6期

最新文章

• A CLASS OF RENYI INFORMATION ESTIMATORS FOR MULTIDIMENSIONAL DENSITIES (vol 36, pg 2153, 2008) AI翻译
  作者：Leonenko, Nikolai; Pronzato, Luc
  作者单位：Cardiff University; Universite Cote d'Azur; Centre National de la Recherche Scientifique (CNRS)
• ON CONSTRUCTION OF THE SMALLEST ONE-SIDED CONFIDENCE INTERVAL FOR THE DIFFERENCE OF TWO PROPORTIONS AI翻译
  作者：Lloyd, Chris; Kabaila, Paul
  作者单位：La Trobe University
• CONVERGENCE AND PREDICTION OF PRINCIPAL COMPONENT SCORES IN HIGH-DIMENSIONAL SETTINGS AI翻译
  作者：Lee, Seunggeun; Zou, Fei; Wright, Fred A.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：A number of settings arise in which it is of interest to predict Principal Component (PC) scores for new observations using data from an initial sample. In this paper, we demonstrate that naive approaches to PC score prediction can be substantially biased toward 0 in the analysis of large matrices. This phenomenon is largely related to known inconsistency results for sample eigenvalues and eigenvectors as both dimensions of the matrix increase. For the spiked eigenvalue model for random matric...
• ANOVA FOR LONGITUDINAL DATA WITH MISSING VALUES AI翻译
  作者：Chen, Song Xi; Zhong, Ping-Shou
  作者单位：Iowa State University; Peking University
  摘要：We carry out ANOVA comparisons of multiple treatments for longitudinal studies with missing values. The treatment effects are modeled semiparametrically via a partially linear regression which is flexible in quantifying the time effects of treatments. The empirical likelihood is employed to formulate model-robust nonparametric ANOVA tests for treatment effects with respect to covariates, the nonparametric time-effect functions and interactions between covariates and time. The proposed tests ca...
• COORDINATE-INDEPENDENT SPARSE SUFFICIENT DIMENSION REDUCTION AND VARIABLE SELECTION AI翻译
  作者：Chen, Xin; Zou, Changliang; Cook, R. Dennis
  作者单位：Syracuse University; Nankai University; Nankai University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal set of their linear combinations without loss of information, is very helpful when the number of predictors is large. The standard SDR methods suffer because the estimated linear combinations usually consist of all original predictors, making it difficult to interpret. In this paper, we propose a unified method-coordinate-independent sparse estimation (CISE)-that can ...
• NONPARAMETRIC ESTIMATION OF MULTIVARIATE CONVEX-TRANSFORMED DENSITIES AI翻译
  作者：Seregin, Arseni; Wellner, Jon A.
  作者单位：University of Washington; University of Washington Seattle
  摘要：We study estimation of multivariate densities p of the form p(x) = h(g(x)) for x is an element of R-d and for a fixed monotone function h and an unknown convex function g. The canonical example is h(y) = e(-y) for y is an element of R; in this case, the resulting class of densities P(e(-y)) = {p = exp(-g) : g is convex} is well known as the class of log-concave densities. Other functions h allow for classes of densities with heavier tails than the log-concave class. We first investigate when t...
• ADAPTIVE NONPARAMETRIC BAYESIAN INFERENCE USING LOCATION-SCALE MIXTURE PRIORS AI翻译
  作者：de Jonge, R.; van Zanten, J. H.
  作者单位：Eindhoven University of Technology
  摘要：We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if a kernel mixture prior on a regression function is constructed using a Gaussian kernel, an inverse gamma bandwidth, and Gaussian mixing weights.
• SPARSITY IN MULTIPLE KERNEL LEARNING AI翻译
  作者：Koltchinskii, Vladimir; Yuan, Ming
  作者单位：University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
  摘要：The problem of multiple kernel learning based on penalized empirical risk minimization is discussed. The complexity penalty is determined jointly by the empirical L-2 norms and the reproducing kernel Hilbert space (RKHS) norms induced by the kernels with a data-driven choice of regularization parameters. The main focus is on the case when the total number of kernels is large, but only a relatively small number of them is needed to represent the target function, so that the problem is sparse. T...
• SEQUENTIALLY INTERACTING MARKOV CHAIN MONTE CARLO METHODS AI翻译
  作者：Brockwell, Anthony; Del Moral, Pierre; Doucet, Arnaud
  作者单位：Carnegie Mellon University; University of British Columbia; University of British Columbia
  摘要：Sequential Monte Carlo (SMC) is a methodology for sampling approximately from a sequence of probability distributions of increasing dimension and estimating their normalizing constants. We propose here an alternative methodology named Sequentially Interacting Markov Chain Monte Carlo (SIMCMC). SIMCMC methods work by generating interacting non-Markovian sequences which behave asymptotically like independent Metropolis-Hastings (MH) Markov chains with the desired limiting distributions. Contrary...
• UNIFORM CONVERGENCE RATES FOR NONPARAMETRIC REGRESSION AND PRINCIPAL COMPONENT ANALYSIS IN FUNCTIONAL/LONGITUDINAL DATA AI翻译
  作者：Li, Yehua; Hsing, Tailen
  作者单位：University System of Georgia; University of Georgia; University of Michigan System; University of Michigan
  摘要：We consider nonparametric estimation of the mean and covariance functions for functional/longitudinal data. Strong uniform convergence rates are developed for estimators that are local-linear smoothers. Our results are obtained in a unified framework in which the number of observations within each curve/cluster can be of any rate relative to the sample size. We show that the convergence rates for the procedures depend on both the number of sample curves and the number of observations on each c...