Stats Pub Date : 2023-02-08 DOI: 10.3390/stats6010017

Nikolaos I. Panayiotou, I. Triantafyllou

引用次数: 0

Point Cloud Registration via Heuristic Reward Reinforcement Learning 基于启发式奖励强化学习的点云配准

Stats Pub Date : 2023-02-06 DOI: 10.3390/stats6010016

Bingren Chen

引用次数: 1

Farlie–Gumbel–Morgenstern Bivariate Moment Exponential Distribution and Its Inferences Based on Concomitants of Order Statistics Farlie–Gumbel–Morgenstern双变量矩指数分布及其基于阶统计量的推论

Stats Pub Date : 2023-02-03 DOI: 10.3390/stats6010015

S. P. Arun, C. Chesneau, R. Maya, M. Irshad

引用次数: 1

A New Class of Alternative Bivariate Kumaraswamy-Type Models: Properties and Applications 一类新的可替换二元Kumaraswamy型模型：性质与应用

Stats Pub Date : 2023-01-30 DOI: 10.3390/stats6010014

I. Ghosh

引用次数: 1

Bayesian Logistic Regression Model for Sub-Areas 子区域的贝叶斯逻辑回归模型

Stats Pub Date : 2023-01-29 DOI: 10.3390/stats6010013

Lu Chen, B. Nandram

{"title":"Bayesian Logistic Regression Model for Sub-Areas","authors":"Lu Chen, B. Nandram","doi":"10.3390/stats6010013","DOIUrl":"https://doi.org/10.3390/stats6010013","url":null,"abstract":"Many population-based surveys have binary responses from a large number of individuals in each household within small areas. One example is the Nepal Living Standards Survey (NLSS II), in which health status binary data (good versus poor) for each individual from sampled households (sub-areas) are available in the sampled wards (small areas). To make an inference for the finite population proportion of individuals in each household, we use the sub-area logistic regression model with reliable auxiliary information. The contribution of this model is twofold. First, we extend an area-level model to a sub-area level model. Second, because there are numerous sub-areas, standard Markov chain Monte Carlo (MCMC) methods to find the joint posterior density are very time-consuming. Therefore, we provide a sampling-based method, the integrated nested normal approximation (INNA), which permits fast computation. Our main goal is to describe this hierarchical Bayesian logistic regression model and to show that the computation is much faster than the exact MCMC method and also reasonably accurate. The performance of our method is studied by using NLSS II data. Our model can borrow strength from both areas and sub-areas to obtain more efficient and precise estimates. The hierarchical structure of our model captures the variation in the binary data reasonably well.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41412551","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}

引用次数: 0

Comparing Robust Linking and Regularized Estimation for Linking Two Groups in the 1PL and 2PL Models in the Presence of Sparse Uniform Differential Item Functioning 比较1PL和2PL模型中存在稀疏一致微分项函数的两组连接的鲁棒连接和正则化估计

Stats Pub Date : 2023-01-25 DOI: 10.3390/stats6010012

A. Robitzsch

引用次数: 2

Informative g-Priors for Mixed Models 混合模型的信息g先验

Stats Pub Date : 2023-01-16 DOI: 10.3390/stats6010011

Yu-Fang Chien, Haiming Zhou, T. Hanson, Theodore C. Lystig

引用次数: 1

A Novel Flexible Class of Intervened Poisson Distribution by Lagrangian Approach 基于拉格朗日方法的一类新的柔性干涉泊松分布

Stats Pub Date : 2023-01-15 DOI: 10.3390/stats6010010

M. Irshad, M. Monisha, C. Chesneau, R. Maya, D. S. Shibu

{"title":"A Novel Flexible Class of Intervened Poisson Distribution by Lagrangian Approach","authors":"M. Irshad, M. Monisha, C. Chesneau, R. Maya, D. S. Shibu","doi":"10.3390/stats6010010","DOIUrl":"https://doi.org/10.3390/stats6010010","url":null,"abstract":"The zero-truncated Poisson distribution (ZTPD) generates a statistical model that could be appropriate when observations begin once at least one event occurs. The intervened Poisson distribution (IPD) is a substitute for the ZTPD, in which some intervention processes may change the mean of the rare events. These two zero-truncated distributions exhibit underdispersion (i.e., their variance is less than their mean). In this research, we offer an alternative solution for dealing with intervention problems by proposing a generalization of the IPD by a Lagrangian approach called the Lagrangian intervened Poisson distribution (LIPD), which in fact generalizes both the ZTPD and the IPD. As a notable feature, it has the ability to analyze both overdispersed and underdispersed datasets. In addition, the LIPD has a closed-form expression of all of its statistical characteristics, as well as an increasing, decreasing, bathtub-shaped, and upside-down bathtub-shaped hazard rate function. A consequent part is devoted to its statistical application. The maximum likelihood estimation method is considered, and the effectiveness of the estimates is demonstrated through a simulated study. To evaluate the significance of the new parameter in the LIPD, a generalized likelihood ratio test is performed. Subsequently, we present a new count regression model that is suitable for both overdispersed and underdispersed datasets using the mean-parametrized form of the LIPD. Additionally, the LIPD’s relevance and application are shown using real-world datasets.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42405726","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}

引用次数: 2

Acknowledgment to the Reviewers of Stats in 2022 对2022年统计审稿人的感谢

Stats Pub Date : 2023-01-12 DOI: 10.3390/stats6010009

引用次数: 0

Statistical Prediction of Future Sports Records Based on Record Values 基于记录值的未来体育记录的统计预测

Stats Pub Date : 2023-01-11 DOI: 10.3390/stats6010008

Christina Empacher, U. Kamps, G. Volovskiy

引用次数: 1

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