2期

最新文章

• Logistic regression for spatial Gibbs point processes AI翻译
  作者：Baddeley, Adrian; Coeurjolly, Jean-Francois; Rubak, Ege; Waagepetersen, Rasmus
  作者单位：University of Western Australia; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Aalborg University
  摘要：We propose a computationally efficient technique, based on logistic regression, for fitting Gibbs point process models to spatial point pattern data. The score of the logistic regression is an unbiased estimating function and is closely related to the pseudolikelihood score. Implementation of our technique does not require numerical quadrature, and thus avoids a source of bias inherent in other methods. For stationary processes, we prove that the parameter estimator is strongly consistent and ...
• Distances and inference for covariance operators AI翻译
  作者：Pigoli, Davide; Aston, John A. D.; Dryden, Ian L.; Secchi, Piercesare
  作者单位：University of Warwick; University of Cambridge; University of Nottingham; Polytechnic University of Milan
  摘要：A framework is developed for inference concerning the covariance operator of a functional random process, where the covariance operator itself is an object of interest for statistical analysis. Distances for comparing positive-definite covariance matrices are either extended or shown to be inapplicable to functional data. In particular, an infinite-dimensional analogue of the Procrustes size-and-shape distance is developed. Convergence of finite-dimensional approximations to the infinite-dimen...
• Permuting regular fractional factorial designs for screening quantitative factors AI翻译
  作者：Tang, Yu; Xu, Hongquan
  作者单位：Soochow University - China; University of California System; University of California Los Angeles
  摘要：Fractional factorial designs arewidely used in screening experiments. They are often chosen by the minimum aberration criterion, which regards factor levels as symbols. For designs with quantitative factors, however, permuting the levels for one or more factors could alter their geometrical structures and statistical properties. We provide a justification of the minimum beta-aberration criterion for quantitative factors and study level permutations for regular fractional factorial designs in o...
• A combined estimating function approach for fitting stationary point process models AI翻译
  作者：Deng, C.; Waagepetersen, R. P.; Guan, Y.
  作者单位：Yale University; Aalborg University; University of Miami
  摘要：A composite likelihood technique based on pairwise contributions provides a computationally simple but potentially inefficient approach for fitting spatial point process models. We propose a new estimation procedure that improves the efficiency. Our approach combines estimating functions derived from pairwise composite likelihood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial poin...
• Sequential combination of weighted and nonparametric bagging for classification AI翻译
  作者：Soleymani, M.; Lee, S. M. S.
  作者单位：University of Hong Kong
  摘要：We propose a simple sequential procedure for bagged classification, which modifies nonparametric bagging by randomizing class labels of resampled data points. The random labelling feature of the procedure also enables us to undertake unsupervised classification with the benefit of supervised learning. Theoretical properties are given for the nearest neighbour classifier in the case of supervised learning and a hard-thresholding indicator in the case of unsupervised learning, showing that seque...
• Measurement bias and effect restoration in causal inference AI翻译
  作者：Kuroki, Manabu; Pearl, Judea
  作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of California System; University of California Los Angeles
  摘要：This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, it discusses the control of unmeasured confounders in parametric and nonparametric models and the computational problem of obtaining bias-free effect estimates in such models. We derive new conditions under which causal effects can be restored by observing proxy variables of unmeasured confounders with/without external studies.
• Bayes and empirical Bayes: do they merge? AI翻译
  作者：Petrone, S.; Rousseau, J.; Scricciolo, C.
  作者单位：Bocconi University; Institut Polytechnique de Paris; ENSAE Paris
  摘要：Bayesian inference is attractive due to its internal coherence and for often having good frequentist properties. However, eliciting an honest prior may be difficult, and common practice is to take an empirical Bayes approach using an estimate of the prior hyperparameters. Although not rigorous, the underlying idea is that, for a sufficiently large sample size, empirical Bayes methods should lead to similar inferential answers as a proper Bayesian inference. However, precise mathematical result...
• Direct estimation of differential networks AI翻译
  作者：Zhao, Sihai Dave; Cai, T. Tony; Li, Hongzhe
  作者单位：University of Pennsylvania; University of Pennsylvania
  摘要：It is often of interest to understand how the structure of a genetic network differs between two conditions. In this paper, each condition-specific network is modelled using the precision matrix of a multivariate normal random vector, and a method is proposed to directly estimate the difference of the precision matrices. In contrast to other approaches, such as separate or joint estimation of the individual matrices, direct estimation does not require those matrices to be sparse, and thus can ...
• Convergence of sample eigenvalues, eigenvectors, and principal component scores for ultra-high dimensional data AI翻译
  作者：Lee, Seunggeun; Zou, Fei; Wright, Fred A.
  作者单位：University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University
  摘要：The development of high-throughput biomedical technologies has led to increased interest in the analysis of high-dimensional data where the number of features is much larger than the sample size. In this paper, we investigate principal component analysis under the ultra-high dimensional regime, where both the number of features and the sample size increase as the ratio of the two quantities also increases. We bridge the existing results from the finite and the high-dimension low sample size re...
• Locally φp-optimal designs for generalized linear models with a single-variable quadratic polynomial predictor AI翻译
  作者：Wu, Hsin-Ping; Stufken, John
  作者单位：University System of Georgia; University of Georgia
  摘要：Finding optimal designs for generalized linear models is a challenging problem. Recent research has identified the structure of optimal designs for generalized linear models with single or multiple unrelated explanatory variables that appear as first-order terms in the predictor. We consider generalized linear models with a single-variable quadratic polynomial as the predictor under a popular family of optimality criteria. When the design region is unrestricted, our results establish that opti...