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作者：Leeb, Hannes; Poetscher, Benedikt M.
作者单位：Yale University; University of Vienna
摘要：We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model. The notion of a post-model-selection estimator here refers to the combined procedure resulting from first selecting a model (e.g., by a model selection criterion such as AIC or by a hypothesis testing procedure) and then estimating the parameters in the selected model (e.g., by least-squares or maximum likelihood), all based on the same data se...
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作者：Mammen, Enno; Park, Byeong U.
作者单位：University of Mannheim; Seoul National University (SNU)
摘要：In this paper a new smooth backfitting estimate is proposed for additive regression models. The estimate has the simple structure of Nadaraya-Watson smooth backfitting but at the same time achieves the oracle property of local linear smooth backfitting. Each component is estimated with the same asymptotic accuracy as if the other components were known.
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作者：Tai, Yu Chuan; Speed, Terence P.
作者单位：University of California System; University of California Berkeley
摘要：In this paper we derive one- and two-sample multivariate empirical Bayes statistics (the MB-statistics) to rank genes in order of interest from longitudinal replicated developmental microarray time course experiments. We first use conjugate priors to develop our one-sample multivariate empirical Bayes framework for the null hypothesis that the expected temporal profile stays at 0. This leads to our one-sample MB-statistic and a one-sample T-2-statistic, a variant of the one-sample Hotelling T-...
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作者：Straumann, Daniel; Mikosch, Thomas
作者单位：University of Copenhagen
摘要：This paper studies the quasi-maximum-likelihood estimator (QMLE) in a general conditionally heteroscedastic time series model of multiplicative form X-t = sigma(t)Z(t), where the unobservable volatility sigma(t) is a parametric function of (Xt-1,..., Xt-p, sigma(t-1),..., sigma(t-q)) for some p, q >= 0, and (Z(t)) is standardized i.i.d. noise. We assume that these models are solutions to stochastic recurrence equations which satisfy a contraction (random Lipschitz coefficient) property. These ...
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作者：Nau, Robert
作者单位：Duke University
摘要：Incomplete preferences provide the epistemic foundation for models of imprecise subjective probabilities and utilities that are used in robust Bayesian analysis and in theories of bounded rationality. This paper presents a simple axiomatization of incomplete preferences and characterizes the shape of their representing sets of probabilities and utilities. Deletion of the completeness assumption from the axiom system of Anscombe and Aumann yields preferences represented by a convex set of state...
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作者：Greenshtein, Eitan
作者单位：Purdue University System; Purdue University
摘要：Let (Y, X-1,..., X-m) be a random vector. It is desired to predict Y based on (X-1,..., X-m). Examples of prediction methods are regression, classification using logistic regression or separating hyperplanes, and so on. We consider the problem of best subset selection, and study it in the context m = n(alpha), alpha > 1, where n is the number of observations. We investigate procedures that are based on empirical risk minimization. It is shown, that in common cases, we should aim to find the be...
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作者：Pere, Anneli
作者单位：University of Helsinki
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作者：Wei, Ying; He, Xuming
作者单位：Columbia University; University of Illinois System; University of Illinois Urbana-Champaign
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作者：Zuo, Yijun
作者单位：Michigan State University
摘要：As estimators of location parameters, univariate trimmed means are well known for their robustness and efficiency. They can serve as robust alternatives to the sample mean while possessing high efficiencies at normal as well as heavy-tailed models. This paper introduces multidimensional trimmed means based on projection depth induced regions. Robustness of these depth trimmed means is investigated in terms of the influence function and finite sample breakdown point. The influence function capt...
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作者：Zhang, Tong
作者单位：Yahoo! Inc
摘要：We consider an extension of E-entropy to a KL-divergence based complexity measure for randomized density estimation methods. Based on this extension, we develop a general information-theoretical inequality that measures the statistical complexity of some deterministic and randomized density estimators. Consequences of the new inequality will be presented. In particular, we show that this technique can lead to improvements of some classical results concerning the convergence of minimum descript...
