Journal of Statistical Planning and Inference最新文献_第3页

Maximum likelihood estimation of short panel autoregressive models with flexible form of fixed effects 具有灵活形式固定效应的短面板自回归模型的最大似然估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-12-18 DOI: 10.1016/j.jspi.2024.106252

Kazuhiko Hayakawa, Boyan Yin

引用次数: 0

Outcome dependent subsampling divide and conquer in generalized linear models for massive data 海量数据广义线性模型的结果依赖子抽样分治方法

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-12-04 DOI: 10.1016/j.jspi.2024.106253

Jie Yin , Jieli Ding , Changming Yang

引用次数: 0

Nonparametric estimators of inequality curves and inequality measures 不等式曲线的非参数估计和不等式测度

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-11-28 DOI: 10.1016/j.jspi.2024.106251

Alicja Jokiel-Rokita, Sylwester Pia̧tek

引用次数: 0

Estimation and group-feature selection in sparse mixture-of-experts with diverging number of parameters 参数数量分散的稀疏专家混合物中的估计和组特征选择

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-11-19 DOI: 10.1016/j.jspi.2024.106250

Abbas Khalili , Archer Yi Yang , Xiaonan Da

引用次数: 0

Modeling and testing for endpoint-inflated count time series with bounded support 有界支持端点膨胀计数时间序列的建模与测试

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-11-15 DOI: 10.1016/j.jspi.2024.106248

Yao Kang , Xiaojing Fan , Jie Zhang , Ying Tang

{"title":"Modeling and testing for endpoint-inflated count time series with bounded support","authors":"Yao Kang , Xiaojing Fan , Jie Zhang , Ying Tang","doi":"10.1016/j.jspi.2024.106248","DOIUrl":"10.1016/j.jspi.2024.106248","url":null,"abstract":"<div><div>Count time series with bounded support frequently exhibit binomial overdispersion, zero inflation and right-endpoint inflation in practical scenarios. Numerous models have been proposed for the analysis of bounded count time series with binomial overdispersion and zero inflation, yet right-endpoint inflation has received comparatively less attention. To better capture these features, this article introduces three versions of extended first-order binomial autoregressive (BAR(1)) models with endpoint inflation. Corresponding stochastic properties of the new models are investigated and model parameters are estimated by the conditional maximum likelihood and quasi-maximum likelihood methods. A binomial right-endpoint inflation index is also constructed and further used to test whether the data set has endpoint-inflated characteristic with respect to a BAR(1) process. Finally, the proposed models are applied to two real data examples. Firstly, we illustrate the usefulness of the proposed models through an application to the voting data on supporting interest rate changes during consecutive monthly meetings of the Monetary Policy Council at the National Bank of Poland. Then, we apply the proposed models to the number of police stations that received at least one drunk driving report per month. The results of the two real data examples indicate that the new models have significant advantages in terms of fitting performance for the bounded count time series with endpoint inflation.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"237 ","pages":"Article 106248"},"PeriodicalIF":0.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759599","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

Semi-parametric empirical likelihood inference on quantile difference between two samples with length-biased and right-censored data 利用长度偏差和右删失数据对两个样本之间的量差进行半参数经验似然推断

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-11-14 DOI: 10.1016/j.jspi.2024.106249

Li Xun , Xin Guan , Yong Zhou

{"title":"Semi-parametric empirical likelihood inference on quantile difference between two samples with length-biased and right-censored data","authors":"Li Xun , Xin Guan , Yong Zhou","doi":"10.1016/j.jspi.2024.106249","DOIUrl":"10.1016/j.jspi.2024.106249","url":null,"abstract":"<div><div>Exploring quantile differences between two populations at various probability levels offers valuable insights into their distinctions, which are essential for practical applications such as assessing treatment effects. However, estimating these differences can be challenging due to the complex data often encountered in clinical trials. This paper assumes that right-censored data and length-biased right-censored data originate from two populations of interest. We propose an adjusted smoothed empirical likelihood (EL) method for inferring quantile differences and establish the asymptotic properties of the proposed estimators. Under mild conditions, we demonstrate that the adjusted log-EL ratio statistics asymptotically follow the standard chi-squared distribution. We construct confidence intervals for the quantile differences using both normal and chi-squared approximations and develop a likelihood ratio test for these differences. The performance of our proposed methods is illustrated through simulation studies. Finally, we present a case study utilizing Oscar award nomination data to demonstrate the application of our method.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"237 ","pages":"Article 106249"},"PeriodicalIF":0.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705362","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

Sieve estimation of the accelerated mean model based on panel count data 基于面板计数数据的加速平均模型的筛分估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-11-12 DOI: 10.1016/j.jspi.2024.106247

Xiaoyang Li , Zhi-Sheng Ye , Xingqiu Zhao

{"title":"Sieve estimation of the accelerated mean model based on panel count data","authors":"Xiaoyang Li , Zhi-Sheng Ye , Xingqiu Zhao","doi":"10.1016/j.jspi.2024.106247","DOIUrl":"10.1016/j.jspi.2024.106247","url":null,"abstract":"<div><div>Panel count data are gathered when subjects are examined at discrete times during a study, and only the number of recurrent events occurring before each examination time is recorded. We consider a semiparametric accelerated mean model for panel count data in which the effect of the covariates is to transform the time scale of the baseline mean function. Semiparametric inference for the model is inherently challenging because the finite-dimensional regression parameters appear in the argument of the (infinite-dimensional) functional parameter, i.e., the baseline mean function, leading to the phenomenon of bundled parameters. We propose sieve pseudolikelihood and likelihood methods to construct the random criterion function for estimating the model parameters. An inexact block coordinate ascent algorithm is used to obtain these estimators. We establish the consistency and rate of convergence of the proposed estimators, as well as the asymptotic normality of the estimators of the regression parameters. Novel consistent estimators of the asymptotic covariances of the estimated regression parameters are derived by leveraging the counting process associated with the examination times. Comprehensive simulation studies demonstrate that the optimization algorithm is much less sensitive to the initial values than the Newton–Raphson method. The proposed estimators perform well for practical sample sizes, and are more efficient than existing methods. An example based on real data shows that due to this efficiency gain, the proposed method is better able to detect the significance of practically meaningful covariates than an existing method.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"237 ","pages":"Article 106247"},"PeriodicalIF":0.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660066","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

The proximal bootstrap for constrained estimators 受约束估计器的近似自举法

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-10-28 DOI: 10.1016/j.jspi.2024.106245

Jessie Li

引用次数: 0

Testing the equality of distributions using integrated maximum mean discrepancy 利用综合最大均值差异测试分布的相等性

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-10-25 DOI: 10.1016/j.jspi.2024.106246

Tianxuan Ding , Zhimei Li , Yaowu Zhang

引用次数: 0

Semiparametric estimation of a principal functional coefficient panel data model with cross-sectional dependence and its application to cigarette demand 具有横截面依赖性的主函数系数面板数据模型的半参数估计及其在卷烟需求中的应用

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2024-10-05 DOI: 10.1016/j.jspi.2024.106244

Yan-Yong Zhao , Ling-Ling Ge , Kong-Sheng Zhang

{"title":"Semiparametric estimation of a principal functional coefficient panel data model with cross-sectional dependence and its application to cigarette demand","authors":"Yan-Yong Zhao , Ling-Ling Ge , Kong-Sheng Zhang","doi":"10.1016/j.jspi.2024.106244","DOIUrl":"10.1016/j.jspi.2024.106244","url":null,"abstract":"<div><div>In this paper, we consider the estimation of functional coefficient panel data models with cross-sectional dependence. Borrowing the principal component structure, the functional coefficient panel data models can be transformed into a semiparametric panel data model. Combining the local linear dummy variable technique and profile least squares method, we develop a semiparametric profile method to estimate the coefficient functions. A gradient-descent iterative algorithm is employed to enhance computation speed and estimation accuracy. The main results show that the resulting parameter estimator enjoys asymptotic normality with a <span><math><msqrt><mrow><mi>N</mi><mi>T</mi></mrow></msqrt></math></span> convergence rate and the nonparametric estimator is asymptotically normal with a nonparametric convergence rate <span><math><msqrt><mrow><mi>N</mi><mi>T</mi><mi>h</mi></mrow></msqrt></math></span> when both the number of cross-sectional units <span><math><mi>N</mi></math></span> and the length of time series <span><math><mi>T</mi></math></span> go to infinity, under some regularity conditions. Monte Carlo simulations are carried out to evaluate the proposed methods, and an application to cigarette demand is investigated for illustration.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"236 ","pages":"Article 106244"},"PeriodicalIF":0.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416590","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