Biometrika

最新文章

• Generalized Ewens-Pitman model for Bayesian clustering AI翻译
  作者：Crane, Harry
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：We propose a Bayesian method for clustering from discrete data structures that commonly arise in genetics and other applications. This method is equivariant with respect to relabelling units; unsampled units do not interfere with sampled data; and missing data do not hinder inference. Cluster inference using the posterior mode performs well on simulated and real datasets, and the posterior predictive distribution enables supervised learning based on a partial clustering of the sample.
• Shared kernel Bayesian screening AI翻译
  作者：Lock, Eric F.; Dunson, David B.
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Duke University
  摘要：This article concerns testing for equality of distribution between groups. We focus on screening variables with shared distributional features such as common support, modes and patterns of skewness. We propose a Bayesian testing method using kernel mixtures, which improves performance by borrowing information across the different variables and groups through shared kernels and a common probability of group differences. The inclusion of shared kernels in a finite mixture, with Dirichlet priors ...
• Tree-based methods for individualized treatment regimes AI翻译
  作者：Laber, E. B.; Zhao, Y. Q.
  作者单位：North Carolina State University; University of Wisconsin System; University of Wisconsin Madison
  摘要：Individualized treatment rules recommend treatments on the basis of individual patient characteristics. A high-quality treatment rule can produce better patient outcomes, lower costs and less treatment burden. If a treatment rule learned from data is to be used to inform clinical practice or provide scientific insight, it is crucial that it be interpretable; clinicians may be unwilling to implement models they do not understand, and black-box models may not be useful for guiding future researc...
• Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems AI翻译
  作者：Belloni, A.; Chernozhukov, V.; Kato, K.
  作者单位：Duke University; Massachusetts Institute of Technology (MIT); University of Tokyo
  摘要：We develop uniformly valid confidence regions for regression coefficients in a high-dimensional sparse median regression model with homoscedastic errors. Our methods are based on a moment equation that is immunized against nonregular estimation of the nuisance part of the median regression function by using Neyman's orthogonalization. We establish that the resulting instrumental median regression estimator of a target regression coefficient is asymptotically normally distributed uniformly with...
• Optimal multiple testing under a Gaussian prior on the effect sizes AI翻译
  作者：Dobriban, Edgar; Fortney, Kristen; Kim, Stuart K.; Owen, Art B.
  作者单位：Stanford University; Stanford University
  摘要：We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal p-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly....
• A new specification of generalized linear models for categorical responses AI翻译
  作者：Peyhardi, J.; Trottier, C.; Guedon, Y.
  作者单位：Universite de Montpellier; Universite de Montpellier; Universite Paul-Valery
  摘要：Many regression models for categorical responses have been introduced, motivated by different paradigms, but it is difficult to compare them because of their different specifications. In this paper we propose a unified specification of regression models for categorical responses, based on a decomposition of the link function into an inverse continuous cumulative distribution function and a ratio of probabilities. This allows us to define a new family of reference models for nominal responses, ...
• Robust estimation under heavy contamination using unnormalized models AI翻译
  作者：Kanamori, Takafumi; Fujisawa, Hironori
  作者单位：Nagoya University; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
  摘要：Contamination caused by outliers is inevitable in data analysis, and robust statistical methods are often needed. In this paper we develop a new approach for robust data analysis on the basis of scoring rules. A scoring rule is a discrepancy measure to assess the quality of probabilistic forecasts. We propose a simple method of estimating not only parameters in the statistical model but also the contamination ratio, i.e., the ratio of outliers. The outliers are detected based on the estimated ...
• Designing dose-finding studies with an active control for exponential families AI翻译
  作者：Dette, H.; Kettelhake, K.; Bretz, F.
  作者单位：Ruhr University Bochum; Novartis
  摘要：Optimal design of dose-finding studies with an active control has only been considered in the literature for regression models with normally distributed errors and known variances, where the focus is on estimating the smallest dose that achieves the same treatment effect as the active control. This paper discusses such dose-finding studies from a broader perspective. We consider a general class of optimality criteria and models arising from an exponential family. Optimal designs are constructe...
• Order selection in finite mixture models: complete or observed likelihood information criteria? AI翻译
  作者：Hui, Francis K. C.; Warton, David I.; Foster, Scott D.
  作者单位：University of New South Wales Sydney; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  摘要：Choosing the number of components in a finite mixture model is a challenging task. In this article, we study the behaviour of information criteria for selecting the mixture order, based on either the observed likelihood or the complete likelihood including component labels. We propose a new observed likelihood criterion called aic(mix), which is shown to be order consistent. We further show that when there is a nontrivial level of classification uncertainty in the true model, complete likeliho...
• A cautionary note on robust covariance plug-in methods AI翻译
  作者：Nordhausen, Klaus; Tyler, David E.
  作者单位：University of Turku; Rutgers University System; Rutgers University New Brunswick
  摘要：The sample covariance matrix, which is well known to be highly nonrobust, plays a central role in many classical multivariate statistical methods. A popular way of making such multivariate methods more robust is to replace the sample covariance matrix with some robust scatter matrix. The aim of this paper is to point out that multivariate methods often require that certain properties of the covariance matrix hold also for the robust scatter matrix in order for the corresponding robust plug-in ...