The Annals of Statistics

最新文章

• 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...
• ROBUSTNESS OF MULTIPLE TESTING PROCEDURES AGAINST DEPENDENCE AI翻译
  作者：Clarke, Sandy; Hall, Peter
  作者单位：University of Melbourne
  摘要：An important aspect of multiple hypothesis testing is controlling the significance level, or the level of Type I error. When the test statistics are not independent it can be particularly challenging to deal with this problem, without resorting to very conservative procedures. In this paper we show that, in the context of contemporary multiple testing problems, where the number of tests is often very large, the difficulties caused by dependence are less serious than in classical cases. This is...
• MINIMUM DISTANCE REGRESSION MODEL CHECKING WITH BERKSON MEASUREMENT ERRORS AI翻译
  作者：Koul, Hira L.; Song, Weixing
  作者单位：Michigan State University; Kansas State University
  摘要：Lack-of-fit testing of a regression model with Berkson measurement error has not been discussed in the literature to date. To fill this void, we propose a class of tests based on minimized integrated square distances between nonparametric regression function estimator and the parametric model being fitted. We prove asymptotic normality of these test statistics under the null hypothesis and that of the corresponding minimum distance estimators under minimal conditions on the model being fitted....
• A CONJUGATE PRIOR FOR DISCRETE HIERARCHICAL LOG-LINEAR MODELS AI翻译
  作者：Massam, Helene; Liu, Jinnan; Dobra, Adrian
  作者单位：York University - Canada; University of Washington; University of Washington Seattle
  摘要：In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the log-linear parameters or the cell probabilities parameters is a major challenge. In this paper, we define a flexible family of conjugate priors for the wide class of discrete hierarchical log-linear models, which includes the class of graphical models. These priors are defined as the Diaconis-Ylvisaker conjugate priors on the log-linear parameters subject to baseline constraints under mul...