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作者：Benjamini, Yoav; Madar, Vered; Stark, Philip B.
作者单位：Tel Aviv University; University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Berkeley
摘要：Many studies draw inferences about multiple endpoints but ignore the statistical implications of multiplicity. Effects inferred to be positive when there is no adjustment for multiplicity can lose their statistical significance when multiplicity is taken into account, perhaps explaining why such adjustments are so often omitted. We develop new simultaneous confidence intervals that mitigate this problem; these are uniformly more likely to determine signs than are standard simultaneous confiden...
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作者：Titterington, D. M.
作者单位：University of Glasgow
摘要：Highlights, trends and influences are identified associated with the pages of Biometrika subsequent to the editorship of Karl Pearson.
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作者：Han, Peisong; Wang, Lu
作者单位：University of Michigan System; University of Michigan
摘要：We propose an estimator that is more robust than doubly robust estimators, based on weighting complete cases using weights other than inverse probability when estimating the population mean of a response variable subject to ignorable missingness. We allow multiple models for both the propensity score and the outcome regression. Our estimator is consistent if any of the multiple models is correctly specified. Such multiple robustness against model misspecification is a significant improvement o...
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作者：Polson, N. G.; Scott, J. G.
作者单位：University of Chicago; University of Texas System; University of Texas Austin
摘要：We use the theory of normal variance-mean mixtures to derive a data-augmentation scheme for a class of common regularization problems. This generalizes existing theory on normal variance mixtures for priors in regression and classification. It also allows variants of the expectation-maximization algorithm to be brought to bear on a wider range of models than previously appreciated. We demonstrate the method on several examples, focusing on the case of binary logistic regression. We also show t...
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作者：Dombry, C.; Eyi-Minko, F.; Ribatet, M.
作者单位：Universite de Poitiers; Universite de Montpellier
摘要：Since many environmental processes are spatial in extent, a single extreme event may affect several locations, and the spatial dependence must be taken into account in an appropriate way. This paper proposes a framework for conditional simulation of max-stable processes and gives closed forms for the regular conditional distributions of Brown-Resnick and Schlather processes. We test the method on simulated data and present applications to extreme rainfall around Zurich and extreme temperatures...
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作者：Chen, Kun; Dong, Hongbo; Chan, Kung-Sik
作者单位：University of Connecticut; University of Wisconsin System; University of Wisconsin Madison; University of Iowa
摘要：We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally nonconvex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed nonconvex penalized regression method has a global optimal ...
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作者：Liu, Xiaoxi; Zeng, Donglin
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We study variable selection in general transformation models for right-censored data. The models studied can incorporate external time-varying covariates, and they include the proportional hazards model and the proportional odds model as special cases. We propose an estimation method that involves minimizing a weighted negative partial loglikelihood function plus an adaptive lasso penalty, with the initial values obtained from nonparametric maximum likelihood estimation. The objective function...
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作者：Titterington, D. M.
作者单位：University of Glasgow
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作者：Roverato, A.; Lupparelli, M.; La Rocca, L.
作者单位：University of Bologna; Universita di Modena e Reggio Emilia
摘要：This paper introduces a novel class of models for binary data, which we call log-mean linear models. They are specified by linear constraints on the log-mean linear parameter, defined as a log-linear expansion of the mean parameter of the multivariate Bernoulli distribution. We show that marginal independence relationships between variables can be specified by setting certain log-mean linear interactions to zero and, more specifically, that graphical models of marginal independence are log-mea...
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作者：Susko, Edward
作者单位：Dalhousie University
摘要：When the null hypothesis constrains parameters to the boundary of the parameter space, the asymptotic null distribution of the likelihood ratio statistic is often a mixture of chi-squared distributions, giving rise to the so-called chi-bar test, where weights can depend on the true unknown parameter and be difficult to calculate. We consider the test that conditions on the observed number of null hypothesis parameters in the interior of the parameter space. This approach uses simple chi-square...
