-
作者：Reluga, Katarzyna; Lombardia, Maria-Jose; Sperlich, Stefan
作者单位：University of Cambridge; Universidade da Coruna; University of Geneva
摘要：Today, generalized linear mixed models (GLMM) are broadly used in many fields. However, the development of tools for performing simultaneous inference has been largely neglected in this domain. A framework for joint inference is indispensable to carry out statistically valid multiple comparisons of parameters of interest between all or several clusters. We therefore develop simultaneous confidence intervals and multiple testing procedures for empirical best predictors under GLMM. In addition, ...
-
作者：Masak, Tomas; Panaretos, Victor M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even though the assumption may fail in practice. We consider a setting where the covariance structure may fail to be separable locally-either due to noise contamination or due to the presence of a nonseparable short-range dependent signal componen...
-
作者：Tong, Zhaoxue; Cai, Zhanrui; Yang, Songshan; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Carnegie Mellon University; Renmin University of China
摘要：In this article, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built upon a new measure of conditional independence. Thus, the new method does not require a specific functional form of the regression function and is robust to heavy-tailed responses and predictors. The variables to be conditional on are allowed to be multivariate. The proposed method enjoys sure screening and ranking c...
-
作者：Scheike, Thomas H.; Martinussen, Torben; Ozenne, Brice
作者单位：University of Copenhagen
摘要：Direct regression for the cumulative incidence function (CIF) has become increasingly popular since the Fine and Gray model was suggested (Fine and Gray) due to its more direct interpretation on the probability risk scale. We here consider estimation within the Fine and Gray model using the theory of semiparametric efficient estimation. We show that the Fine and Gray estimator is semiparametrically efficient in the case without censoring. In the case of right-censored data, however, we show th...
-
作者：Dai, Xiaowu; Lyu, Xiang; Li, Lexin
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：Thanks to its fine balance between model flexibility and interpretability, the nonparametric additive model has been widely used, and variable selection for this type of model has been frequently studied. However, none of the existing solutions can control the false discovery rate (FDR) unless the sample size tends to infinity. The knockoff framework is a recent proposal that can address this issue, but few knockoff solutions are directly applicable to nonparametric models. In this article, we...
-
作者：Luo, Lan; Zhou, Ling; Song, Peter X-K
作者单位：University of Iowa; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of Michigan System; University of Michigan
摘要：This article develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare ...
-
作者：Chen, Yen-Chi
作者单位：University of Washington; University of Washington Seattle
摘要：We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood function. We derive the population quantity that is the target of this estimator and study the properties of confidence intervals (CIs) constructed from asymptotic normality and the bootstrap approach. In particular, we analyze the coverage deficiency due to finite number of random initializations. We also investigate the CIs by inverting th...
-
作者：Law, Michael; Ritov, Ya'acov
作者单位：University of Michigan System; University of Michigan
摘要：We consider three problems in high-dimensional linear mixed models. Without any assumptions on the design for the fixed effects, we construct asymptotic statistics for testing whether a collection of random effects is zero, derive an asymptotic confidence interval for a single random effect at the parametric rate root n, and propose an empirical Bayes estimator for a part of the mean vector in ANOVA type models that performs asymptotically as well as the oracle Bayes estimator. We support our ...
-
作者：Chiang, Harold D.; Kato, Kengo; Sasaki, Yuya
作者单位：University of Wisconsin System; University of Wisconsin Madison; Cornell University; Vanderbilt University
摘要：We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems over the rectangles and subsequently develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffidi...
-
作者：Ellenberg, Susan S.
作者单位：University of Pennsylvania
