{"title":"Feature screening for case-cohort studies with failure time outcome.","authors":"Jing Zhang, Haibo Zhou, Yanyan Liu, Jianwen Cai","doi":"10.1111/sjos.12503","DOIUrl":"https://doi.org/10.1111/sjos.12503","url":null,"abstract":"<p><p>Case-cohort design has been demonstrated to be an economical and efficient approach in large cohort studies when the measurement of some covariates on all individuals is expensive. Various methods have been proposed for case-cohort data when the dimension of covariates is smaller than sample size. However, limited work has been done for high-dimensional case-cohort data which are frequently collected in large epidemiological studies. In this paper, we propose a variable screening method for ultrahigh-dimensional case-cohort data under the framework of proportional model, which allows the covariate dimension increases with sample size at exponential rate. Our procedure enjoys the sure screening property and the ranking consistency under some mild regularity conditions. We further extend this method to an iterative version to handle the scenarios where some covariates are jointly important but are marginally unrelated or weakly correlated to the response. The finite sample performance of the proposed procedure is evaluated via both simulation studies and an application to a real data from the breast cancer study.</p>","PeriodicalId":520775,"journal":{"name":"Scandinavian journal of statistics, theory and applications","volume":" ","pages":"349-370"},"PeriodicalIF":1.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/sjos.12503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40590326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal Estimator for Logistic Model with Distribution-free Random Intercept.","authors":"Tanya P Garcia, Yanyuan Ma","doi":"10.1111/sjos.12170","DOIUrl":"https://doi.org/10.1111/sjos.12170","url":null,"abstract":"<p><p>Logistic models with a random intercept are prevalent in medical and social research where clustered and longitudinal data are often collected. Traditionally, the random intercept in these models is assumed to follow some parametric distribution such as the normal distribution. However, such an assumption inevitably raises concerns about model misspecification and misleading inference conclusions, especially when there is dependence between the random intercept and model covariates. To protect against such issues, we use a semiparametric approach to develop a computationally simple and consistent estimator where the random intercept is distribution-free. The estimator is revealed to be optimal and achieve the efficiency bound without the need to postulate or estimate any latent variable distributions. We further characterize other general mixed models where such an optimal estimator exists.</p>","PeriodicalId":520775,"journal":{"name":"Scandinavian journal of statistics, theory and applications","volume":" ","pages":"156-171"},"PeriodicalIF":1.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/sjos.12170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40391800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}