2010

最新文章

• OPTIONAL POLYA TREE AND BAYESIAN INFERENCE AI翻译
  作者：Wong, Wing H.; Ma, Li
  作者单位：Stanford University
  摘要：We introduce an extension of the Polya tree approach for constructing distributions on the space of probability measures. By using optional stopping and optional choice of splitting variables, the construction gives rise to random measures that are absolutely continuous with piecewise smooth densities on partitions that can adapt to fit the data. The resulting optional Polya tree distribution has large support in total variation topology and yields posterior distributions that are also optiona...
• MAXIMUM Lq-LIKELIHOOD ESTIMATION AI翻译
  作者：Ferrari, Davide; Yang, Yuhong
  作者单位：Universita di Modena e Reggio Emilia; University of Minnesota System; University of Minnesota Twin Cities
  摘要：In this paper, the maximum L-q-likelihood estimator (MLqE), a new parameter estimator based on nonextensive entropy [Kibernetika 3 (1967) 30-35] is introduced. The properties of the MLqE are studied via asymptotic analysis and computer simulations. The behavior of the MLqE is characterized by the degree of distortion q applied to the assumed model. When q is properly chosen for small and moderate sample sizes, the MLqE can successfully trade bias for precision, resulting in a substantial reduc...
• 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...
• EMPIRICAL DYNAMICS FOR LONGITUDINAL DATA AI翻译
  作者：Mueller, Hans-Georg; Yao, Fang
  作者单位：University of California System; University of California Davis; University of Toronto
  摘要：We demonstrate that the processes underlying on-line auction price bids and many other longitudinal data can be represented by an empirical first order stochastic ordinary differential equation with time-varying coefficients and a smooth drift process. This equation may be empirically obtained from longitudinal observations for a sample of subjects and does not presuppose specific knowledge of the underlying processes. For the nonparametric estimation of the components of the differential equa...
• MODELING THE VARIABILITY OF RANKINGS AI翻译
  作者：Hall, Peter; Miller, Hugh
  作者单位：University of Melbourne
  摘要：For better or for worse, rankings of institutions, such as universities, schools and hospitals, play an important role today in conveying information about relative performance. They inform policy decisions and budgets, and are often reported in the media. While overall rankings can vary markedly over relatively short time periods, it is not unusual to find that the ranks of a small number of highly performing institutions remain fixed, even when the data on which the rankings are based are ex...
• INCONSISTENCY OF BOOTSTRAP: THE GRENANDER ESTIMATOR AI翻译
  作者：Sen, Bodhisattva; Banerjee, Moulinath; Woodroofe, Michael
  作者单位：Columbia University; University of Michigan System; University of Michigan
  摘要：In this paper, we investigate the (in)-consistency of different bootstrap methods for constructing confidence intervals in the class of estimators that converge at rate n(1/3). The Grenander estimator, the nonparametric maximum likelihood estimator of an unknown nonincreasing density function f on [0, infinity), is a prototypical example. We focus on this example and explore different approaches to constructing bootstrap confidence intervals for f(t(0)), where t(o) is an element of (0, infinit...
• GOODNESS-OF-FIT TESTS FOR HIGH-DIMENSIONAL GAUSSIAN LINEAR MODELS AI翻译
  作者：Verzelen, Nicolas; Villers, Fanny
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Universite Paris Saclay; INRAE
  摘要：Let (Y, (X-i)(1 <= i <= p)) be a real zero mean Gaussian vector and V be a subset of {1,..., p}. Suppose we are given n i.i.d. replications of this vector. We propose anew test for testing that Y is independent of (X-i)(i is an element of{1,...,p}\V) conditionally to (X-i)(i is an element of V) against the general alternative that it is not. This procedure does not depend on any prior information on the covariance of X or the variance of Y and applies in a high-dimensional setting. It straight...
• BAYESIAN ANALYSIS IN MOMENT INEQUALITY MODELS AI翻译
  作者：Liao, Yuan; Jiang, Wenxin
  作者单位：Northwestern University
  摘要：This paper presents a study of the large-sample behavior of the posterior distribution of a structural parameter which is partially identified by moment inequalities. The posterior density is derived based on the limited information likelihood. The posterior distribution converges to zero exponentially fast on any delta-contraction Outside the identified region. Inside, if is bounded below by a positive constant if the identified region is assumed to have a nonempty interior. Our simulation ev...
• SPADES AND MIXTURE MODELS AI翻译
  作者：Bunea, Florentina; Tsybakov, Alexandre B.; Wegkamp, Marten H.; Barbu, Adrian
  作者单位：State University System of Florida; Florida State University; Institut Polytechnique de Paris; ENSAE Paris; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
  摘要：This paper studies sparse density estimation via l(1) penalization (SPADES). We focus on estimation in high-dimensional mixture models and nonparametric adaptive density estimation. We show, respectively, that SPADES can recover, with high probability, the unknown components of a mixture of probability densities and that it yields minimax adaptive density estimates. These results are based on a general sparsity oracle inequality that the SPADES estimates satisfy. We offer a data driven method ...