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Generalized distributions have become widely used in applications recently. They are very flexible in data analysis, especially with skewed models that are important and occur frequently in many applications. In particular, the Generalized Logistic Distribution with its several types has lately gained a lot of attention in the literature. In this study, based on progressively Type II censored data, we obtained estimators of the unknown parameters of Type II Generalized Logistic Distribution. Several point estimation methods are used. Specifically, we consider the maximum likelihood estimation (MLE), the Bayesian estimator based on importance sampling and Lindley's approximation, the linear estimators (BLUE and BLEE). The estimators were investigated and compared using simulation techniques in a variety of scenarios and progressive censoring schemes. The criteria used for comparison are the mean squared error (MSE) and bias. The derived estimators are applied to real-world data in order to see how they operate in real situations.

Publisher Copyright: CCBY Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which, point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm, but is also automatic in that it does not require any additional input or manual pre-processing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data.

The results of a meta-analysis are more than just the reported odds ratio, 95% confidence interval (CI), and p value. Of equal importance is the fine print of the study which should include assessment of the risk of bias, certainty in evidence, and heterogeneity in the individual point estimates and CIs. These areas all have an influence on the quality of the data in the analysis. Reading and understanding the fine print is important.

In this article, we propose procedures based on the modified information criterion and the confidence distribution for detecting and estimating changes in a three-parameter Weibull distribution. Corresponding asymptotic results of the test statistic associated the detection procedure are established. Moreover, instead of only providing point estimates of change locations, the proposed estimation procedure provides the confidence sets for change locations at a given significance level through the confidence distribution. In general, the proposed procedures are valid for a large class of parametric distributions under Wald conditions and the certain regularity conditions being satisfied. Simulations are conducted to investigate the performance of the proposed method in terms of powers, coverage probabilities and average lengths of confidence sets with respect to a three-parameter Weibull distribution. Corresponding comparisons are also made with other existing methods to indicate the advantages of the proposed method. Rainfall data is used to illustrate the application of the proposed method.

The change point estimation concept is usually useful in time series models. This concept helps to decrease the decision making or production costs by monitoring the stock market and production lin...

The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1,000 live births. As part of a larger effort to reduce preventable neonatal deaths in the country, we conducted a study to examine risk factors for low birthweight. The data was collected via a cost-efficient cluster-based outcome-dependent sampling scheme wherein clusters of individuals (health centers) were selected on the basis of, in part, the outcome rate of the individuals. For a given dataset collected via a cluster-based outcome-dependent sampling scheme, estimation for a marginal model may proceed via inverse-probability-weighted generalized estimating equations, where the cluster-specific weights are the inverse probability of the health center's inclusion in the sample. In this paper, we provide a detailed treatment of the asymptotic properties of this estimator, together with an explicit expression for the asymptotic variance and a corresponding estimator. Furthermore, motivated by the study we conducted in Rwanda, we propose a number of small-sample bias corrections to both the point estimates and the standard error estimates. Through simulation, we show that applying these corrections when the number of clusters is small generally reduces the bias in the point estimates, and results in closer to nominal coverage. The proposed methods are applied to data from 18 health centers and 1 district hospital in Rwanda.

Meta-analysis aggregates quantitative outcomes from multiple scientific studies to produce comparable effect sizes. The resultant integration of useful information leads to a statistical estimate with higher power and more reliable point estimate when compared to the measure derived from any individual study. Effect sizes are usually estimated using mean differences of the outcomes of treatment and control groups in experimental studies. Although different software exists for the calculations in meta-analysis, understanding how the calculations are done can be useful to many researchers, particularly where the values reported in the literature data is not applicable in the software available to the researcher. In this paper, search was conducted online primarily using Google and PubMed to retrieve relevant articles on the different methods of calculating the effect sizes and the associated confidence intervals, effect size correlation, p values and I2, and how to evaluate heterogeneity and publication bias are presented.

Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this paper, we present \texttt{WattScale}, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, \texttt{WattScale} uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. \texttt{WattScale} also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. \texttt{WattScale} has two execution modes -- (i) individual, and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41\%, 23.73\%, and 0.51\% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that \texttt{WattScale} can be extended to millions of homes in the US due to the recent availability of representative energy datasets., This paper appeared in the Journal ACM Transactions on Data Science

Based on the approach of left and right kernel smoothing with unilateral kernel function, we, in this paper, define estimators of change point and jump size in nonparametric regression model with r...

Groundwater resources are the only source of water in many arid and semi-arid regions. It is important to manage these resources to have a sustainable development. However, there are many factors influencing the accuracy of the results in groundwater modeling. In this research, the uncertainty of two important groundwater model parameters (hydraulic conductivity and specific yield) were considered as the main sources of uncertainty in estimating water level in an unconfined aquifer, in Iran. For this purpose, a simple method called Rosenblueth Point Estimate Method (RPEM) was used to assess groundwater modeling parametric uncertainty, and its performance was compared with Monte Carlo method as a very complicated and time-consuming method. According to calibrated values of hydraulic conductivity and specific yield, several uncertainty intervals were considered to analyze uncertainty. The results showed that the optimum interval for hydraulic conductivity was 40% increase–30% decrease of the calibrated values in both Monte Carlo and RPEM methods. This interval for specific yield was 200% increase–90% decrease of the calibrated values. RPEM showed better performance using the evaluating indices in comparison with Monte Carlo method for both hydraulic conductivity and specific yield with 43% and 17% higher index values, respectively. These results can be used in groundwater management and future prediction of groundwater level.

A two-sample pivot for comparing the means from independent populations is well known. For large sample sizes, the distribution of the pivot is routinely approximated by a standard normal distribution. The question is about the thinking process that may guide one to rationalize invoking the asymptotic theory. In this pedagogical piece, we put forward soft statistical arguments to make users feel more at ease by suitably indexing the sample sizes from a practical standpoint that would allow a valid interpretation and understanding of pointwise convergence of the pivot's cumulative distribution function (c.d.f.) to the c.d.f. of a standard normal random variable.

Most organizational researchers understand the detrimental effects of measurement errors in testing relationships among latent variables and hence adopt structural equation modeling (SEM) to control for measurement errors. Nonetheless, many of them revert to regression-based approaches, such as moderated multiple regression (MMR), when testing for moderating and other nonlinear effects. The predominance of MMR is likely due to the limited evidence showing the superiority of latent interaction approaches over regression-based approaches combined with the previous complicated procedures for testing latent interactions. In this teaching note, we first briefly explain the latent moderated structural equations (LMS) approach, which estimates latent interaction effects while controlling for measurement errors. Then we explain the reliability-corrected single-indicator LMS (RCSLMS) approach to testing latent interactions with summated scales and correcting for measurement errors, yielding results which approximate those from LMS. Next, we report simulation results illustrating that LMS and RCSLMS outperform MMR in terms of accuracy of point estimates and confidence intervals for interaction effects under various conditions. Then, we show how LMS and RCSLMS can be implemented with Mplus, providing an example-based tutorial to demonstrate a 4-step procedure for testing a range of latent interactions, as well as the decisions at each step. Finally, we conclude with answers to some frequently asked questions when testing latent interactions. As supplementary files to support researchers, we provide a narrated PowerPoint presentation, all Mplus syntax and output files, data sets for numerical examples, and Excel files for conducting the loglikelihood values difference test and plotting the latent interaction effects.

The present study proposes a new stochastic finite element method. The Karhunen–Loeve expansion is utilized to discretize the stochastic field, while the point estimate method is applied for calculating the random response of the structure. Two illustrative examples, including finite element models with one-dimensional and two-dimensional stochastic fields, are investigated to demonstrate the accuracy and efficiency of the proposed method. Furthermore, two classical finite element analysis methods are used to validate the results. It is proved that the proposed method can model both the one-dimensional and the two-dimensional stochastic finite element problems accurately and efficiently.

This paper examines the effect of social participation on cognitive performance by using data from a longitudinal survey of Chinese elderly. It addresses the problem of endogenous participation by exploiting the variation in changes of social participation that are driven by change in community service for social activities. The results show that participating in social activity has significantly positive impacts on the cognitive function of the elderly. The point estimates indicate that cognitive score increases by 29% of a standard deviation as a result of engaging in social activity. I also investigate heterogeneity and potential mechanisms of the effect.

For better performance of hyperspectral target detectors, the prior target spectrum is expected to be accurate and consistent with the target spectrum in the hyperspectral image to be detected. The existing hyperspectral target detection algorithms usually assume that the prior target spectrum is highly reliable. However, the label obtained is not always precise in practice, and pixels of the same object may have quite different spectra. Since it is hard to acquire a highly reliable prior target spectrum in some application scenarios, we propose a Bayesian constrained energy minimization (B-CEM) method for hyperspectral target detection. Instead of the point estimation of the target spectrum, we infer the posterior distribution of the true target spectrum based on the prior target spectrum. Specifically, considering the characteristics of hyperspectral image and target detection task, we adopt the Dirichlet distribution to approximate the true target spectrum. Experimental results on three datasets demonstrate the effectiveness of the proposed B-CEM when the known target spectrum is noisy or inconsistent with the true target spectrum. The necessity to approximate the true target spectrum is also proved. Generally, the distributional estimate achieves better performance than using the known target spectrum directly.

We investigate the relation between common institutional ownership of the firms in an industry and product market competition. We find that common ownership is neither robustly positively related with industry profitability or output prices nor is it robustly negatively related with measures of nonprice competition, as would be expected if common ownership reduces competition. This conclusion holds regardless of industry classification choice, common ownership measure, profitability measure, nonprice competition proxy, or model specification. Our point estimates are close to zero with tight bounds, rejecting even modestly sized economic effects. We conclude that antitrust restrictions seeking to limit intra-industry common ownership are not currently warranted.

The assumption of stationarity is too restrictive especially for long time series. This paper studies the change point problem through a change point estimator based on the φ-divergence which provides a rich set of distance like measures between pairs of distributions. The change point problem is considered in the following sub-fields: the problem of divergence estimation, testing for the homogeneity between two samples as well as estimating the time of change. The asymptotic distribution of the change point estimator is estimated by the limiting distribution of a stochastic process within given bounds through asymptotic theory surrounding the likelihood theory. The distribution is found to converge to that of a standardized Brownian bridge process.

There has been increasing interest in modeling survival data using deep learning methods in medical research. In this paper, we proposed a Bayesian hierarchical deep neural networks model for modeling and prediction of survival data. Compared with previously studied methods, the new proposal can provide not only point estimate of survival probability but also quantification of the corresponding uncertainty, which can be of crucial importance in predictive modeling and subsequent decision making. The favorable statistical properties of point and uncertainty estimates were demonstrated by simulation studies and real data analysis. The Python code implementing the proposed approach was provided.

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach.

In this paper, the randomised pseudolikelihood ratio change point estimator for GARCH models in [1] is employed and its limiting distribution is derived as the supremum of a standard Brownian bridge. Data analysis to validate the estimator is carried out using the United states dollar (USD)-Ghana cedi (GHS) daily exchange rate data. The randomised estimator is able to detect and estimate a single change in the variance structure of the data and provides a reference point for historic data analysis.

Citing of previous publications is an important factor in knowledge development. Because of the great amount of publications available, only a selection of studies gets cited, for varying reasons. If the selection of citations is associated with study outcome this is called citation bias. We will study determinants of citation in a broader sense, including e.g. study design, journal impact factor or the funding source of the publication. As a case study we assess which factors drive citation in the human literature on phthalates, specifically the metabolite mono(2-ethylhexyl) phthalate (MEHP). A systematic literature search identified all relevant publications on human health effect of MEHP. Data on potential determinants of citation were extracted in duplo. Specialized software was used to create a citation network, including all potential citation pathways. Random effect logistic regression was used to assess whether these determinants influence the likelihood of citation. 112 Publications on MEHP were identified, with 5684 potential citation pathways of which 551 were actual citations. Reporting of a harmful point estimate, journal impact factor, authority of the author, a male corresponding author, research performed in North America and self-citation were positively associated with the likelihood of being cited. In the literature on MEHP, citation is mostly driven by a number of factors that are not related to study outcome. Although the identified determinants do not necessarily give strong indications of bias, it shows selective use of published literature for a variety of reasons.

Lately, researchers have widely centered around renewable energy assets, for example, wind control and solar units to decrease the utilization of fossil fuels as the fundamental wellspring of ecological natural contaminations. One of the fundamental difficulties for the generation of wind and solar energies is that they frequently including the uncertainty because of the stochastic natures of wind speeds and solar radiation. Along these lines, the current uncertainty to assets the wind and solar units and necessary to assess the arranging strategy of distribution systems for having a dependable execution. To display the uncertainty related along with the wind and solar power, the point estimate method (PEM) is utilized. Weibull and Beta distributions are utilized to deal with uncertain information factors. These fundamental goals present work is to minimize the generation cost of the framework is enhanced dependent on the butterfly optimization algorithm (BOA). Four case test frameworks viewed as it is discovered that the proposed strategy gives better arrangement as far as execution time and normal cost viability. The recreation results demonstrate that the entrance of sustainable energy source builds, the generation cost diminishes. The outcomes acquired along with the butterfly optimization algorithm contrasted and another one understood strategy. Also, the precise distribution of generation cost.

In this paper, we design an innovative and general class of modified two-stage sampling schemes to enhance double sampling and modified double sampling procedures. Under the squared error loss plus...

Selective publication and reporting in individual papers compromise the scientific record, but are meta-analyses as compromised as their constituent studies? We systematically sampled 63 meta-analyses (each comprising at least 40 studies) in PLoS One, top medical journals, top psychology journals, and Metalab, an online, open-data database of developmental psychology meta-analyses. We empirically estimated publication bias in each, including only the peer-reviewed studies. Across all meta-analyses, we estimated that “statistically significant” results in the expected direction were only 1.17 times more likely to be published than “nonsignificant” results or those in the unexpected direction (95% CI: [0.93, 1.47]), with a confidence interval substantially overlapping the null. Comparable estimates were 0.83 for meta-analyses in PLoS One, 1.02 for top medical journals, 1.54 for top psychology journals, and 4.70 for Metalab. The severity of publication bias did differ across individual meta-analyses; in a small minority (10%; 95% CI: [2%, 21%]), publication bias appeared to favor “significant” results in the expected direction by more than threefold. We estimated that for 89% of meta-analyses, the amount of publication bias that would be required to attenuate the point estimate to the null exceeded the amount of publication bias estimated to be actually present in the vast majority of meta-analyses from the relevant scientific discipline (exceeding the 95th percentile of publication bias). Study-level measures (“statistical significance” with a point estimate in the expected direction and point estimate size) did not indicate more publication bias in higher-tier versus lower-tier journals, nor in the earliest studies published on a topic versus later studies. Overall, we conclude that the mere act of performing a meta-analysis with a large number of studies (at least 40) and that includes non-headline results may largely mitigate publication bias in meta-analyses, suggesting optimism about the validity of meta-analytic results.

Many reliability analysis methods require complicated mathematical process or access to comprehensive datasets. Such shortcomings limit their application to solve the geotechnical problems. It is of advantage to develop simpler reliability analysis methods that can be employed in the design of geotechnical structures. The current study suggests a simple framework for quick reliability analysis of deep excavation projects in urban areas, which is a common geotechnical problem. To investigate the feasibility of the presented method, five case studies were considered. It is worth mentioning that the horizontal displacement at the crest of excavation was set to be the main system response. For verification purposes, the results were compared to the random set, point estimate and Monte Carlo methods results, which are also used for reliability analysis of geotechnical problems. All case studies were recognized as projects of high importance and monitored during the excavation process. The field observations confirmed that the estimated probabilities of excessive deformations were reasonable for all cases. Comparing the modeling results and field measurements suggests the feasibility of the presented method for evaluating the reliability of deep urban excavations and estimating the horizontal displacement at the crest of excavations.

Summary A property, or statistical functional, is said to be elicitable if it minimizes the expected loss for some loss function. The study of which properties are elicitable sheds light on the capabilities and limitations of point estimation and empirical risk minimization. While recent work has sought to identify which properties are elicitable, here we investigate a more nuanced question: how many dimensions are required to indirectly elicit a given property? This number is called the elicitation complexity of the property. We lay the foundation for a general theory of elicitation complexity, which includes several basic results on how elicitation complexity behaves and the complexity of standard properties of interest. Building on this foundation, our main result gives tight complexity bounds for the broad class of Bayes risks. We apply these results to several properties of interest, including variance, entropy, norms and several classes of financial risk measures. The article concludes with a discussion and open questions.

The paper investigates the optimal kernel choice in heteroskedasticity and autocorrelation robust tests based on the fixed-b asymptotics. In parallel with the optimality of the quadratic spectral kernel under the asymptotic mean squared error criterion of the point estimator of the long run variance as considered in Andrews (1991) , we show that the optimality of the quadratic spectral kernel continues to hold under the testing-oriented criterion of Sun, Philips and Jin (2008) which takes a weighted average of the probabilities of type I and type II errors of the fixed-b asymptotic test.

Expert-coded datasets provide scholars with otherwise unavailable cross-national longitudinal data on important concepts. However, expert coders vary in their reliability and scale perception, potentially resulting in substantial measurement error; this variation may correlate with outcomes of interest, biasing results in analyses that use these data. This latter concern is particularly acute for key concepts in peace research. In this article, I describe potential sources of expert error, focusing on the measurement of identity-based discrimination. I then use expert-coded data on identity-based discrimination to examine: 1) the implications of measurement error for quantitative analyses that use expert-coded data, and 2) the degree to which different techniques for aggregating these data ameliorate these issues. To do so, I simulate data with different forms and levels of expert error and regress conflict onset on different aggregations of these data. These analyses yield two important results. First, almost all aggregations show a positive relationship between identity-based discrimination and conflict onset consistently across simulations, in line with the assumed true relationship between the concept and outcome. Second, different aggregation techniques vary in their substantive robustness beyond directionality. A structural equation model provides the most consistently robust estimates, while both the point estimates from an Item Response Theory (IRT) model and the average over expert codings provide similar and relatively robust estimates in most simulations. The median over expert codings and a naive multiple imputation technique yield the least robust estimates.

With complicated structures and complex physicochemical reactions, most industrial processes are intrinsically characterized by high dimensionality, nonlinearity, non-Gaussianity, and self-correlation. In this article, a novel probabilistic generative model, called weighted copula regression (WCR), is developed for complex processes. This method employs a probabilistic graphical tool (vine copula) to flexibly handle the underlying patterns via the factorization of complex dependence structures into multiple bivariate copulas. Monte Carlo estimation is incorporated for establishing a fast and reliable computing framework. To avoid overinterpretation of the industrial data, an adaptive sample selection strategy is proposed to explore the underlying distribution of the sample space and to select the most “informative” samples. By considering the copula weights that carry crucial information on the local data, the probabilistic WCR method can provide fast point estimate with prediction uncertainty for every predicted sample. The proposed WCR method is compared with six state-of-the-art methods, and the efficiency is validated using a numerical example and the ethylene cracking furnace process.

Perkembangan pemanfaatan panel surya di masa depan akan terus meningkat. Salah satu bentuk karakteristik panel surya merupakan kurva I-V yang mana dengan kurva tersebut dapat digunakan untuk menganalisa besaran daya keluaran panel surya. Dengan mengetahui kurva I-V tersebut dapat dilakukan Maximum Power Point Estimation (MPPE) yang dapat diampu oleh panel surya. Informasi mengenai nilai estimasi daya maksimum panel surya merupakan bagian penting untuk menentukan kapasitas pembebanan, selain itu juga untuk menjaga umur peralatan yang digunakan. Feed Forward Neural Network dengan Algoritma Back Propagation (FFBP) terbukti dapat memberikan informasi nilai MPPE pada keluaran panel surya. Nilai masukan pada ANN berupa tegangan dan arus dari panel surya, sedangkan keluaran dari ANN tersebut berupa nilai estimasi daya. Hasil dari simulasi MPPE didapatkan galat rata rata sebesar 0.04 poin antara daya aktual (MPP) dan daya estimasi (MPPE).

In this work, we utilize a multi-column CNNs framework to estimate the gaze point of a person sitting in front of a display from an RGB-D image of the person. Given that gaze points are determined by head poses, eyeball poses, and 3D eye positions, we propose to infer the three components separately and then integrate them for gaze point estimation. The captured depth images, however, usually contain noises and black holes which prevent us from acquiring reliable head pose and 3D eye position estimation. Therefore, we propose to refine the raw depth for 68 facial keypoints by first estimating their relative depths from RGB face images, which along with the captured raw depths are then used to solve the absolute depth for all facial keypoints through global optimization. The refined depths will provide us reliable estimation for both head pose and 3D eye position. Given that existing publicly available RGB-D gaze tracking datasets are small, we also build a new dataset for training and validating our method. To the best of our knowledge, it is the largest RGB-D gaze tracking dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the Eyediap dataset.

Concussion tolerance and head impact exposure are highly variable among football players. Recent findings highlight that head impact data analyses need to be performed at the subject level. In this paper, we describe a method of characterizing concussion risk between individuals using a new survival analysis technique developed with real-world head impact data in mind. Our approach addresses the limitations and challenges seen in previous risk analyses of football head impact data. Specifically, this demonstrative analysis appropriately models risk for a combination of left-censored recurrent events (concussions) and right-censored recurrent non-events (head impacts without concussion). Furthermore, the analysis accounts for uneven impact sampling between players. In brief, we propose using the Consistent Threshold method to develop subject-specific risk curves and then determine average risk point estimates between subjects at injurious magnitude values. We describe an approach for selecting an optimal cumulative distribution function to model risk between subjects by minimizing injury prediction error. We illustrate that small differences in distribution fit can result in large predictive errors. Given the vast amounts of on-field data researchers are collecting across sports, this approach can be applied to develop population-specific risk curves that can ultimately inform interventions that reduce concussion incidence.

Single platform aerial line transect and land-based surveys of Southern Hemisphere Group IV humpback whales were undertaken to provide absoluteabundance estimates of animals migrating northward along the western Australian coast during June–August 2005. The aerial survey was designedto cover the whole period of northward migration but the resulting estimates from that survey alone could only, at best, provide relative abundanceestimates as it was not possible to estimate g(0), the detection probability along the trackline, from the data. Owing to logistical constraints, theland-based survey was only possible for a much shorter period (two weeks during the expected peak of the migration in mid-July). This paperproposes three methods that utilise these complementary data in different ways to attempt to obtain absolute abundance estimates. The aerial linetransect data were used to estimate relative whale density (for each day), allowing absolute abundance from the land-based survey to be estimatedfor the short period of its duration. In turn, the land-based survey allowed estimation of g(0) for the aerial survey. Absolute estimates of abundancefor the aerial survey were obtained by combining the g(0) estimate with the relative density estimates, summing over the appropriate number ofdays. The most reliable estimate of northward migrating whales passing the land station for the period of the land-based survey only was 4,700(95% CI 2,700–14,000). The most reliable estimate for the number of whales passing through the aerial survey region for the duration of that survey(55 days from June through to August) was 10,300 (95% CI 6,700–24,500). This is a conservative estimate because the duration of the aerial surveywas almost certainly shorter than the period of the migration. Extrapolation beyond the end of this survey was considered unreliable, but abundancefrom the estimated start of the migration to the end of the survey (87 days from mid-April to August) was estimated to be 12,800 (95% CI 7,500–44,600). The estimated number of whales depends crucially on the assumed migration and period of migration. Results for different migrationparameters are also presented. The point estimates of abundance, whilst higher than those from a previous survey in 1999 (when adjusted for surveyduration) are not significantly so. The peak of the whales’ distribution was found at c.90m water depth.

Social hierarchy theory predicts that the power and status of an organizational function have a first-order effect on the function's ability to influence outcomes. We find that the rank of the title of the top tax executive is positively associated with tax planning after controlling for treatment effects. Our inferences remain when (1) using changes in the size of the c-suite as a shock to the relative power and status of the tax function, and (2) examining promotions and demotions in title rank. Point estimates suggest that tax function power and status are up to 2.6 times as important as tax planning resources, up to 4.0 times as important as tax function-specific expertise, and, more often than not, more important than manager fixed effects. Overall, results suggest that the power and status of the tax function is often what matters most in determining tax outcomes. JEL Classifications: H25; L22; M41.

Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability. We explore the inverse rendering problem of procedural material parameter estimation from photographs, presenting a unified view of the problem in a Bayesian framework. In addition to computing point estimates of the parameters by optimization, our framework uses a Markov Chain Monte Carlo approach to sample the space of plausible material parameters, providing a collection of plausible matches that a user can choose from, and efficiently handling both discrete and continuous model parameters. To demonstrate the effectiveness of our framework, we fit procedural models of a range of materials---wall plaster, leather, wood, anisotropic brushed metals and layered metallic paints---to both synthetic and real target images., 12 pages, 13 figures, Pacific Graphics 2020

Purely sequential estimation for unknown mean ( μ ) in a normal population having an unknown variance ( σ 2 ) when observations are gathered in groups has been recently discussed in Mukhopadhyay an...

Introduction: Rapid spread of COVID-19 has become a major concern worldwide. Strong adherence to preventive measures can help to break the chain of the spread of coronavirus. We conducted this study to find out the extent of information general people of Eastern Nepal have regarding COVID-19 and their attitude and practice towards preventing its spread. Methods: A descriptive cross-sectional online study was done among the people of Eastern Nepal on knowledge, attitude, and practice related to COVID-19 from May 1st to May 15th after obtaining ethical clearance from the ethical review board (ERB) (ref no. 319/2020 P). A 20 item survey instrument was adapted using WHO course materials on an emerging COVID-19. A convenience sample method was used. Data were collected and entered in Statistical Packages for Social Services version 11.5. Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary data. Results: Among 1069 respondents, the correct answer on the COVID-19 related knowledge questionnaire was 958 (89.61%), 487 (93.11%) were health professionals, and 471 (86.26%) non-health professionals. Preventive measures were strictly followed by 1044 (97.66%) participants. A wrong perception about the disease was present in 390 (36.48%). Health ministry website 356 (33.30%) followed by news media 309 (29%) was the major source of information among the people. Conclusions: Knowledge regarding COVID-19 among people the selected population of eastern is satisfactory which was similar to other studies done. However, people still have misperceptions regarding the disease and do not strictly follow the preventive measures.

This work presents an application of upgraded arithmetic operations on trapezoidal fuzzy number to extend the reliability from point estimation to interval estimation. A realistic example of an acc...

The mixture cure model has been widely applied to survival data in which a fraction of the observations never experience the event of interest, despite long-term follow-up. In this paper, we study the Cox proportional hazards mixture cure model where the covariate effects on the distribution of uncured subjects’ failure time may jump when a covariate exceeds a change point. The nonparametric maximum likelihood estimation is used to obtain the semiparametric estimates. We employ a two-step computational procedure involving the Expectation-Maximization algorithm to implement the estimation. The consistency, convergence rate and asymptotic distributions of the estimators are carefully established under technical conditions and we show that the change point estimator is n consistency. The m out of n bootstrap and the Louis algorithm are used to obtain the standard errors of the estimated change point and other regression parameter estimates, respectively. We also contribute a test procedure to check the existence of the change point. The finite sample performance of the proposed method is demonstrated via simulation studies and real data examples.

The probability of agreement has been used as an effective strategy for quantifying the similarity between the reliability of two populations. By contrast to hypothesis testing approaches based on P-values, the probability of agreement provides a more realistic assessment of similarity by emphasizing practically important differences. In this article, we propose the use of the probability of agreement to evaluate the similarity of two Kaplan-Meier curves, which estimate the survival functions in two populations. This article extends the probability of agreement paradigm to right censored data and explores three different methods of quantifying uncertainty in the probability of agreement estimate. The first approach provides a convenient assessment based on large-sample normal-theory (LSNT), while the other two approaches are nonparametric alternatives based on ordinary and fractional random-weight bootstrap (FRWB) techniques. All methods are illustrated with examples for which comparing the survival curves of related populations is of interest and the efficacy of the methods are also evaluated through simulation studies. Based on these simulations we recommend point estimation using the proposed LSNT calculation and confidence interval estimation via the FRWB approach. We also provide a Shiny app that facilitates an automated implementation of the methodology.