden proliferation of regional trade agreements (RTAs). Existing empirical literature argues about the differential impact of these trade agreements on high-income and lower-middle income countries' trade. Further, economic and political arguments have developed to justify the proliferation of such trade negotiations. In this article, we have tried to build an argument taking economic and non-economic rationales of the formation of trade agreements as a complement to each other. Unlike the majority of existing literature in this field, our empirical analysis is an establishment of this debate which provides a platform to consider RTA formation to be an endogenous rather than exogeneous decision. Further, RTAs of varying nature are related differently to the unobservable which impede or facilitate trade. These unobservable also create endogeneity bias in the traditional estimation of RTAs' impact on the trade flows of a country. In this study, we have attempted to investigate the endogeneity in the context of RTA engagements using the Heckman control function approach. This study ensures the endogeneity of RTAs with their positive and significant impact on trade flows. This results do not significantly differ from similar kinds of empirical studies available on international platforms. JEL Classification: F1, F12, F14 [ABSTRACT FROM AUTHOR]

Early detection of thyroid nodules enables personalized treatments, reducing mortality and morbidity rates. But, obtaining sufficient ultrasound images is challenging, often resulting in insufficient data which hinders the effective development of innovative techniques. In this research, the Improved Gazelle Optimization Algorithm with Extreme Gradient Boosting (IGOA -XGBoost) is proposed for the classification of thyroid ultrasound images. The CNN is used to extract relevant features as it easily identifies the ultrasound images. The CNN output is given to the XGBoost classifier to classify the input image into benign or malignant classes. Logistic mapping is initialized in traditional GOA to optimize the hyperparameters of XGBoost and achieve better global search and large convergence speed, further enabling the best possible performance. The bias field estimation and correction technique is used as preprocessing to enhance the image quality. The U-Net is employed for segmenting affected regions in the ultrasound image. The IGOA-XGBoost performance is estimated with accuracy, recall, precision, f1-score and specificity for the DDTI dataset. It achieves 99.62% accuracy, 99.35% recall, 99.57% precision, 99.15% f1-score and 98.24% specificity, as opposed compared to the existing techniques like Xception and QuCNet. [ABSTRACT FROM AUTHOR]

This article investigates the mechanisms that underlie the biases in price elasticities of demand in applying static demand models under dynamic demand, which have been pointed out by previous empirical studies. It studies three sources of biases: disregard of state variables (affecting short-run elasticity); inconsistent utility parameter estimates; and changing expectations of consumers (affecting long-run elasticity). Disregard of state variables, such as durable product holdings, which is not negligible but not paid much attention to in the literature, leads to an overestimate of short-run own elasticities. Inconsistent utility parameter estimates arises due to the failure to account for consumers' future expectations and unobserved state variables. Changing expectations of consumers are not explicitly specified in the static model, and this also leads to biased results when applying static models. Regarding the magnitude of the biases, the first and the third sources of biases might induce large biases in price elasticities, especially when the focus is on the large conditional choice probability products. Possible remedies for the use of static demand models are also discussed. [ABSTRACT FROM AUTHOR]

Fractional Stochastic Differential Equations are becoming more popular in the literature as they can model phenomena in financial data that typical Stochastic Differential Equations models cannot. In the formulation considered here, the Hurst parameter, H, controls the Fraction of Differentiation, which needs to be estimated from the data. Fortunately, the covariance structure among observations in time is easily expressed in terms of the Hurst parameter which means that a likelihood is easily defined. This work derives the Maximum Likelihood Estimator for H, which shows that it is biased and is not a consistent estimator. Simulation data used to understand the bias of the estimator is used to create an empirical bias correction function and a bias-corrected estimator is proposed and studied. Via simulation, the bias-corrected estimator is shown to be minimally biased and its simulation-based standard error is created, which is then used to create a 95% confidence interval for H. A simulation study shows that the 95% confidence intervals have decent coverage probabilities for large n. This method is then applied to the S&P500 and VIX data before and after the 2008 financial crisis. [ABSTRACT FROM AUTHOR]

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower's distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower's distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, often referred to as Asia's water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study's findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Background: Mediterranean Diet has been reported to possess immunomodulatory and anti-inflammatory properties. These properties are closely associated with the immunopathogenesis of COVID-19. Objective: The present systematic review aimed to determine the association between Mediterranean Diet and COVID-19, COVID-19 symptoms, and COVID-19 severity. Methods: The protocol for this systematic review was registered in International Prospective Register of Systematic Reviews (PROSPERO) with identification number CRD42023451794. The literature search was conducted through Pubmed, Proquest, and Google Scholar on August 2023. The inclusion criteria were studies with a population of human subjects, reported the association between Mediterranean diet adherence with risk of COVID-19 infection, COVID-19 symptoms, or COVID-19 severity, and full text must be available in English. The exclusion criteria were reviews, editorials, letters, replies, systematic reviews, meta-analyses, studies on animals, and duplicates. Risk of bias in included studies was assessed using Newcastle Ottawa Scale (NOS). Data was synthesized narratively. Each study was compared and a structured summary was developed. Results: After selection process, 6 articles were included, with a sample size of 55,489 patients. All studies were observational studies and assessed Mediterranean diet adherence using food frequency questionnaires (FFQ), with scoring system varied between each study. Four studies found a significant correlation between increased adherence to Mediterranean Diet and reduced COVID-19 risk, while one study indicated non-significant association. One study reported a significant association between higher adherence to Mediterranean Diet and COVID-19 symptoms, but three studies reported non-significant association. One study found that individuals with higher adherence to Mediterranean Diet had reduced likelihood of developing severe COVID-19, however, two studies yielded inconclusive findings. Limitations: All studies used self-administrated food frequency questionnaires (FFQs), which were prone to biased responses, such as recall and estimation bias. Discussion: Lower trends of odds ratios (ORs) were consistently observed in higher Mediterranean diet adherence. In every outcome of the included studies, ORs ranged between 0.06–0.992, however, differing levels of significance were reported in each outcome. Conclusion: Overall analyses suggest that high adherence to Mediterranean Diet is a protective factor against COVID-19, with unclear benefits against COVID-19 symptoms and severity. [ABSTRACT FROM AUTHOR]

In laboratory setting evaluating the agreement between two measurement methods is a very frequent practice. Unfortunately, the guidelines to refer to are not free from criticisms from a statistical methodological point of view. We reviewed the Clinical and Laboratory Standards Institute guideline EP09c, 3rd ed. pointing out some drawbacks and some aspects that have not been well defined, leaving situations of uncertainty and/or of excessive subjectivity in the judgement.We have stressed the need of having replicates to estimate the systematic and the proportional biases of the measurement methods to be compared. Indeed, unequal variance of the two measurement methods gives a slope and intercept of the regression between the difference and the mean of the two values of the measurement methods to be compared that can be absolutely calculated from their means, their variances and their correlation coefficient. So, it is not possible to disentangle true from spurious biases. For laboratory professionals we have developed a worked exemplification of an agreement assessment.We have stressed the need of other approaches than the classic Bland and Altman method to calculate the systematic and proportional biases of two measurement methods compared for their agreement in a study with replicates. [ABSTRACT FROM AUTHOR]