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Objectives: Among the provisions within the Affordable Care Act (ACA), expanding Medicaid was arguably the greatest contributor to increasing access to care. For over a decade, researchers have investigated how Medicaid expansion impacted cancer outcomes. Over this same decade, statistical theory illuminated how state‐based policy research could be compromised by invalid inference. After reviewing the literature to identify the inference strategies of state‐based cancer registry Medicaid expansion research, this study aimed to assess how inference decisions could change the interpretation of Medicaid expansion's impact on staging, treatment, and mortality in cancer patients. Data sources: Cancer case data (2000–2019) was obtained from the Surveillance, Epidemiology, End Results (SEER) programme. Cases included all cancer sites combined, top 10 cancer sites combined, and three screening amenable cancers (colorectal, female breast, female cervical). Study design: A Difference‐in‐Differences design estimated the association between Medicaid expansion and four binary outcomes: distant stage, initiating treatment >1 month after diagnosis, no surgery recommendation, and death. Three inference techniques were compared: (1) traditional, (2) cluster, and (3) Wild Cluster Bootstrap. Data collection: Data was accessed via SEER*Stat. Principal findings: Estimating standard errors via traditional inference would suggest that Medicaid expansion was associated with delayed treatment initiation and surgery recommendations. Traditional and clustered inference also suggested that Medicaid expansion reduced mortality. Inference using Wild Cluster Bootstrap techniques never rejected the null hypotheses. Conclusions: This study reiterates the importance of explicit inference. Future state‐based, cancer policy research can be improved by incorporating emerging techniques. These findings warrant caution when interpreting prior SEER research reporting significant effects of Medicaid expansion on cancer outcomes, especially studies that did not explicitly define their inference strategy. [ABSTRACT FROM AUTHOR]

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors (SEs), and the corresponding z-statistics are affected by the scales of the manifest and latent variables. Analytical and empirical results show that (1) the normal-distribution-based likelihood ratio statistic is scale-invariant with respect to scale changes of manifest and latent variables as well as to anchor change of latent variables; (2) the normal-distribution-based maximum likelihood (NML) parameter estimates are scale-equivariant with respect to scale-change of manifest and latent variables as well as to anchor change of latent variables; (3) standard errors (SEs) following the NML method are parallel-scale-equivariant with respect to scale changes of the manifest and latent variables; and (4) the z-statistics are scale-invariant with respect to scale changes of the manifest and latent variables. However, only (1) and (2) hold if latent variables are rescaled by changing anchors. Nevertheless, parameters that are not directly related to latent variables with changing anchors are still scale-equivariant and their z-statistics are still scale-invariant. The results are expected to advance understanding of SEM analysis, and also facilitate result interpretation and comparison across studies as in meta analysis. [ABSTRACT FROM AUTHOR]

The social defeat hypothesis posits that low status and repeated humiliation increase the risk for psychotic disorders (PDs) and psychotic experiences (PEs). The purpose of this paper was to provide a systematic review of studies on risk of PDs and PEs among lesbian, gay, or bisexual (LGB) people and a quantitative synthesis of any difference in risk. PubMed, PsycINFO, Embase, and Web of Science were searched from database inception until January 30, 2024. Two independent reviewers assessed the eligibility and quality of studies, extracted effect sizes, and noted the results of mediation analyses. Using a random effects model we computed pooled odds ratios (ORs). Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The search identified seven studies of PDs and six of PEs. As for PDs, the unadjusted (2.13; 95% confidence interval 0.72–6.34) and covariate-adjusted pooled OR (2.24; 1.72–3.53) were not significantly increased for LGB individuals. After exclusion of a study of limited quality, both the unadjusted pooled OR (2.77; 1.21–6.32) and the covariate-adjusted pooled OR (2.67; 1.53–4.66) were significantly increased. The pooled ORs were increased for PEs: unadjusted, pooled OR = 1.97 (1.47–2.63), covariate-adjusted, pooled OR = 1.85 (1.50–2.28). Studies of PE that examined the mediating role of several variables reported that the contribution of drug abuse was small compared to that of psychosocial stressors. The results of a study in adolescents suggested a protective effect of parental support. These findings suggest an increased psychosis risk for LGB people and support the social defeat hypothesis. [ABSTRACT FROM AUTHOR]

Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé; Hodges and Lehmann; Freedman, Pisani, and Purves; and Dunning). These examples illustrate that Neyman’s key insights can be explained in intuitive and interesting ways to audiences at all levels, including undergraduates in introductory statistics courses. We have found Freedman, Pisani, and Purves’s box-of-tickets model to be a valuable expository tool, and we also find their intuitive explanation of Neyman’s variance result helpful: It is a “minor miracle” that in randomized experiments, the two-sample zz-test is conservative because of “two mistakes that cancel.” All four books take a more positive view of Neyman’s results than Neyman himself did. We encourage educators and researchers to explore ways to communicate Neyman’s ideas that are helpful for their own audiences.

Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to be fully addressed. To tackle this issue, the present study proposed an updated supplemented expectation maximization (USEM) method and a bootstrap method for SE estimation. These two methods were compared in terms of SE recovery accuracy. The simulation results demonstrated that the GVEM algorithm with bootstrap and item priors (GVEM-BSP) outperformed the other methods, exhibiting less bias and relative bias for SE estimates under most conditions. Although the GVEM with USEM (GVEM-USEM) was the most computationally efficient method, it yielded an upward bias for SE estimates. [ABSTRACT FROM AUTHOR]

The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, and Haebara linking are investigated in nonrobust and robust specifications in the presence of differential item functioning (DIF). M-estimation theory is applied to derive linking errors for the studied linking methods. However, estimated linking errors are prone to sampling error in estimated item parameters, thus resulting in artificially increased the linking error estimates in finite samples. For this reason, a bias-corrected linking error estimate is proposed. The usefulness of the modified linking error estimate is demonstrated in a simulation study. It is shown that a simultaneous assessment of the standard error and linking error in a total error must be conducted to obtain valid statistical inference. In the computation of the total error, using the bias-corrected linking error estimate instead of the usually employed linking error provides more accurate coverage rates. [ABSTRACT FROM AUTHOR]

The two-parameter logistic (2PL) item response theory model is a statistical model for analyzing multivariate binary data. In this article, two groups are brought onto a common metric using the 2PL model using linking methods. The linking methods of mean–mean linking, mean–geometric–mean linking, and Haebara linking are investigated in nonrobust and robust specifications in the presence of differential item functioning (DIF). M-estimation theory is applied to derive linking errors for the studied linking methods. However, estimated linking errors are prone to sampling error in estimated item parameters, thus resulting in artificially increased the linking error estimates in finite samples. For this reason, a bias-corrected linking error estimate is proposed. The usefulness of the modified linking error estimate is demonstrated in a simulation study. It is shown that a simultaneous assessment of the standard error and linking error in a total error must be conducted to obtain valid statistical inference. In the computation of the total error, using the bias-corrected linking error estimate instead of the usually employed linking error provides more accurate coverage rates.

RFC1 disease, caused by biallelic repeat expansion in RFC1 , is clinically heterogeneous in terms of age of onset, disease progression and phenotype. We investigated the role of the repeat size in influencing clinical variables in RFC1 disease. We also assessed the presence and role of meiotic and somatic instability of the repeat. In this study, we identified 553 patients carrying biallelic RFC1 expansions and measured the repeat expansion size in 392 cases. Pearson's coefficient was calculated to assess the correlation between the repeat size and age at disease onset. A Cox model with robust cluster standard errors was adopted to describe the effect of repeat size on age at disease onset, on age at onset of each individual symptoms, and on disease progression. A quasi-Poisson regression model was used to analyse the relationship between phenotype and repeat size. We performed multivariate linear regression to assess the association of the repeat size with the degree of cerebellar atrophy. Meiotic stability was assessed by Southern blotting on first-degree relatives of 27 probands. Finally, somatic instability was investigated by optical genome mapping on cerebellar and frontal cortex and unaffected peripheral tissue from four post-mortem cases. A larger repeat size of both smaller and larger allele was associated with an earlier age at neurological onset [smaller allele hazard ratio (HR) = 2.06, P < 0.001; larger allele HR = 1.53, P < 0.001] and with a higher hazard of developing disabling symptoms, such as dysarthria or dysphagia (smaller allele HR = 3.40, P < 0.001; larger allele HR = 1.71, P = 0.002) or loss of independent walking (smaller allele HR = 2.78, P < 0.001; larger allele HR = 1.60; P < 0.001) earlier in disease course. Patients with more complex phenotypes carried larger expansions [smaller allele: complex neuropathy rate ratio (RR) = 1.30, P = 0.003; cerebellar ataxia, neuropathy and vestibular areflexia syndrome (CANVAS) RR = 1.34, P < 0.001; larger allele: complex neuropathy RR = 1.33, P = 0.008; CANVAS RR = 1.31, P = 0.009]. Furthermore, larger repeat expansions in the smaller allele were associated with more pronounced cerebellar vermis atrophy (lobules I–V β = −1.06, P < 0.001; lobules VI–VII β = −0.34, P = 0.005). The repeat did not show significant instability during vertical transmission and across different tissues and brain regions. RFC1 repeat size, particularly of the smaller allele, is one of the determinants of variability in RFC1 disease and represents a key prognostic factor to predict disease onset, phenotype and severity. Assessing the repeat size is warranted as part of the diagnostic test for RFC1 expansion. [ABSTRACT FROM AUTHOR]

Prenatal drug exposure is a public health problem, which results in profound behavioral problems during childhood and adolescence, mainly represented by an increase in the risk of cocaine abuse at an early age. In rodents, prenatal and postnatal cocaine exposure enhanced locomotor activity and cocaine‐ or nicotine‐induced locomotor sensitization. Various authors consider that the adverse emotional states (anxiety and depression) that occur during cocaine withdrawal are the main factors that precipitate, relapse, and increase chronic cocaine abuse, which could increase the risk of relapse of cocaine abuse. Therefore, the objective of this study was to characterize anxiety‐ and depression‐like behaviors at different times (30, 60, 90, and 120 days) of cocaine withdrawal in rats born to females exposed prenatally and postnatally to cocaine. A group of pregnant female Wistar rats were administered daily from day GD0 to GD21 with cocaine (cocaine preexposure group), and another group of pregnant female rats was administered daily with saline (saline preexposure group). Of the litters resulting from the cocaine‐pre‐exposed and saline‐pre‐exposed pregnant female groups, only the male rats were used for the recording of the anxiety‐ and depression‐like behaviors at different times (30, 60, 90, and 120 days) of cocaine withdrawal The study found that prenatal and postnatal cocaine exposure dose‐dependent enhanced anxiety‐ and depression‐like behaviors. This suggests that prenatal and postnatal cocaine exposure can result in enhanced vulnerability to cocaine abuse in young and adult humans. [ABSTRACT FROM AUTHOR]

This study aimed at developing a model for re-aeration (K2) that may be utilized in conjunction with de-oxygenation (K1) to calculate the self-purification capacity, f, of Nkisa River. An iterative least squares fitting routine was employed by XLSTAT to produce the optimal goodness of fit between Nkisa data and function. Thus, Nkisa re-aeration models; K22DRY (dry season model), K22RAINY (rainy season model) and K22all-seasons (combined model) were developed and validated statistically resulting in the output with the optimal statistical performance (SSE = 0.0005; R2 = 0.948; Adjusted R2 = 0.937 and RMSE = 0.009), (SSE =0.0483, R2 = 0.81, Adjusted R2 = 0.77, and RMSE = 0.08) and (SSE = 0.1004; R2 = 0.93; Adjusted R2 = 0.92 and RMSE = 0.08) for dry, rainy and all-seasons respectively. Its performance was verified by comparing the model with nine previously existing models and Nkisa re-aeration models gave the least SSE values of 0.0005, 0.0483 and 0.1004; which was the best interpretation of the conditions of Nkisa River in dry, rainy and all-seasons. The self-purification capacity of the studied river was found to be greater than unity during all studied period except in March which implies that, re-aeration is greater than de-oxygenation. HIGHLIGHTS Raw water samples were collected and field measurement of hydraulic and physical parameters of the Nkisa River was done.; Laboratory analyses of the collected raw water samples were done.; Modeling and validating the re-aeration coefficient (K2) for the Nkisa River were carried out.; The models' suitability was verified by comparing them with nine other existing models.; The performance of different K2 models was compared with the following statistics: SSE, R2, adjusted R2, and RMSE.;

Sliding mode control is a promising approach for designing controllers for systems with empirical characteristics. This is a favored nonlinear control strategy that effectively addresses the uncertainties present in derived mathematical models. To further enhance the stability of such systems, an Adaptive Neuro Fuzzy Inference System is employed by adapting to dynamic changes and inconsistent correlations between excitation and response. In this study, Sliding Mode Control was deployed in the feedback loop, effectively serving as a state feedback controller based on a nonlinear control law. As a two-parameter control approach, Sliding Mode Control requires careful tuning to achieve optimal performance. The integration of the Adaptive Neuro-Fuzzy System aims to bestow the two parameters of Sliding Mode Control with the ability to rapidly reduce errors to zero, thereby enhancing overall control efficiency. The research focuses on utilizing an Adaptive Neuro Fuzzy Inference System to implement Sliding Mode Control for a DC servo system while emphasizing state feedback control. The Harmony Search Optimization method is employed to optimize controller parameters effectively. The results of the research demonstrate the achievement of a best-fit value, where the minimal standard error and Best fitness are considered. This highlights the successful integration of the proposed control strategy and validates its effectiveness in providing accurate and reliable control of the real-time DC servo system.

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstrap and simulation techniques from LMM paradigm can be used to construct the confidence intervals. The purpose of this research is to examine four different LMM-based methods for computing the confidence intervals that have been proposed and to determine their accuracy under six simulated conditions based on the type of test scores (normal, dichotomous, and polytomous data) and data measurement design (p×i×r and p× [i:r]). A bootstrap technique called parametric methods with spherical random effects consistently produced more accurate confidence intervals than the three other LMM-based methods. Furthermore, the selected technique was compared with model-based approach to investigate the performance at the levels of variance components via the second simulation study, where the numbers of examines, raters, and items were varied. We conclude with the recommendation generalizability coefficients, the confidence interval should accompany the point estimate.

Out-of-sample prediction is the acid test of predictive models, yet an independent test dataset is often not available for assessment of the prediction error. For this reason, out-of-sample performance is commonly estimated using data splitting algorithms such as cross-validation or the bootstrap. For quantitative outcomes, the ratio of variance explained to total variance can be summarized by the coefficient of determination or in-sample R2, which is easy to interpret and to compare across different outcome variables. As opposed to in-sample R2, out-of-sample R2 has not been well defined and the variability on out-of-sample R ̂ 2 has been largely ignored. Usually only its point estimate is reported, hampering formal comparison of predictability of different outcome variables. Here we explicitly define out-of-sample R2 as a comparison of two predictive models, provide an unbiased estimator and exploit recent theoretical advances on uncertainty of data splitting estimates to provide a standard error for R ̂ 2 . The performance of the estimators for R2 and its standard error are investigated in a simulation study. We demonstrate our new method by constructing confidence intervals and comparing models for prediction of quantitative Brassica napus and Zea mays phenotypes based on gene expression data. Our method is available in the R-package oosse. [ABSTRACT FROM AUTHOR]

Background The lumbar artery perforator (LAP) flap is a valuable secondary option for autologous breast reconstruction when abdominal donor sites are not available. Objectives The aim of this study was to determine how the LAP donor site affects waistline and gluteal proportions, and whether LAP flaps may produce potential secondary benefits concerning donor site aesthetics. Methods A retrospective review of 50 patients who underwent bilateral breast reconstruction with LAP flaps (100 flaps) between 2018 and 2022 was performed. Patient characteristics and postoperative complications were recorded. Pre- and postoperative standardized frontal and lateral patient photographs were analyzed to identify postoperative changes in waist-to-hip ratio (WHR) to assess waistline definition and buttock projection. The patients' individual perception of postoperative aesthetic changes to their waistline and buttocks was determined by an electronic survey. Results The patients had a mean age of 51 years (range, 24-63 years) and a mean BMI of 26.9 kg/m2 (range, 19.3-37.4 kg/m2). Postoperative donor site complications included seroma (n = 10), wound dehiscence (n = 5), hematoma (n = 4), and wound infection (n = 2). Flap loss rate was 2%. After reconstruction, patients were found to have a more defined waistline, indicated by a significantly decreased WHR on frontal images (mean [standard error of the mean], 0.85 [0.05] vs 0.80 [0.05], P <.005) and a more projected buttocks indicated by a significant reduction in WHR on lateral images (0.92 [0.07] vs 0.87 [0.07], P <.0001). Among the patients who responded to the survey, 73% indicated that their waistline had aesthetically improved, 6.7% felt it had not changed, and 20% felt that it had worsened. An aesthetic improvement of the buttocks was reported by 53%, 40% felt their buttocks had not changed, and 6.7% felt their buttocks appearance had worsened. Conclusions Bilateral LAP flap breast reconstruction leads to improved waistline definition and buttock projection, bringing patients closer to ideal aesthetic proportions. This reconstructive approach is ideal for patients who are not candidates for abdominal free flaps, but who demonstrate excess flank tissue and seek a more defined waistline and projected buttocks. Level of Evidence: 4 [ABSTRACT FROM AUTHOR]

In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster.

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Estimating structural equation models with categorical data often proceeds in two stages. Stage 1 obtains polychoric correlations among categorical variables. Stage 2 fits the researcher's model to the polychoric correlations, often with diagonally-weighted least squares (DWLS) or unweighted least squares (ULS). For DWLS/ULS model parameter estimates, the current literature contains two standard error (SE) methods: the classic SE and robust SE. Although the robust SE is generally believed to be satisfactory, in this paper we show it is reliable only given perfect models. When a model is misspecified, the robust SE can sometimes be egregiously biased, and the bias is not directly related to the model's overall misfit. We also propose a new SE method for DWLS and ULS. The new SE is consistent regardless of the model fit, and substantially outperforms the robust SE in our simulation studies. The classic SE is the worst among the three SE methods. [ABSTRACT FROM AUTHOR]

In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann's jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann's jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster. [ABSTRACT FROM AUTHOR]

Walnut (Juglans regia L.) is a particularly significant plant for humans in terms of its useful properties and in the Russian Federation it can be attributed to the most valuable introducers for forestry and horticulture. It is grown in many countries of the world and the area of its cultivation is constantly expanding. Breeding species for food purposes requires a selection assessment of the quality of its fruits. For this, at different times, appropriate methods were developed and the existing gene pool of the culture was assessed. By now, new varieties and forms have been bred and brought, which also need to be evaluated for rational use for scientific and practical purposes. The descriptions of varieties and forms of walnut often contain insufficient information about all selected traits, which requires the development of new models to assess the variety fund using incomplete data. The purpose of the research is to develop the missing models of selection evaluation using incomplete data and evaluate the world walnut variety fund in terms of fruit quality, to identify varieties and forms of the highest quality category. The well-known methodology and new models have been used for the evaluation. When developing evaluation models for incomplete data, a database of 50 varieties evaluated for all indicators has been used. Using the method of excluding individual indicators, the corresponding multiple regression models have been calculated on its basis, taking into account a different combination of features. The licensed programs Stadia 8.0, Microsoft Excel have been used in the research. The proposed models provide an estimate with an average error of ± 0.48 – 3.72 points at R2 = 0.63 – 0.99. A promising walnut gene pool from 23 countries has been assessed, 69 representatives with fruits of the breeding category of the highest quality identified, and a list of them compiled. Of the 512 ancestors, 19.73% had the selection category of fruits of the highest quality, 62.88% of high-quality fruits, 16.80% of ordinary fruits, and 0.59% of varieties and forms of low-quality fruits.

This research was conducted at PT. Starindo Sakti Award for 31 days. This study aims to determine whether or not there is a significant relationship between overburden stripping production and loss time. Because stripping of overburden is an activity that affects mining activities, the researcher is interested in analyzing every mining activity, especially in stripping the overburden layer at the mining front at PT. Starindo Sakti Award using a Kobelco SK-200 excavator. To determine whether or not the relationship between overburden stripping production and loss time is significant, the F test can be carried out. find out the deviation of the data. After conducting the F test, it turns out that the relationship between overburden stripping production and loss time is significant (very influential), as evidenced by the results Fhitung of 686 and Ftabel of 3.34 with a standard error of 5,8.

When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot of data, enjoying high dimension, strong correlation and redundancy, has been generated in real life. So it is necessary to find an effective variable selection method for dealing with collinearity based on the robust method. This paper proposes a penalized M-estimation method based on standard error adjusted adaptive elastic-net, which uses M-estimators and the corresponding standard errors as weights. The consistency and asymptotic normality of this method are proved theoretically. For the regularization in high-dimensional space, the authors use the multi-step adaptive elastic-net to reduce the dimension to a relatively large scale which is less than the sample size, and then use the proposed method to select variables and estimate parameters. Finally, the authors carry out simulation studies and two real data analysis to examine the finite sample performance of the proposed method. The results show that the proposed method has some advantages over other commonly used methods. [ABSTRACT FROM AUTHOR]

We derive the maximum likelihood estimate (MLE) of a population proportion when it differs from the same of a second population by a known value. This constrained MLE (CMLE) has a closed form in limited scenarios, which are completely characterized. These include the cases when the CMLE takes a boundary value in the parameter space. The existence of solution is established in the other cases and numerical methods are adopted in R and Excel to obtain the estimates solving a nonlinear equation. The standard error of the CMLE is estimated via bootstrap which also yields a confidence interval estimate; this is compared with a second method based on asymptotic distribution. The CMLE is of particular importance in the two sample testing of hypothesis of proportions based on independent samples, when these parameters differ by a non-zero value under the null hypothesis. Numerical computation establishes that the test statistic using the standard error based on this CMLE leads to a more reliable decision than the existing alternatives when the sample sizes are moderate to large. [ABSTRACT FROM AUTHOR]

The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta‐analysis since these can be readily computed based on the reported results of a linear regression analysis. The default inverse variance weights in standard meta‐analysis models require researchers to compute not only the partial correlation coefficients of each study but also its corresponding sampling variance. The existing literature is diffuse on how to estimate this sampling variance, because two estimators exist that are both widely used. We critically reflect on both estimators, study their statistical properties, and provide recommendations for applied researchers. We also compute the sampling variances of studies using both estimators in a meta‐analysis on the partial correlation between self‐confidence and sports performance. [ABSTRACT FROM AUTHOR]

In this paper, the use of bootstrap method with Monte Carlo integration is introduced for one dimension. This approach is based on generating observations from a known distribution for the bootstrap samples, then apply the Monte Carlo method on each bootstrap sample to estimate the integral of interest. The empirical distribution, or the bootstrap distribution, of the estimation results can be used as a good proxy for the distribution of the integral of interest. Based on the bootstrap distribution, the standard error of the estimate of the integral of interest can be derived. Also, the percentile and Normal confidence intervals with confidence level (1-α)% can be derived as well. The bootstrap method with Monte Carlo integration is easy to implement and straightforward to provide well results. Moreover, it provides small variance for the estimate of the integral of interest. Four examples with different functions and different domains are used to present the performance of the proposed method. From the study, we find that the method provides nearly identical results for the standard errors, regardless of the distributions used for generating observations for the bootstrap samples.

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19. [ABSTRACT FROM AUTHOR]

Statistical analysis of data is an integral part of research projects in all scientific disciplines including the plant pathology. Appropriate design, application and interpretation of statistical analysis are also, therefore, at the center of publishing and properly evaluating studies in plant pathology. A survey of research works published in the Plant Pathology Journal, however, cast doubt on high standard of statistical analysis required for scientific rigor and reproducibility in the journal. Here I first describe, based on the survey of published works, what mistakes are commonly made and what components are often lacking during statistical analysis and interpretation of its results. Next, I provide possible remedies and suggestions to help guide researchers in preparing manuscript and reviewers in evaluating manuscripts submitted to the Plant Pathology Journal. This is not aiming at delineating technical and practical details of particular statistical methods or approaches.

This study aimed to uncover the effect of the missing values using the Rasch model on an experimental test of mathematics for the second-secondary students. To achieve the goal of the study, a multiple-choice test was designed testing all basic skills found on the student’s book. The test consisted of 103 items. This study was conducted in September 2019. The results were analyzed using SPSS, Winstep, and MG3-BILOG. To determine the correlation coefficients, the results showed that the test had good psychometric properties and the test had high reliability, validity indications, acceptable difficulty, and discrimination coefficients. The findings also showed that the differentiation coefficients of items were equal. The results also showed that the premises of the Rasch model were fulfilled. Further, the results showed that there were statistically significant differences between the three models according to the percentage of data loss, standard error, difficulty, internal and external matching, and bias, and were in favor of the original model before missing the values.

Global access to the internet has surprisingly changed how we see the arena. SM is one of the children of the internet, which can be found in many bureaucracies: online gaming platforms, dating apps, boards, online information services, and social networks with extraordinary functions. On Twitter and Facebook, you can share opinions make business contacts on LinkedIn, share photos on Instagram, send videos on YouTube, and court someone on Meetic. However, they all have one thing in common: they aim to attach people. Social networks have such great potential that by the year 2021, it is projected that there will be 3.02 billion active social media users globally. The rapid rise of social networks and microblogging sites has caused direct communication between people of diverse cultures and mentalities, leading to an increasing number of "cyber" conflicts. As a consequence, hate speech is used more and more, to the point where it has become a serious problem invading these public spaces. Hate speech is defined as the use of competitive, violent, or abusive words directed towards a specific group of people who share a common set of assets, whether that set of assets includes their gender, ethnic group, race, or beliefs and faith. While most internet social networks and microblogging services prohibit hate speech, the sheer size of these networks and websites makes controlling all of their content extremely difficult. As a result, there is a need to detect such speech automatically and remove any content that contains hateful or inciting language. [ABSTRACT FROM AUTHOR]

This paper presents a concise approach for calibrating the damage models in the TxME, a mechanistic–empirical flexible pavement design model developed for Texas. The models calibrated are rutting, bottom-up fatigue cracking and transverse (thermal) cracking. Pavement performance observations from the Texas Department of Transportation (TxDOT) maintained Data Storage System (DSS) were used for this purpose. Prior to calibration, Analysis of Variance (ANOVA) was used to establish similar groups of sections in terms of structural, traffic and environmental conditions. For the rutting model, the layer calibration coefficients weighed plastic deformations linearly, hence it was possible to establish them by minimising the sum of the squared errors (SSE) between the estimated and observed surface (i.e. total) rutting using the generalised gradient method (GRG) through the MS® Excel solver. For the fatigue cracking model, calibration focused on the transfer function between the accumulated fatigue damage and the observed fatigue cracked area. Curve fitting was done using the bi-square robust algorithms in MATLAB®. For the transverse cracking model, the exponent of the pavement life ratio ρ / m was established through curve-fitting on transverse cracking observations. In general, the calibration resulted in significant quality of fit improvements. [ABSTRACT FROM AUTHOR]

Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is broadly applicable to various joint modeling settings, such as those containing generalized linear mixed-effect models, parametric survival models, or joint models with more than two submodels. We evaluate the performance of the proposed methods through simulation studies and apply them to HIV-1 viral load data from six AIDS Clinical Trials Group studies to characterize viral rebound trajectories following the interruption of antiretroviral therapy (ART), accounting for the informative duration of off-ART periods., (© The Author 2024. Published by Oxford University Press. All rights reserved. For Permissions, email: journals.permissions@oup.com.)

Background: While the use of standard errors and confidence intervals is common in regression-based studies in the population sciences, it is far less common in studies using formal demographic measures and methods, including demographic decompositions. Objective: This article describes and provides explicit instructions for using four different approaches for computing standard errors for complex demographic estimators. Methods: Standard errors for Arriaga's decomposition of life expectancy differences are computed using the delta method, the Poisson bootstrap, the binomial bootstrap, and the Monte Carlo approaches. The methods are demonstrated using a 50Š sample of vital statistics data on age-specific mortality among urban women in the Pacific region of the United States in 1990 and 2019. Results: All four methods for computing standard errors returned similar estimates, with the delta method, Poisson bootstrap, and Monte Carlo approaches being the most consistent. The Monte Carlo approach is recommended for general use, while the delta method is recommended for specific cases. Contribution: This study documents multiple ways of estimating statistical uncertainty for complex demographic estimators and describes in detail how to apply these various methods to nearly any rate-based demographic measure. It also provides advice on when the use of standard errors is and is not appropriate in demographic studies. Explicit formulae for computing standard errors for Arriaga's decomposition using the delta method approach are derived.

Today, betavoltaic batteries has been considered due to their high energy density and long life for operating electrical systems in inaccessible and hostile environments. Conventional electrochemical batteries, despite their widespread use in electronic devices, have a limited lifetime and tend to degrade in extreme environmental conditions. The current paper pursues three goals. The first goal is to the experimental and theoretical investigation of a n-Si/ZnO heterojunction betavoltaic battery based on 90Sr/90Y source. The second goal is to optimize ZnO, SnO 2 , BN, and diamond homojunction betavoltaic cells in two planar and cubical models by Monte Carlo simulation (MCNP code). The third goal is to present a new approach for estimating the standard error in calculating the parameters of betavoltaic batteries. In order to fabrication of a n-Si/ZnO heterojunction, the ZnO nanospheres were placed on the n-Si (100) substrate using chemical bath deposition (CBD) technology. The Al and Au electrodes were deposited on the formed sample. Then this sample was exposed to the radiation of an external 90Sr/90Y source with an activity of 12.8 mCi. The experimental values obtained for the short circuit current (I sc), open circuit voltage (V oc), efficiency (η), and maximum output power (P max) were 0.047 μA, 0.015 V, 3.4 × 10−4 percent, and 0.141 nW, respectively. To compare with the experiment, we investigated the n-Si/ZnO betavoltaic cell by MCNP code. In the simulation, the beta spectrum of the 90Sr/90Y source was considered. The calculated theoretical values for I sc , V oc , η, and P max were 0.063 μA, 0.020 V, 6.4 × 10−4 percent, and 0.265 nW, respectively. The experimental results show that the simulation results can be valid. In the optimization of ZnO, SnO 2 , BN, and diamond homojunction betavoltaic cells in two planar and cubical models by MCNP code, it was found that the I sc , V oc , η, and P max of the cubical model are better compared to the planar model. In the cubical model, P max of ZnO, SnO 2 , BN, and diamond betavoltaic batteries is 2633.34 nW ± 0.16 %, 1670.49 nW ± 0.15 %, 198.20 nW ± 0.17 %, and 1315.24 nW ± 0.15 %, respectively. In other words, P max of the ZnO betavoltaic battery is about 58 %, 1229 %, and 100 % more than P max of SnO 2 , BN, and diamond betavoltaic batteries, respectively. P max of the SnO 2 betavoltaic battery is about 743 % and 27 % more than P max of BN and diamond betavoltaic batteries, respectively. The results show that ZnO and SnO 2 betavoltaic batteries can perform better compared to BN and diamond betavoltaic batteries. Also, their growth process is less expensive compared to BN and diamond. [ABSTRACT FROM AUTHOR]

IntroductionThe Glasgow Coma Scale (GCS) score is the primary method of assessing consciousness after traumatic brain injury (TBI), and the clinical standard for classifying TBI severity. There is scant literature discerning the influence of circadian rhythms or emergency department (ED) arrival hour on this important clinical tool.MethodsRetrospective cohort analysis of adult patients suffering blunt TBI using the National Sample Program of the National Trauma Data Bank, years 2003-2006. ED arrival GCS score was characterized by midday (10 a.m.-4 p.m.) and midnight (12 a.m.-6 a.m.) cohorts (N=24548). Proportions and standard errors are reported for descriptive data. Multivariable regressions using odds ratios (OR), mean differences (B), and their associated 95% confidence intervals [CI] were performed to assess associations between ED arrival hour and GCS score. Statistical significance was assessed at p

In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage. [Display omitted] • BEXP and gamma models have been considered to investigate the trifluralin dissipation. • A simulation study illustrated the importance of the standard error of half-life. • Provided mathematical computation of the standard error of the half-life. • The soil moisture levels influenced the half-life estimates of trifluralin. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine MOHV21, a Moho depth model based on an optimal combination of five global seismic and gravimetric-isostatic models of Moho depth by a weighted least squares approach at a resolution of 1° × 1°. For proper weighting among the data, the study starts with determining (mostly missing) standard errors and correlations among the models. The standard errors among the input models range from 1.0 (in Brazil) to 6.8 km (in Peru) and from 0.1 (in Huna Bay) to 6.0 km (in East Pacific Ridge) for Moho depth on land and ocean, respectively. The correlations among the five models range between − 0.99 and + 0.90. The Moho depths for MOHV21 at land regions vary between 14.5 (at the Horn of Africa) and 75 km (in the Himalayas) and between 6.6 (in the Greenland Sea) and 51.8 (in the Gulf of Bothnia) for land and ocean regions, respectively (However, note that, the Gulf of Bothnia belongs to continental crust, while the oceanic crust is generally within 20 km). The standard errors are generally within a few km but reaches 6.8 km (9%) in the highest mountains. The shallow Moho depths along mid-ocean ridges are well exposed in the model. Notable regional Moho highs are visualized in the Tarim basin in NW China of 59 ± 6.5 km and in Central Finland of 57 ± 4.7 km. A comparison of MOHV21 with a mosaic of regional models shows large differences reaching ± 25 km in Africa, Antarctic, and parts of S. America, while the differences are relatively modest in those parts of oceans that are available in the regional models. [ABSTRACT FROM AUTHOR]