Pub Date : 2023-02-02DOI: 10.1007/s42519-023-00319-6
S. Buckland, D. Borchers, T. Marques, R. Fewster
{"title":"Wildlife Population Assessment: Changing Priorities Driven by Technological Advances","authors":"S. Buckland, D. Borchers, T. Marques, R. Fewster","doi":"10.1007/s42519-023-00319-6","DOIUrl":"https://doi.org/10.1007/s42519-023-00319-6","url":null,"abstract":"","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41970979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1007/s42519-022-00308-1
Abeera Shakeel, Asif Kamal, G. Tesema, M. Siddiqa
{"title":"Analysis of Spatial Patterns and Associated Factors of Stillbirth in Pakistan, PDHS (2017–18): A Spatial and Multilevel Analysis","authors":"Abeera Shakeel, Asif Kamal, G. Tesema, M. Siddiqa","doi":"10.1007/s42519-022-00308-1","DOIUrl":"https://doi.org/10.1007/s42519-022-00308-1","url":null,"abstract":"","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 1","pages":"1-26"},"PeriodicalIF":0.6,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49328984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-10DOI: 10.1007/s42519-022-00315-2
J. Morgan, S. Bagchi
{"title":"Optimality of Some Row–Column Designs","authors":"J. Morgan, S. Bagchi","doi":"10.1007/s42519-022-00315-2","DOIUrl":"https://doi.org/10.1007/s42519-022-00315-2","url":null,"abstract":"","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 1","pages":"1-25"},"PeriodicalIF":0.6,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45777105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-06DOI: 10.1007/s42519-022-00317-0
N. Modarresi, S. Rezakhah, M. Mohammadi
{"title":"Semi-Lévy-Driven CARMA Process: Estimation and Prediction","authors":"N. Modarresi, S. Rezakhah, M. Mohammadi","doi":"10.1007/s42519-022-00317-0","DOIUrl":"https://doi.org/10.1007/s42519-022-00317-0","url":null,"abstract":"","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 1","pages":"1-23"},"PeriodicalIF":0.6,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47754058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/s42519-023-00325-8
Mei Ling Huang, Yansan Han, William Marshall
Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem. Regular linear quantile regression uses an L1 loss function [Koenker in Quantile regression, Cambridge University Press, Cambridge, 2005], and the optimal solution of linear programming for estimating coefficients of regression. A problem with linear quantile regression is that the estimated curves for different quantiles can cross, a result that is logically inconsistent. To overcome the curves crossing problem, and to improve high quantile estimation in the nonlinear case, this paper proposes a nonparametric quantile regression method to estimate high conditional quantiles. A three-step computational algorithm is given, and the asymptotic properties of the proposed estimator are derived. Monte Carlo simulations show that the proposed method is more efficient than linear quantile regression method. Furthermore, this paper investigates COVID-19 and blood pressure real-world examples of extreme events by using the proposed method.
极端事件，如地震、海啸和市场崩溃，可以对社会和生态系统产生重大影响。分位数回归可以用于预测这些极端事件，使其成为一个在许多领域都有应用的重要问题。估计高条件分位数是一个难题。正则线性分位数回归使用1损失函数[Koenker in quantile regression, Cambridge University Press, Cambridge, 2005]，并使用线性规划的最优解来估计回归系数。线性分位数回归的一个问题是，不同分位数的估计曲线可能交叉，结果在逻辑上是不一致的。为了克服曲线交叉问题，改进非线性情况下的高分位数估计，本文提出了一种非参数分位数回归方法来估计高条件分位数。给出了一个三步计算算法，并给出了该估计量的渐近性质。蒙特卡罗仿真结果表明，该方法比线性分位数回归方法更有效。此外，本文还利用该方法研究了COVID-19和血压极端事件的现实例子。
{"title":"An Algorithm of Nonparametric Quantile Regression.","authors":"Mei Ling Huang, Yansan Han, William Marshall","doi":"10.1007/s42519-023-00325-8","DOIUrl":"https://doi.org/10.1007/s42519-023-00325-8","url":null,"abstract":"<p><p>Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem. Regular linear quantile regression uses an <i>L</i> <sub>1</sub> loss function [Koenker in Quantile regression, Cambridge University Press, Cambridge, 2005], and the optimal solution of linear programming for estimating coefficients of regression. A problem with linear quantile regression is that the estimated curves for different quantiles can cross, a result that is logically inconsistent. To overcome the curves crossing problem, and to improve high quantile estimation in the nonlinear case, this paper proposes a nonparametric quantile regression method to estimate high conditional quantiles. A three-step computational algorithm is given, and the asymptotic properties of the proposed estimator are derived. Monte Carlo simulations show that the proposed method is more efficient than linear quantile regression method. Furthermore, this paper investigates COVID-19 and blood pressure real-world examples of extreme events by using the proposed method.</p>","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 2","pages":"32"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10057703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9794184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/s42519-023-00324-9
Eda Gizem Koçyiğit, Khalid Ul Islam Rather
In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1-23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretically with other estimators. The theoretical results have been supported by the different simulations and real-life data sets studies and have shown that the proposed estimator is more effective than the estimators in the literature. It is also seen that the number of repetitions in the RSS affected the effectiveness of the sub-estimators.
在本研究中，基于Koçyiğit和Kadılar (common Stat Theory Methods 1- 23,2022)中给出的子比率估计器的思想，提出了一种新的排序集抽样(RSS)的子回归型估计器。得到了无偏估计量的均方误差，并与其他估计量进行了理论比较。理论结果得到了不同模拟和实际数据集研究的支持，并表明所提出的估计器比文献中的估计器更有效。还可以看出，RSS中的重复次数影响了子估计器的有效性。
{"title":"The New Sub-regression Type Estimator in Ranked Set Sampling.","authors":"Eda Gizem Koçyiğit, Khalid Ul Islam Rather","doi":"10.1007/s42519-023-00324-9","DOIUrl":"https://doi.org/10.1007/s42519-023-00324-9","url":null,"abstract":"<p><p>In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1-23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretically with other estimators. The theoretical results have been supported by the different simulations and real-life data sets studies and have shown that the proposed estimator is more effective than the estimators in the literature. It is also seen that the number of repetitions in the RSS affected the effectiveness of the sub-estimators.</p>","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 2","pages":"27"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9442109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/s42519-022-00305-4
Dasom Lee, Sujit Ghosh
In many clinical trials, patient outcomes are often binary-valued which are measured asynchronously over time across various dose levels. To account for autocorrelation among such longitudinally observed outcomes, a first-order Markov model for binary data is developed. Moreover, to account for the asynchronously observed time points, nonhomogeneous models for the transition probabilities are proposed. The transition probabilities are modeled using B-spline basis functions after suitable transformations. Additionally, if the underlying dose-response curve is assumed to be non-decreasing, our model allows for the estimation of any underlying non-decreasing curve based on suitably constructed prior distributions. We also extended our model to the mixed effect model to incorporate individual-specific random effects. Numerical comparisons with traditional models are provided based on simulated data sets, and also practical applications are illustrated using real data sets.
{"title":"Bayesian Analysis of First-Order Markov Models for Autocorrelated Binary Responses.","authors":"Dasom Lee, Sujit Ghosh","doi":"10.1007/s42519-022-00305-4","DOIUrl":"https://doi.org/10.1007/s42519-022-00305-4","url":null,"abstract":"<p><p>In many clinical trials, patient outcomes are often binary-valued which are measured asynchronously over time across various dose levels. To account for autocorrelation among such longitudinally observed outcomes, a first-order Markov model for binary data is developed. Moreover, to account for the asynchronously observed time points, nonhomogeneous models for the transition probabilities are proposed. The transition probabilities are modeled using B-spline basis functions after suitable transformations. Additionally, if the underlying dose-response curve is assumed to be non-decreasing, our model allows for the estimation of any underlying non-decreasing curve based on suitably constructed prior distributions. We also extended our model to the mixed effect model to incorporate individual-specific random effects. Numerical comparisons with traditional models are provided based on simulated data sets, and also practical applications are illustrated using real data sets.</p>","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 1","pages":"9"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10480610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, stein-type two-stage sampling procedure is carried out for fixed accuracy confidence interval estimation of the common variance ( ) parameter corresponding to multivariate normal distribution with autoregressive covariance structure of order 1. Related asymptotics are obtained and simulation results are presented.
{"title":"On Fixed Accuracy Confidence Interval in Multivariate Normal Distribution with Order 1 Autoregressive Covariance Structure.","authors":"Pritam Sarkar, Uttam Bandyopadhyay, Rahul Bhattacharya","doi":"10.1007/s42519-022-00310-7","DOIUrl":"https://doi.org/10.1007/s42519-022-00310-7","url":null,"abstract":"<p><p>In this paper, stein-type two-stage sampling procedure is carried out for fixed accuracy confidence interval estimation of the common variance ( <math><msup><mi>σ</mi> <mn>2</mn></msup> </math> ) parameter corresponding to multivariate normal distribution with autoregressive covariance structure of order 1. Related asymptotics are obtained and simulation results are presented.</p>","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":"17 1","pages":"13"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10401552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-20DOI: 10.1007/s42519-022-00314-3
N. Balakrishnan, C. Charalambides, T. Christofides, M. Koutras, S. Meintanis
{"title":"Preface to a Special Issue in Memory of Professor Theophilos Cacoullos","authors":"N. Balakrishnan, C. Charalambides, T. Christofides, M. Koutras, S. Meintanis","doi":"10.1007/s42519-022-00314-3","DOIUrl":"https://doi.org/10.1007/s42519-022-00314-3","url":null,"abstract":"","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45072875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1007/s42519-022-00313-4
Ayan Pal, D. Samanta, D. Kundu
{"title":"Cure Rate-Based Step-Stress Model","authors":"Ayan Pal, D. Samanta, D. Kundu","doi":"10.1007/s42519-022-00313-4","DOIUrl":"https://doi.org/10.1007/s42519-022-00313-4","url":null,"abstract":"","PeriodicalId":45853,"journal":{"name":"Journal of Statistical Theory and Practice","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47320296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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