2009

最新文章

• ASYMPTOTIC EQUIVALENCE AND ADAPTIVE ESTIMATION FOR ROBUST NONPARAMETRIC REGRESSION AI翻译
  作者：Cai, T. Tony; Zhou, Harrison H.
  作者单位：University of Pennsylvania; Yale University
  摘要：Asymptotic equivalence theory developed in the literature so far are only for bounded loss functions. This limits the potential applications of the theory because many commonly used loss functions in statistical inference are unbounded. In this paper we develop asymptotic equivalence results for robust nonparametric regression with unbounded loss functions. The results imply that all the Gaussian nonparametric regression procedures can be robustified in a unified way. A key step in our equival...
• EFFICIENT RANDOMIZED-ADAPTIVE DESIGNS AI翻译
  作者：Hu, Feifang; Zhang, Li-Xin; He, Xuming
  作者单位：University of Virginia; Zhejiang University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Response-adaptive randomization hits recently attracted a lot of attention in the literature. In this paper, we propose a new and simple family of response-adaptive randomization procedures that attain the cramer-Rao lower bounds on the allocation variances for any allocation proportions including optimal allocation proportions. The allocation Probability functions of proposed procedures are discontinuous. The existing large sample theory for adaptive designs relies on Taylor expansions of the...
• FAST LEARNING RATES IN STATISTICAL INFERENCE THROUGH AGGREGATION AI翻译
  作者：Audibert, Jean-Yves
  作者单位：Universite Gustave-Eiffel; Institut Polytechnique de Paris; Ecole Nationale des Ponts et Chaussees; Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); Inria
  摘要：We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set g up to the smallest possible additive term, called the convergence rate. When the reference set is finite and when n denotes the size of the training data, we provide minimax convergence rates of the form C(log|g|/n)(nu) with tight evaluation of the positive constant C and with exact 0 < nu <= 1, the latter value depending on the convexity of the loss f...
• ON A GENERALIZED FALSE DISCOVERY RATE AI翻译
  作者：Sarkar, Sanat K.; Guo, Wenge
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
  摘要：The concept of k-FWER has received much attention lately as an appropriate error rate for multiple testing when one seeks to control at least k false rejections, for some fixed k >= 1. A less conservative notion, the k-FDR, has been introduced very recently by Sarkar [Ann. Statist. 34 (2006) 394-415], generalizing the false discovery rate of Benjamini and Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289-300]. In this article, we bring newer insight to the k-FDR considering a mixture model ...
• LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA AI翻译
  作者：Meinshausen, Nicolai; Yu, Bin
  作者单位：University of Oxford; University of California System; University of California Berkeley
  摘要：The Lasso is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables p(n) is potentially much larger than the number of samples n. However, it was recently discovered that the sparsity pattern of the Lasso estimator can only be asymptotically identical to the true sparsity pattern if the design matrix satisfies the so-called irrepresentable condition. The latter condition can easily be violated in the presence of high...
• ASYMPTOTIC INFERENCE FOR SEMIPARAMETRIC ASSOCIATION MODELS AI翻译
  作者：Osius, Gerhard
  作者单位：University of Bremen; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS)
  摘要：Association models for a pair of random elements X and Y (e.g., vectors) are considered which specify the odds ratio function up to an unknown parameter theta. These models are shown to be semiparametric in the sense that they do not restrict the marginal distributions of X and Y. Inference for the odds ratio parameter theta may be obtained from sampling either Y conditionally on X or vice versa. Generalizing results from Prentice and Pyke, Weinberg and Wacholder and Scott and Wild, we show th...
• ON THE PATH DENSITY OF A GRADIENT FIELD AI翻译
  作者：Genovese, Christopher R.; Perone-Pacifico, Marco; Verdinelli, Isabella; Wasserman, Larry
  作者单位：Carnegie Mellon University; Sapienza University Rome
  摘要：We consider the problem of reliably finding filaments in point clouds. Realistic data sets often have numerous filaments of various sizes and shapes. Statistical techniques exist for finding one (or a few) filaments but these methods do not handle noisy data sets with many filaments. Other methods can be found in the astronomy literature but they do not have rigorous statistical guarantees. We propose the following method. Starting at each data point we construct the steepest ascent path along...
• CONTOUR PROJECTED DIMENSION REDUCTION AI翻译
  作者：Luo, Ronghua; Wang, Hansheng; Tsai, Chih-Ling
  作者单位：Southwestern University of Finance & Economics - China; Peking University; University of California System; University of California Davis
  摘要：In regression analysis, we employ contour projection (CP) to develop a new dimension reduction theory. Accordingly, we introduce the notions of the central contour subspace and generalized contour subspace. We show that both of their structural dimensions are no larger than that of the central subspace Cook [Regression Graphics (1998b) Wiley]. Furthermore, we employ CP-sliced inverse regression, CP-sliced average variance estimation and CP-directional regression to estimate the generalized con...
• IMPROVING SAMC USING SMOOTHING METHODS: THEORY AND APPLICATIONS TO BAYESIAN MODEL SELECTION PROBLEMS AI翻译
  作者：Liang, Faming
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll [J. Amer Statist. Assoc. 102 (2007) 305-320] as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a change-point identification example. The numerical results indicate that the new algorithm can outperf...
• PROPERTIES AND REFINEMENTS OF THE FUSED LASSO AI翻译
  作者：Rinaldo, Alessandro
  作者单位：Carnegie Mellon University
  摘要：We consider estimating an unknown signal, both blocky and sparse, which is corrupted by additive noise. We study three interrelated least squares procedures and their asymptotic properties. The first procedure is the fused lasso, put forward by Friedman et al. [Ann. Appl. Statist. 1 (2007) 302-332], which we modify into a different estimator, called the fused adaptive lasso, with better properties. The other two estimators we discuss solve least squares problems on sieves; one constrains the m...