Biometrika

最新文章

• A theoretical study of Stein's covariance estimator AI翻译
  作者：Rajaratnam, Bala; Vincenzi, Dario
  作者单位：Stanford University; Universite Cote d'Azur; Centre National de la Recherche Scientifique (CNRS)
  摘要：Stein proposed an estimator to address the poor performance of the sample covariance matrix for samples of small size. The estimator does not impose sparsity conditions and uses an isotonizing algorithm to preserve the order of the sample eigenvalues. Despite its superior numerical performance, its theoretical properties are not well understood. We demonstrate that Stein's covariance estimator gives modest risk reductions when it is not isotonized, and when it is isotonized the risk reductions...
• A pairwise likelihood-based approach for changepoint detection in multivariate time series models AI翻译
  作者：Ma, Ting Fung; Yau, Chun Yip
  作者单位：Chinese University of Hong Kong
  摘要：This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algor...
• Stochastic mechanistic interaction AI翻译
  作者：Berzuini, Carlo; Dawid, A. Philip
  作者单位：University of Manchester; University of Cambridge
  摘要：We define mechanistic interaction between the effects of two variables on an outcome in terms of departure of these effects from a generalized noisy-OR model in a stratum of the population. We develop a fully probabilistic framework for the observational identification of this type of interaction via excess risk or superadditivity, one novel feature of which is its applicability when the interacting variables have been generated by arbitrarily dichotomizing continuous exposures. The method all...
• Modelling complex survey data with population level information: an empirical likelihood approach AI翻译
  作者：Oguz-Alper, M.; Berger, Y. G.
  作者单位：Statistics Norway; University of Southampton
  摘要：Survey data are often collected with unequal probabilities from a stratified population. In many modelling situations, the parameter of interest is a subset of a set of parameters, with the others treated as nuisance parameters. We show that in this situation the empirical likelihood ratio statistic follows a chi-squared distribution asymptotically, under stratified single and multi-stage unequal probability sampling, with negligible sampling fractions. Simulation studies show that the empiric...
• Partially functional linear regression in high dimensions AI翻译
  作者：Kong, Dehan; Xue, Kaijie; Yao, Fang; Zhang, Hao H.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Toronto; University of Arizona
  摘要：In modern experiments, functional and nonfunctional data are often encountered simultaneously when observations are sampled from random processes and high-dimensional scalar covariates. It is difficult to apply existing methods for model selection and estimation. We propose a new class of partially functional linear models to characterize the regression between a scalar response and covariates of both functional and scalar types. The new approach provides a unified and flexible framework that ...
• Quantile-based classifiers AI翻译
  作者：Hennig, C.; Viroli, C.
  作者单位：University of London; University College London; University of Bologna
  摘要：Classification with small samples of high-dimensional data is important in many application areas. Quantile classifiers are distance-based classifiers that require a single parameter, regardless of the dimension, and classify observations according to a sum of weighted componentwise distances of the components of an observation to the within-class quantiles. An optimal percentage for the quantiles can be chosen by minimizing the misclassification error in the training sample. It is shown that ...
• A differential-geometric approach to generalized linear models with grouped predictors AI翻译
  作者：Augugliaro, Luigi; Mineo, Angelo M.; Wit, Ernst C.
  作者单位：University of Palermo; University of Groningen
  摘要：We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a gr...
• Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression AI翻译
  作者：Su, Z.; Zhu, G.; Chen, X.; Yang, Y.
  作者单位：State University System of Florida; University of Florida; National University of Singapore; McGill University
  摘要：The envelope model allows efficient estimation in multivariate linear regression. In this paper, we propose the sparse envelope model, which is motivated by applications where some response variables are invariant with respect to changes of the predictors and have zero regression coefficients. The envelope estimator is consistent but not sparse, and in many situations it is important to identify the response variables for which the regression coefficients are zero. The sparse envelope model pe...
• Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out AI翻译
  作者：Han, Peisong
  作者单位：University of Waterloo
  摘要：Intrinsic efficiency and multiple robustness are desirable properties in missing data analysis. We establish both for estimating the mean of a response at the end of a longitudinal study with drop-out. The idea is to calibrate the estimated missingness probability at each visit using data from past visits. We consider one working model for the missingness probability and multiple working models for the data distribution. Intrinsic efficiency guarantees that, when the missingness probability is...
• On inverse probability-weighted estimators in the presence of interference AI翻译
  作者：Liu, L.; Hudgens, M. G.; Becker-Dreps, S.
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We consider inference about the causal effect of a treatment or exposure in the presence of interference, i.e., when one individual's treatment affects the outcome of another individual. In the observational setting where the treatment assignment mechanism is not known, inverse probability-weighted estimators have been proposed when individuals can be partitioned into groups such that there is no interference between individuals in different groups. Unfortunately this assumption, which is some...