IF 1.3 4区数学

Test Pub Date : 2024-02-14 DOI: 10.1007/s11749-024-00921-1

Yang Liu, Yukun Liu, Pengfei Li, Riquan Zhang

{"title":"Two-step semiparametric empirical likelihood inference from capture–recapture data with missing covariates","authors":"Yang Liu, Yukun Liu, Pengfei Li, Riquan Zhang","doi":"10.1007/s11749-024-00921-1","DOIUrl":"https://doi.org/10.1007/s11749-024-00921-1","url":null,"abstract":"Missing covariates are not uncommon in capture–recapture studies. When covariate information is missing at random in capture–recapture data, an empirical full likelihood method has been demonstrated to outperform conditional-likelihood-based methods in abundance estimation. However, the fully observed covariates must be discrete, and the method is not directly applicable to continuous-time capture–recapture data. Based on the Binomial and Poisson regression models, we propose a two-step semiparametric empirical likelihood approach for abundance estimation in the presence of missing covariates, regardless of whether the fully observed covariates are discrete or continuous. We show that the maximum semiparametric empirical likelihood estimators for the underlying parameters and the abundance are asymptotically normal, and more efficient than the counterpart for a completely known non-missingness probability. After scaling, the empirical likelihood ratio test statistic for abundance follows a limiting chi-square distribution with one degree of freedom. The proposed approach is further extended to one-inflated count regression models, and a score-like test is constructed to assess whether one-inflation exists among the number of captures. Our simulation shows that, compared with the previous method, the proposed method not only performs better in correcting bias, but also has a more accurate coverage in the presence of fully observed continuous covariates, although there may be a slight efficiency loss when the fully observed covariates are only discrete. The performance of the new method is illustrated by analyses of the yellow-bellied prinia data and the rana pretiosa data.","PeriodicalId":51189,"journal":{"name":"Test","volume":"73 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A copula formulation for multivariate latent Markov models 多元潜马尔可夫模型的共轭公式

IF 1.3 4区数学

Test Pub Date : 2024-02-07 DOI: 10.1007/s11749-024-00919-9

Alfonso Russo, Alessio Farcomeni

引用次数: 0

Change-point detection in a tensor regression model 张量回归模型中的变化点检测

IF 1.3 4区数学

Test Pub Date : 2024-02-06 DOI: 10.1007/s11749-023-00915-5

Mai Ghannam, Sévérien Nkurunziza

{"title":"Change-point detection in a tensor regression model","authors":"Mai Ghannam, Sévérien Nkurunziza","doi":"10.1007/s11749-023-00915-5","DOIUrl":"https://doi.org/10.1007/s11749-023-00915-5","url":null,"abstract":"In this paper, we consider an inference problem in a tensor regression model with one change-point. Specifically, we consider a general hypothesis testing problem on a tensor parameter and the studied testing problem includes as a special case the problem about the absence of a change-point. To this end, we derive the unrestricted estimator (UE) and the restricted estimator (RE) as well as the joint asymptotic normality of the UE and RE. Thanks to the established asymptotic normality, we derive a test for testing the hypothesized restriction. We also derive the asymptotic power of the proposed test and we prove that the established test is consistent. Beyond the complexity of the testing problem in the tensor model, we consider a very general case where the tensor error term and the regressors do not need to be independent and the dependence structure of the outer-product of the tensor error term and regressors is as weak as that of an (mathcal {L}^2-) mixingale. Further, to study the performance of the proposed methods in small and moderate sample sizes, we present some simulation results that corroborate the theoretical results. Finally, to illustrate the application of the proposed methods, we test the non-existence of a change-point in some fMRI neuro-imaging data.\u0000","PeriodicalId":51189,"journal":{"name":"Test","volume":"29 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian sample size determination for detecting heterogeneity in multi-site replication studies 贝叶斯样本量确定法检测多站点复制研究中的异质性

IF 1.3 4区数学

Test Pub Date : 2024-01-23 DOI: 10.1007/s11749-023-00916-4

Konstantinos Bourazas, Guido Consonni, Laura Deldossi

引用次数: 0

Comments on: Shape-based functional data analysis 评论基于形状的功能数据分析

IF 1.3 4区数学

Test Pub Date : 2024-01-18 DOI: 10.1007/s11749-023-00914-6

J. E. Borgert, J. S. Marron

引用次数: 0

LRD spectral analysis of multifractional functional time series on manifolds 流形上多分量函数时间序列的 LRD 频谱分析