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作者：Naveau, Philippe; Guillou, Armelle; Rietsch, Theo
作者单位：Universite Paris Saclay; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Centre National de la Recherche Scientifique (CNRS)
摘要：The paper focuses primarily on temperature extremes measured at 24 European stations with at least 90 years of data. Here, the term extremes refers to rare excesses of daily maxima and minima. As mean temperatures in this region have been warming over the last century, it is automatic that this positive shift can be detected also in extremes. After removing this warming trend, we focus on the question of determining whether other changes are still detectable in such extreme events. As we do no...
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作者：Lv, Jinchi; Liu, Jun S.
作者单位：University of Southern California; Harvard University
摘要：Model selection is of fundamental importance to high dimensional modelling featured in many contemporary applications. Classical principles of model selection include the Bayesian principle and the Kullback-Leibler divergence principle, which lead to the Bayesian information criterion and Akaike information criterion respectively, when models are correctly specified. Yet model misspecification is unavoidable in practice. We derive novel asymptotic expansions of the two well-known principles in...
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作者：Huser, R.; Davison, A. C.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
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作者：Borgonovo, E.; Tarantola, S.; Plischke, E.; Morris, M. D.
作者单位：Bocconi University; European Commission Joint Research Centre; EC JRC ISPRA Site; TU Clausthal; Iowa State University
摘要：Monotonic transformations are widely employed in statistics and data analysis. In computer experiments they are often used to gain accuracy in the estimation of global sensitivity statistics. However, one faces the question of interpreting results that are obtained on the transformed data back on the original data. The situation is even more complex in computer experiments, because transformations alter the model input-output mapping and distort the estimators. This work demonstrates that the ...
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作者：Imai, Kosuke; Ratkovic, Marc
作者单位：Princeton University
摘要：The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias...
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作者：Chen, Yu-Chun; Cheng, Ming-Yen; Wu, Hau-Tieng
作者单位：National Yang Ming Chiao Tung University; National Taiwan University; University of California System; University of California Berkeley
摘要：Periodicity and trend are features describing an observed sequence, and extracting these features is an important issue in many scientific fields. However, it is not an easy task for existing methods to analyse simultaneously the trend and dynamics of the periodicity such as time varying frequency and amplitude, and the adaptivity of the analysis to such dynamics and robustness to heteroscedastic dependent errors are not guaranteed. These tasks become even more challenging when there are multi...
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作者：Chiou, Jeng-Min; Mueller, Hans-Georg
作者单位：Academia Sinica - Taiwan; University of California System; University of California Davis
摘要：Multivariate functional data are increasingly encountered in data analysis, whereas statistical models for such data are not well developed yet. Motivated by a case-study where one aims to quantify the relationship between various longitudinally recorded behaviour intensities for Drosophila flies, we propose a functional linear manifold model. This model reflects the functional dependence between the components of multivariate random processes and is defined through data-determined linear comb...
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作者：Marin, Jean-Michel; Pillai, Natesh S.; Robert, Christian P.; Rousseau, Judith
作者单位：Universite de Montpellier; Harvard University; Universite PSL; Universite Paris-Dauphine; University of Warwick; Institut Polytechnique de Paris; ENSAE Paris; Universite PSL; Universite Paris-Dauphine
摘要：The choice of the summary statistics that are used in Bayesian inference and in particular in approximate Bayesian computation algorithms has bearings on the validation of the resulting inference. Those statistics are nonetheless customarily used in approximate Bayesian computation algorithms without consistency checks. We derive necessary and sufficient conditions on summary statistics for the corresponding Bayes factor to be convergent, namely to select the true model asymptotically. Those c...
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作者：Aharoni, Ehud; Rosset, Saharon
作者单位：International Business Machines (IBM); IBM ISRAEL; Tel Aviv University
摘要：The increasing prevalence and utility of large public databases necessitates the development of appropriate methods for controlling false discovery. Motivated by this challenge, we discuss the generic problem of testing a possibly infinite stream of null hypotheses. In this context, Foster and Stine suggested a novel method named alpha-investing for controlling a false discovery measure known as mFDR. We develop a more general procedure for controlling mFDR, of which alpha-investing is a speci...
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作者：Polson, Nicholas G.; Scott, James G.; Windle, Jesse
作者单位：University of Chicago; University of Texas System; University of Texas Austin
摘要：We propose the Bayesian bridge estimator for regularized regression and classification. Two key mixture representations for the Bayesian bridge model are developed: a scale mixture of normal distributions with respect to an a-stable random variable; a mixture of Bartlett-Fejer kernels (or triangle densities) with respect to a two-component mixture of gamma random variables. Both lead to Markov chain Monte Carlo methods for posterior simulation, and these methods turn out to have complementary ...
