Journal of the American Statistical Association

最新文章

• Robust estimation of mixture complexity AI翻译
  作者：Woo, Mi-Ja; Sriram, T. N.
  作者单位：University System of Georgia; University of Georgia
  摘要：In many applications, it is important to find the mixture with fewest number of components, known as the mixture complexity, that provides a satisfactory fit to the data. This article focuses on developing an estimator of mixture complexity that is consistent when the form of component densities are unknown but are postulated to be members of some parametric family and is simultaneously robust against model misspecification. We treat the estimation of mixture complexity as a model selection pr...
• Bayesian-optimal design via interacting particle systems AI翻译
  作者：Amzal, Billy; Bois, Frederic Y.; Parent, Eric; Robert, Christian R.
  作者单位：Universite PSL; Universite Paris-Dauphine
  摘要：We propose a new stochastic algorithm for Bayesian-optimal design in nonlinear and high-dimensional contexts. Following Peter Muller, we solve an optimization problem by exploring the expected utility surface through Markov chain Monte Carlo simulations. The optimal design is the mode of this surface considered a probability distribution. Our algorithm relies on a particle method to efficiently explore high-dimensional multimodal surfaces, with simulated annealing to concentrate the samples ne...
• Inference in semiparametric dynamic models for binary longitudinal data AI翻译
  作者：Chib, Siddhartha; Jeliazkov, Ivan
  作者单位：Washington University (WUSTL); University of California System; University of California Irvine
  摘要：This article deals with the analysis of a hierarchical sermparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N-3), where N is the total number of observations in the sample. Therefore, nonparametric estimation is largely infeasible when the sample size is large, as in typical in the longitudinal setti...
• Likelihood-based inference on haplotype effects in genetic association studies - Comment AI翻译
  作者：Chatterjee, N; Spinka, C; Chen, JB; Carroll, RJ
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; University of Missouri System; University of Missouri Columbia; Texas A&M University System; Texas A&M University College Station
• Estimation of expression indexes for oligonucleotide arrays using the singular value decomposition AI翻译
  作者：Hu, JH; Wright, FA; Zou, F
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Multiprobe oligonucleotide arrays are a widely used type of expression microarray with the attractive feature that numerous probes are used to represent each transcript. An expression index is a statistic used to represent expression level for a particular gene that is estimated from the probe hybridization intensities. We show that a popular model-based expression index proposed by Li and Wong has an interpretation as a component of the singular value decomposition (SVD) of the probe intensit...
• Optimal and efficient crossover designs when subject effects are random AI翻译
  作者：Hedayat, A. S.; Stufken, John; Yang, Min
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University System of Georgia; University of Georgia; University of Missouri System; University of Missouri Columbia
  摘要：Most studies on optimal crossover designs are based on models that assume subject effects to be fixed effects. In this article we identify and study optimal and efficient designs for a model with random subject effects. With the number of periods not exceeding the number of treatments, we find that totally balanced designs are universally optimal for treatment effects in a large subclass of competing designs. However, in the entire class of designs, totally balanced designs are in general not ...
• Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data AI翻译
  作者：Hong, Guanglei; Raudenbush, Stephen W.
  作者单位：University of Toronto; University Health Network Toronto; University of Chicago
  摘要：This article considers the policy of retaining low-achieving children in kindergarten rather than promoting them to first grade. Under the stable unit treatment value assumption (SUTVA) as articulated by Rubin, each child at risk of retention has two potential outcomes: Y(1) if retained and Y(0) if promoted. But SUTVA is questionable, because a child's potential outcomes will plausibly depend on which school that child attends and also on treatment assignments of other children. We develop a c...
• Random forests and adaptive nearest neighbors AI翻译
  作者：Lin, Yi; Jeon, Yongho
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：In this article we study random forests through their connection with a new framework of adaptive nearest-neighbor methods. We introduce a concept of potential nearest neighbors (k-PNNs) and show that random forests can be viewed as adaptively weighted k-PNN methods. Various aspects of random forests can be studied from this perspective. We study the effect of terminal node sizes on the prediction accuracy of random forests. We further show that random forests with adaptive splitting schemes a...
• Non-gaussian Bayesian geostatistical modeling AI翻译
  作者：Palacios, M. Blanca; Steel, Mark F. J.
  作者单位：University of Basque Country; University of Warwick
  摘要：Sampling models for geostatistical data are usually based on Gaussian processes. However, real data often display non-Gaussian features, such as heavy tails. In this article we propose a more flexible class of sampling models. We start from the spatial linear model that has a spatial trend plus a stationary Gaussian error process. We extend the sampling model to non-Gaussianity by including a scale parameter at each location. We make sure that we obtain a valid stochastic process. The scale pa...
• Large-sample joint posterior approximations when full conditionals are approximately normal: Application to generalized linear mixed models AI翻译
  作者：Su, Chun-Lung; Johnson, Wesley O.
  作者单位：University of California System; University of California Davis; University of California System; University of California Irvine
  摘要：Modern Bayesian statistical methods, such as Gibbs and Metropolis-Hastings sampling, were developed to liberate statisticians from the necessity of making large-sample assumptions and to facilitate the numerical approximation of problems that had previously been analytically intractable. Counter to this trend, we develop a method for constructing asymptotic joint posterior approximations based on models with k blocks of parameters and where the corresponding properly normalized full conditiona...