Biometrika

最新文章

• Robust rank estimation for transformation models with random effects AI翻译
  作者：Lin, Yuanyuan; Luo, Yang; Xie, Shangyu; Chen, Kani
  作者单位：Chinese University of Hong Kong; Hong Kong University of Science & Technology; University of International Business & Economics
  摘要：Semiparametric transformation models with random effects are useful in analysing recurrent and clustered data. With specified error and random effect distributions, Zeng & Lin (2007a) proved that nonparametric maximum likelihood estimators are semiparametric efficient. In this paper we consider a more general class of transformation models with random effects, under which an unknown monotonic transformation of the response is linearly related to the covariates and the random effects with unspe...
• Testing for high-dimensional white noise using maximum cross-correlations AI翻译
  作者：Chang, Jinyuan; Yao, Qiwei; Zhou, Wen
  作者单位：Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science; Colorado State University System; Colorado State University Fort Collins
  摘要：We propose a new omnibus test for vector white noise using the maximum absolute auto-correlations and cross-correlations of the component series. Based on an approximation by the L-infinity-norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing departure from white noise that is not independent and identically dist...
• Distribution-free tests of independence in high dimensions AI翻译
  作者：Han, Fang; Chen, Shizhe; Liu, Han
  作者单位：University of Washington; University of Washington Seattle; Columbia University; Princeton University
  摘要：We consider the testing of mutual independence among all entries in a d-dimensional random vector based on n independent observations. We study two families of distribution-free test statistics, which include Kendall's tau and Spearman's rho as important examples. We show that under the null hypothesis the test statistics of these two families converge weakly to Gumbel distributions, and we propose tests that control the Type I error in the high-dimensional setting where d > n. We further show...
• Blocking strategies and stability of particle Gibbs samplers AI翻译
  作者：Singh, S. S.; Lindsten, F.; Moulines, E.
  作者单位：University of Cambridge; Uppsala University; Institut Polytechnique de Paris; Ecole Polytechnique; ENSTA Paris
  摘要：Sampling from the posterior probability distribution of the latent states of a hidden Markov model is nontrivial even in the context of Markov chain Monte Carlo. To address this, Andrieu et al. (2010) proposed a way of using a particle filter to construct a Markov kernel that leaves the posterior distribution invariant. Recent theoretical results have established the uniform ergodicity of this Markov kernel and shown that the mixing rate does not deteriorate provided the number of particles gr...
• On the Pitman-Yor process with spike and slab base measure AI翻译
  作者：Canale, A.; Lijoi, A.; Nipoti, B.; Prunster, I.
  作者单位：University of Padua; Bocconi University; Trinity College Dublin
  摘要：For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom i...
• Weighted envelope estimation to handle variability in model selection AI翻译
  作者：Eck, D. J.; Cook, R. D.
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：Envelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our ...
• Principal weighted support vector machines for sufficient dimension reduction in binary classification AI翻译
  作者：Shin, Seung Jun; Wu, Yichao; Zhang, Hao Helen; Liu, Yufeng
  作者单位：Korea University; North Carolina State University; University of Arizona; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are...
• Maximum empirical likelihood estimation for abundance in a closed population from capture-recapture data AI翻译
  作者：Liu, Yukun; Li, Pengfei; Qin, Jing
  作者单位：East China Normal University; University of Waterloo; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：Capture-recapture experiments are widely used to collect data needed for estimating the abundance of a closed population. To account for heterogeneity in the capture probabilities, Huggins (1989) and Alho (1990) proposed a semiparametric model in which the capture probabilities are modelled parametrically and the distribution of individual characteristics is left unspecified. A conditional likelihood method was then proposed to obtain point estimates andWald-type confidence intervals for the a...
• Automatic tagging with existing and novel tags AI翻译
  作者：Wang, Junhui; Shen, Xiaotong; Sun, Yiwen; Qu, Annie
  作者单位：City University of Hong Kong; University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Automatic tagging by key words and phrases is important in multi-label classification of a document. In this paper, we first introduce a tagging loss to measure the discrepancy between predicted and actual tag sets, which is expressed in terms of a sum of weighted pairwise margins between two tags by their degree of similarity. We then construct a regularized empirical loss to incorporate linguistic knowledge, and identify a tagger maximizing the separations between the pairwise margins. One s...
• On asymptotic validity of naive inference with an approximate likelihood AI翻译
  作者：Ogden, H. E.
  作者单位：University of Southampton
  摘要：Many statistical models have likelihoods which are intractable: it is impossible or too expensive to compute the likelihood exactly. In such settings, a common approach is to replace the likelihood with an approximation, and proceed with inference as if the approximate likelihood were the true likelihood. In this paper, we describe conditions which guarantee that such naive inference with an approximate likelihood has the same first-order asymptotic properties as inference with the true likeli...