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作者：Heinrich, C.
作者单位：Ruprecht Karls University Heidelberg
摘要：This article is concerned with point forecasting of a real-valued random variable with a general Lebesgue density. Answering a question of Gneiting (2011), it is shown that the mode is not elicitable, or, in other words, that it is impossible to find a loss or scoring function under which the mode is the Bayes predictor.
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作者：Zhang, Teng; Zou, Hui
作者单位：Princeton University; University of Minnesota System; University of Minnesota Twin Cities
摘要：We introduce a constrained empirical loss minimization framework for estimating high-dimensional sparse precision matrices and propose a new loss function, called the D-trace loss, for that purpose. A novel sparse precision matrix estimator is defined as the minimizer of the lasso penalized D-trace loss under a positive-definiteness constraint. Under a new irrepresentability condition, the lasso penalized D-trace estimator is shown to have the sparse recovery property. Examples demonstrate tha...
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作者：Weller, Grant B.; Cooley, Daniel
作者单位：Carnegie Mellon University; Colorado State University System; Colorado State University Fort Collins
摘要：A fundamental deficiency of classical multivariate extreme value theory is the inability to distinguish between asymptotic independence and exact independence. In this work, we examine multivariate threshold modelling in the framework of regular variation on cones. Tail dependence is described by a limiting measure, which in some cases is degenerate on joint tail regions despite strong subasymptotic dependence in such regions. Hidden regular variation, a higher-order tail decay on these region...
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作者：Xiong, Shifeng
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
摘要：This paper studies the relationship between model fitting and screening performance to find efficient screening methods for high-dimensional linear regression models. Under a sparsity assumption we show in a general asymptotic setting that a subset that includes the true submodel always yields a smaller residual sum of squares than those that do not. To seek such a subset, we consider the optimization problem associated with best subset regression. An em algorithm, known as orthogonalizing sub...
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作者：Wadsworth, Jennifer L.; Tawn, Jonathan A.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Lancaster University
摘要：Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, inference for max-stable processes has been restricted to the use of pairwise composite likelihoods, due to intractability of higher-dimensional distributions. In this work we consider random fields that are in the domain of attraction of a widely ...
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作者：Byrne, Simon P. J.; Dawid, A. Philip
作者单位：University of London; University College London; University of Cambridge
摘要：Prentice & Pyke (1979) established that the maximum likelihood estimate of an odds ratio in a case-control study is the same as would be found by fitting a logistic regression; in other words, for this specific target the incorrect prospective model is inferentially equivalent to the correct retrospective model. Similar results have been obtained for other models, and conditions have also been identified under which the corresponding Bayesian property holds, namely that the posterior distribut...
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作者：Voorman, Arend; Shojaie, Ali; Witten, Daniela
作者单位：University of Washington; University of Washington Seattle
摘要：In recent years, there has been considerable interest in estimating conditional independence graphs in high dimensions. Most previous work assumed that the variables are multivariate Gaussian or that the conditional means of the variables are linearly related. Unfortunately, if these assumptions are violated, the resulting conditional independence estimates can be inaccurate. We propose a semiparametric method, graph estimation with joint additive models, which allows the conditional means of ...
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作者：Skrondal, A.; Rabe-Hesketh, S.
作者单位：Norwegian Institute of Public Health (NIPH); University of California System; University of California Berkeley
摘要：We consider estimation of mixed-effects logistic regression models for longitudinal data when missing outcomes are not missing at random. A typology of missingness mechanisms is presented that includes missingness dependent on observed or missing current outcomes, observed or missing lagged outcomes and subject-specific effects. When data are not missing at random, consistent estimation by maximum marginal likelihood generally requires correct parametric modelling of the missingness mechanism,...
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作者：Zhang, Xinyu; Zou, Guohua; Liang, Hua
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; George Washington University
摘要：This article studies model averaging for linear mixed-effects models. We establish an unbiased estimator of the squared risk for the model averaging, and use the estimator as a criterion for choosing weights. The resulting model average estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments show it is superior or comparable to estimators based on the final models selected by the commonly-used methods and some existing averaging procedures. The...
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作者：Vansteelandt, S.; Martinussen, T.; Tchetgen, E. J.
作者单位：Ghent University; University of Copenhagen; Harvard University
摘要：We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least-squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard's dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We furthermore show that adjustment for auxiliary baseline covariates d...
