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作者：Roueff, F; Rydén, T
作者单位：Centre National de la Recherche Scientifique (CNRS); Lund University
摘要：By a mixture density is meant a density of the form pi(mu) (.) = f pi(theta) (.) x mu(d theta), where (pi(theta))(theta Theta is an element of) is a family of probability densities and mu is a probability measure on Theta. We consider the problem of identifying the unknown part of this model, the mixing distribution A, from a finite sample of independent observations from pi(mu). Assuming that the mixing distribution has a density function, we wish to estimate this density within appropriate f...
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作者：Cai, TT; Low, MG
作者单位：University of Pennsylvania
摘要：Estimation of a quadratic functional over parameter spaces that are not quadratically convex is considered. It is shown, in contrast to the theory for quadratically convex parameter spaces, that optimal quadratic rules are often rate suboptimal. In such cases minimax rate optimal procedures are constructed based on local thresholding. These nonquadratic procedures are sometimes fully efficient even when optimal quadratic rules have slow rates of convergence. Moreover, it is shown that when est...
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作者：Ing, CK; Wei, CZ
作者单位：Academia Sinica - Taiwan; National Taiwan University
摘要：Assume that observations are generated from ail infinite-order autoregressive [AR(infinity)] process. Shibata [Ann. Statist. 8 (1980) 147-164] considered the problem of choosing a finite-order AR model, allowing the order to become infinite as the number of observations does in order to obtain a better approximation. He showed that, for the purpose of predicting the future of ail independent replicate, Akaike's information criterion (AIC) and its variants are asymptotically efficient. Although...
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作者：Zhang, T; Yu, B
作者单位：International Business Machines (IBM); IBM USA; University of California System; University of California Berkeley
摘要：Boosting is one of the most significant advances in machine learning for classification and regression. In its original and computationally flexible version, boosting seeks to minimize empirically a loss function in a greedy fashion. The resulting estimator takes an additive function form and is built iteratively by applying a base estimator (or learner) to updated samples depending on the previous iterations. An unusual regularization technique, early stopping, is employed based on CV or a te...
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作者：Ishwaran, H; Rao, JS
作者单位：Cleveland Clinic Foundation; University System of Ohio; Case Western Reserve University
摘要：Variable selection in the linear regression model takes many apparent faces from both frequentist and Bayesian standpoints. In this paper we introduce a variable selection method referred to as a rescaled spike and slab model. We study the importance of prior hierarchical specifications and draw connections to frequentist generalized ridge regression estimation. Specifically, we study the usefulness of continuous bimodal priors to model hypervariance parameters, and the effect scaling has on t...
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作者：James, LF
作者单位：Hong Kong University of Science & Technology
摘要：Suppose that P theta(g) is a linear functional of a Dirichlet process with shape theta H, where theta > 0 is the total mass and H is a fixed probability measure. This paper describes how one can use the well-known Bayesian prior to posterior analysis of the Dirichlet process, and a posterior calculus for Gamma processes to ascertain properties of linear functionals of Dirichlet processes. In particular, in conjunction with a Gamma identity, we show easily that a generalized Cauchy-Stieltjes tr...
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作者：Gelman, A
作者单位：Columbia University
摘要：Analysis of variance (ANOVA) is an extremely important method in exploratory and confirmatory data analysis. Unfortunately, in complex problems (e.g., split-plot designs), it is not always easy to set up an appropriate ANOVA. We propose a hierarchical analysis that automatically gives the correct ANOVA comparisons even in complex scenarios. The inferences for all means and variances are performed under a model with a separate batch of effects for each row of the ANOVA table. We connect to clas...
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作者：Yao, F; Müller, HG; Wang, JL
作者单位：Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Davis
摘要：We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects. In longitudinal studies, the number of repeated measurements per subject is often small and may be modeled as a disc...
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作者：Koltchinskii, V; Panchenko, D
作者单位：University of New Mexico; Massachusetts Institute of Technology (MIT)
摘要：We introduce and study several measures of complexity of functions from the convex hull of a given base class. These complexity measures take into account the sparsity of the weights of a convex combination as well as certain clustering properties of the base functions involved in it. We prove new upper confidence bounds on the generalization error of ensemble (voting) classification algorithms that utilize the new complexity measures along with the empirical distributions of classification ma...
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作者：Stute, W; Zhu, LX
作者单位：Justus Liebig University Giessen; University of Hong Kong
摘要：In this paper we study goodness-of-fit testing of single-index models. The large sample behavior of certain score-type test statistics is investigated. As a by-product, we obtain asymptotically distribution-free maximin tests for a large class of local alternatives. Furthermore, characteristic function based goodness-of-fit tests are proposed which are omnibus and able to detect peak alternatives. Simulation results indicate that the approximation through the limit distribution is acceptable a...
