Journal of the American Statistical Association

最新文章

• Efficient Estimation of the Cox Model with Auxiliary Subgroup Survival Information AI翻译
  作者：Huang, Chiung-Yu; Qin, Jing; Tsai, Huei-Ting
  作者单位：Johns Hopkins University; Johns Hopkins Medicine
  摘要：With the rapidly increasing availability of data in the public domain, combining information from different sources to infer about associations or differences of interest has become an emerging challenge to researchers. This article presents a novel approach to improve efficiency in estimating the survival time distribution by synthesizing information from the individual-level data with t-year survival probabilities from external sources such as disease registries. While disease registries pro...
• An Adaptive Exchange Algorithm for Sampling From Distributions With Intractable Normalizing Constants AI翻译
  作者：Liang, Faming; Jin, Ick Hoon; Song, Qifan; Liu, Jun S.
  作者单位：State University System of Florida; University of Florida; University of Notre Dame; University System of Ohio; Ohio State University; Purdue University System; Purdue University; Harvard University
  摘要：Sampling from the posterior distribution for a model whose normalizing constant is intractable is a long-standing problem in statistical research. We propose a new algorithm, adaptive auxiliary variable exchange algorithm, or, in short, adaptive exchange (AEX) algorithm, to tackle this problem. The new algorithm can be viewed as a MCMC extension of the exchange algorithm, which generates auxiliary variables via an importance sampling procedure from a Markov chain running in parallel. The conve...
• Estimating Variance Components in Functional Linear Models With Applications to Genetic Heritability AI翻译
  作者：Reimherr, Matthew; Nicolae, Dan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Chicago
  摘要：Quantifying heritability is the first step in understanding the contribution of genetic variation to the risk architecture of complex human diseases and traits. Heritability can be estimated for univariate phenotypes from nonfamily data using linear mixed effects models. There is, however, no fully developed methodology for defining or estimating heritability from longitudinal studies. By examining longitudinal studies, researchers have the opportunity to better understand the genetic influenc...
• Multitask Quantile Regression Under the Transnormal Model AI翻译
  作者：Fan, Jianqing; Xue, Lingzhou; Zou, Hui
  作者单位：Princeton University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We consider estimating multitask quantile regression under the transnormal model, with focus on high dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based l(1), penalization with positive-definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of the altern...
• Identifiability of Normal and Normal Mixture Models with Nonignorable Missing Data AI翻译
  作者：Miao, Wang; Ding, Peng; Geng, Zhi
  作者单位：Peking University; University of California System; University of California Berkeley; Peking University; Peking University
  摘要：Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is, the missing mechanism depends on the missing values themselves even conditional on the observed data. With a nonignorable missing mechanism, the model of interest is often not identifiable without imposing further assumptions. We find that even if the missing mechanism has a known parametric form, the model is not i...
• Modeling Probability Forecasts via Information Diversity AI翻译
  作者：Satopaa, Ville A.; Pemantle, Robin; Ungar, Lyle H.
  作者单位：INSEAD Business School; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity stemming from people's cognitive or information diversity is often more important than measurement noise. This article presents a novel framework that uses partially overlapping information sources. A specific model is proposed within that framework and applied to the task of aggregating the probabilities given by a g...
• Conditional Sure Independence Screening AI翻译
  作者：Barut, Emre; Fan, Jianqing; Verhasselt, Anneleen
  作者单位：George Washington University; Princeton University; Fudan University; Hasselt University
  摘要：Independence screening is powerful for variable selection when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or its variants. When some prior knowledge on a certain important set of variables is available, a natural assessment on the relative importance of the other predictors is their conditional contributions to the response given the known set of variables. This results in conditional sure independence screening (CSIS). C...
• Fast Estimation of Regression Parameters in a Broken-Stick Model for Longitudinal Data AI翻译
  作者：Das, Ritabrata; Banerjee, Moulinath; Nan, Bin; Zheng, Huiyong
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this article, we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods that rely on sear...
• Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference AI翻译
  作者：Han, Sung Won; Chen, Gong; Cheon, Myun-Seok; Zhong, Hua
  作者单位：Korea University; University System of Georgia; Georgia Institute of Technology; New York University
  摘要：Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed, graphical models, where all the edges are directed edges and contain. no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the a...
• Convex Banding of the Covariance Matrix AI翻译
  作者：Bien, Jacob; Bunea, Florentina; Xiao, Luo
  作者单位：Cornell University
  摘要：We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator that tapers the sample covariance matrix by a Toeplitz, sparsely banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimator...