Stats最新文献_第8页

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

Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe 欧洲长期气温序列状态空间模型的变化点检测

Stats Pub Date : 2023-01-04 DOI: 10.3390/stats6010007

Magda Monteiro, M. Costa

引用次数: 2

An ϵ-Greedy Multiarmed Bandit Approach to Markov Decision Processes 一种ϵ-Greedy马尔可夫决策过程的多臂强盗方法

Stats Pub Date : 2023-01-01 DOI: 10.3390/stats6010006

Isa Muqattash, Jiaqiao Hu

引用次数: 0

Applying the Multilevel Approach in Estimation of Income Population Differences 多层次方法在收入人口差异估计中的应用

Stats Pub Date : 2022-12-29 DOI: 10.3390/stats6010005

V. Timiryanova, Dina Krasnoselskaya, N. Kuzminykh

引用次数: 0

Estimating Smoothness and Optimal Bandwidth for Probability Density Functions 估计平滑度和最优带宽的概率密度函数

Stats Pub Date : 2022-12-27 DOI: 10.3390/stats6010003

D. Politis, P. Tarassenko, V. Vasiliev

{"title":"Estimating Smoothness and Optimal Bandwidth for Probability Density Functions","authors":"D. Politis, P. Tarassenko, V. Vasiliev","doi":"10.3390/stats6010003","DOIUrl":"https://doi.org/10.3390/stats6010003","url":null,"abstract":"The properties of non-parametric kernel estimators for probability density function from two special classes are investigated. Each class is parametrized with distribution smoothness parameter. One of the classes was introduced by Rosenblatt, another one is introduced in this paper. For the case of the known smoothness parameter, the rates of mean square convergence of optimal (on the bandwidth) density estimators are found. For the case of unknown smoothness parameter, the estimation procedure of the parameter is developed and almost surely convergency is proved. The convergence rates in the almost sure sense of these estimators are obtained. Adaptive estimators of densities from the given class on the basis of the constructed smoothness parameter estimators are presented. It is shown in examples how parameters of the adaptive density estimation procedures can be chosen. Non-asymptotic and asymptotic properties of these estimators are investigated. Specifically, the upper bounds for the mean square error of the adaptive density estimators for a fixed sample size are found and their strong consistency is proved. The convergence of these estimators in the almost sure sense is established. Simulation results illustrate the realization of the asymptotic behavior when the sample size grows large.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48907693","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

Data Cloning Estimation and Identification of a Medium-Scale DSGE Model 一个中尺度DSGE模型的数据克隆估计与识别

Stats Pub Date : 2022-12-24 DOI: 10.3390/stats6010002

Pedro Chaim, M. Laurini

引用次数: 1

A Semiparametric Tilt Optimality Model 一个半参数倾斜最优模型

Stats Pub Date : 2022-12-22 DOI: 10.3390/stats6010001

Chathurangi H. Pathiravasan, B. Bhattacharya

引用次数: 0