发布求助
文献互助 智能选刊 最新文献

Statistics and Its Interface最新文献

筛选
英文 中文
Variable selection for doubly robust causal inference. 双稳健因果推理的变量选择。
IF 0.7 4区 数学
Statistics and Its Interface Pub Date : 2025-01-01 Epub Date: 2024-10-22 DOI: 10.4310/sii.241023040813
Eunah Cho, Shu Yang
{"title":"Variable selection for doubly robust causal inference.","authors":"Eunah Cho, Shu Yang","doi":"10.4310/sii.241023040813","DOIUrl":"10.4310/sii.241023040813","url":null,"abstract":"<p><p>Confounding control is crucial and yet challenging for causal inference based on observational studies. Under the typical unconfoundness assumption, augmented inverse probability weighting (AIPW) has been popular for estimating the average causal effect (ACE) due to its double robustness in the sense it relies on either the propensity score model or the outcome mean model to be correctly specified. To ensure the key assumption holds, the effort is often made to collect a sufficiently rich set of pretreatment variables, rendering variable selection imperative. It is well known that variable selection for the propensity score targeted for accurate prediction may produce a variable ACE estimator by including the instrument variables. Thus, many recent works recommend selecting all outcome predictors for both confounding control and efficient estimation. This article shows that the AIPW estimator with variable selection targeted for efficient estimation may lose the desirable double robustness property. Instead, we propose controlling the propensity score model for any covariate that is a predictor of either the treatment or the outcome or both, which preserves the double robustness of the AIPW estimator. Using this principle, we propose a two-stage procedure with penalization for variable selection and the AIPW estimator for estimation. We show the proposed procedure benefits from the desirable double robustness property. We evaluate the finite-sample performance of the AIPW estimator with various variable selection criteria through simulation and an application.</p>","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"18 1","pages":"93-105"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Composite quantile regression based robust empirical likelihood for partially linear spatial autoregressive models 部分线性空间自回归模型的基于稳健经验似然法的复合量化回归
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii764
Peixin Zhao, Suli Cheng, Xiaoshuang Zhou
{"title":"Composite quantile regression based robust empirical likelihood for partially linear spatial autoregressive models","authors":"Peixin Zhao, Suli Cheng, Xiaoshuang Zhou","doi":"10.4310/22-sii764","DOIUrl":"https://doi.org/10.4310/22-sii764","url":null,"abstract":"In this paper, we consider the robust estimation for a class of partially linear spatial autoregressive models. By combining empirical likelihood and composite quantile regression methods, we propose a robust empirical likelihood estimation procedure. Under some regularity conditions, the proposed empirical log-likelihood ratio is proved to be asymptotically chi-squared, and the convergence rate of the estimator for nonparametric component is also derived. Some simulation analyses are conducted for further illustrating the performance of the proposed method, and simulation results show that the proposed method is more robust.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"6 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743418","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 consistent specification test for functional linear quantile regression models 功能线性量回归模型的一致性规范检验
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii754
Lili Xia, Zhongzhan Zhang, Gongming Shi
{"title":"A consistent specification test for functional linear quantile regression models","authors":"Lili Xia, Zhongzhan Zhang, Gongming Shi","doi":"10.4310/22-sii754","DOIUrl":"https://doi.org/10.4310/22-sii754","url":null,"abstract":"This paper is focused on the specification test of functional linear quantile regression models. A nonparametric test statistic is proposed based on the orthogonality of residual and its conditional expectation. It is proved with mild assumptions that the proposed statistic follows asymptotically the standard normal distribution under the null hypothesis, but tends to infinity under alternative hypothesis. The asymptotic power of the test is also presented for some local alternative hypotheses. The test is easy to implement, and is shown by simulations powerful even for small sample sizes. A real data example with the Capital Bikeshare data is presented for illustration.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"66 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743411","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 latent class selection model for categorical response variables with nonignorably missing data 具有非明显缺失数据的分类响应变量的潜类选择模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii753
Jung Wun Lee, Ofer Harel
{"title":"A latent class selection model for categorical response variables with nonignorably missing data","authors":"Jung Wun Lee, Ofer Harel","doi":"10.4310/22-sii753","DOIUrl":"https://doi.org/10.4310/22-sii753","url":null,"abstract":"We develop a new selection model for nonignorable missing values in multivariate categorical response variables by assuming that the response variables and their missingness can be summarized into categorical latent variables. Our proposed model contains two categorical latent variables. One latent variable summarizes the response patterns while the other describes the response variables’ missingness. Our selection model is an alternative method to other incomplete data methods when the incomplete data mechanism is nonignorable. We implement simulation studies to evaluate the performance of the proposed method and analyze the General Social Survey 2018 data to demonstrate its performance.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"56 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743414","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
Empirical likelihood-based weighted estimation of average treatment effects in randomized clinical trials with missing outcomes 在结果缺失的随机临床试验中，基于经验似然法对平均治疗效果进行加权估计
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/sii.2024.v17.n4.a7
Yuanyao Tan, Xialing Wen, Wei Liang, Ying Yan
{"title":"Empirical likelihood-based weighted estimation of average treatment effects in randomized clinical trials with missing outcomes","authors":"Yuanyao Tan, Xialing Wen, Wei Liang, Ying Yan","doi":"10.4310/sii.2024.v17.n4.a7","DOIUrl":"https://doi.org/10.4310/sii.2024.v17.n4.a7","url":null,"abstract":"There has been growing attention on covariate adjustment for treatment effect estimation in an objective and efficient manner in randomized clinical trials. In this paper, we propose a weighting approach to extract covariate information based on the empirical likelihood method for the randomized clinical trials with possible missingness in the outcomes. Multiple regression models are imposed to delineate the missing data mechanism and the covariate-outcome relationship, respectively. We demonstrate that the proposed estimator is suitable for objective inference of treatment effects. Theoretically, we prove that the proposed approach is multiply robust and semiparametrically efficient. We conduct simulations and a real data study to make comparisons with other existing methods.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"40 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743415","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
Modeling and identifiability of non-homogenous Poisson process cure rate model 非均质泊松过程治愈率模型的建模和可识别性
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii763
Soorya Surendren, Asha Gopalakrishnan, Anup Dewanji
{"title":"Modeling and identifiability of non-homogenous Poisson process cure rate model","authors":"Soorya Surendren, Asha Gopalakrishnan, Anup Dewanji","doi":"10.4310/22-sii763","DOIUrl":"https://doi.org/10.4310/22-sii763","url":null,"abstract":"The promotion time cure models or bounded cumulative hazards model (BCH) was proposed as an alternative to the mixture cure models. In the present paper, this model is modified to provide a class of cure rate models based on a non-homogeneous Poisson process (NHPP). The properties of this class are studied. Also, when censored observations are present, distinguishing censored individuals from the cured group lead to identifiability issues in the members of this class. These identifiability issues are investigated and finally few members of this class are provided. Simulation results using an example of the NHPP cure rate model with exponentiated intensity and exponential baseline is supplemented. The application of the model is illustrated using E1684 real data from a study that included 284 patients from the Eastern Cooperative Oncology Group (ECOG) phase III clinical trial.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"48 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743417","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
Variable selection and estimation for high-dimensional partially linear spatial autoregressive models with measurement errors 具有测量误差的高维部分线性空间自回归模型的变量选择和估计
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii758
Zhensheng Huang, Shuyu Meng, Linlin Zhang
{"title":"Variable selection and estimation for high-dimensional partially linear spatial autoregressive models with measurement errors","authors":"Zhensheng Huang, Shuyu Meng, Linlin Zhang","doi":"10.4310/22-sii758","DOIUrl":"https://doi.org/10.4310/22-sii758","url":null,"abstract":"In this paper, we develop a class of corrected post-model selection estimation method to identify important explanatory variables in parametric component of high-dimensional partially linear spatial autoregressive model with measurement errors. Compared with existing methods, the proposed method adds a new process of re-estimating the selected model parameters after model selection. We show that the post-model selection estimator performs at least as well as the Lasso penalty estimator by establishing some theorems of model selection and estimation properties. Extensive simulation studies not only evaluate the finite sample performance of the proposed method, but also show the superiority of the proposed method over other methods. As an empirical illustration, we apply the proposed model and method to two real data sets.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"90 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743412","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
Flexible quasi-beta prime regression models for dependent continuous positive data 针对依赖性连续正数据的灵活准贝塔质回归模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii762
João Freitas, Juvêncio Nobre, Caio Azevedo
{"title":"Flexible quasi-beta prime regression models for dependent continuous positive data","authors":"João Freitas, Juvêncio Nobre, Caio Azevedo","doi":"10.4310/22-sii762","DOIUrl":"https://doi.org/10.4310/22-sii762","url":null,"abstract":"In many situations of interest, it is common to observe positive responses measured along several assessment conditions, within the same subjects. Usually, such a scenario implies a positive skewness on the response distributions, along with the existence of within-subject dependency. It is known that neglecting these features can lead to a misleading inference. In this paper we extend the beta prime regression model for modeling asymmetric positive data, while taking into account the dependence structure. We consider a useful predictor for modeling a suitable transformation of the mean, along with homogeneous covariance structure. The proposed model is an interesting competitor of the flexible Tweedie regression models, which include distributions such as Gamma and Inverse Gaussian. Furthermore, residual analysis and influence diagnostic tools are proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed methodology, under small and moderate sample sizes, along with suitable discussions. The methodology is illustrated with the analysis of a real longitudinal dataset. An R package was developed to allow the practitioners to use the methodology described in this paper.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"67 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743416","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 double regression method for graphical modeling of high-dimensional nonlinear and non-Gaussian data 高维非线性和非高斯数据图形建模的双重回归方法
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii756
Siqi Liang, Faming Liang
{"title":"A double regression method for graphical modeling of high-dimensional nonlinear and non-Gaussian data","authors":"Siqi Liang, Faming Liang","doi":"10.4310/22-sii756","DOIUrl":"https://doi.org/10.4310/22-sii756","url":null,"abstract":"Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are Gaussian or mixed and the variables are linearly dependent. In this paper, we propose a double regression method for learning graphical models under the high-dimensional nonlinear and non-Gaussian setting, and prove that the proposed method is consistent under mild conditions. The proposed method works by performing a series of nonparametric conditional independence tests. The conditioning set of each test is reduced via a double regression procedure where a model-free sure independence screening procedure or a sparse deep neural network can be employed. The numerical results indicate that the proposed method works well for high-dimensional nonlinear and non-Gaussian data.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"63 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743413","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
Default Bayesian testing for the zero-inflated Poisson distribution 零膨胀泊松分布的默认贝叶斯测试
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii750
Yewon Han, Haewon Hwang, Hon Keung Ng, Seong Kim
{"title":"Default Bayesian testing for the zero-inflated Poisson distribution","authors":"Yewon Han, Haewon Hwang, Hon Keung Ng, Seong Kim","doi":"10.4310/22-sii750","DOIUrl":"https://doi.org/10.4310/22-sii750","url":null,"abstract":"In a Bayesian model selection and hypothesis testing, users should be cautious when choosing suitable prior distributions, as it is an important problem. More often than not, objective Bayesian analyses utilize noninformative priors such as Jeffreys priors. However, since these noninformative priors are often improper, the Bayes factor associated with these improper priors is not well-defined. To circumvent this indeterminate issue, the Bayes factor can be corrected by intrinsic and fractional methods. These adjusted Bayes factors are asymptotically equivalent to the ordinary Bayes factors calculated with proper priors, called intrinsic priors. In this article, we derive intrinsic priors for testing the point null hypothesis under a zero-inflated Poisson distribution. Extensive simulation studies are performed to support the theoretical results on asymptotic equivalence, and two real datasets are analyzed to illustrate the methodology developed in this paper.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"5 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743410","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信