The Annals of Statistics

最新文章

• Cumulative regression function tests for regression models for longitudinal data AI翻译
  作者：Scheike, TH; Zhang, MJ
  作者单位：University of Copenhagen; Medical College of Wisconsin
  摘要：The longitudinal regression model Y-i,Y-j = m(V-tau i,j(i)) + epsilon(i,j) where Y-i,Y-j, is the jth measurement of the ith subject at random time tau(i,j), m is the regression function, V-tau i,j(i) is a predictable covariate process observed at time tau(i,j) and epsilon(i,j) is noise, often provides an adequate framework for modeling and comparing groups of data. The proposed longitudinal regression model is based on marked point process theory, and allows a quite general dependency structur...
• Regular fractional factorial designs with minimum aberration and maximum estimation capacity AI翻译
  作者：Cheng, CS; Mukerjee, R
  作者单位：University of California System; University of California Berkeley; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta
  摘要：Using the approach of finite projective geometry, we make a systematic study of estimation capacity, a criterion of model robustness, under the absence of interactions involving three or more factors. Some general results, providing designs with maximum estimation capacity, are obtained. In particular, for two-level factorials, it is seen that constructing a design with maximum estimation capacity calls for choosing points from a finite projective geometry such that the number of lines is maxi...
• State space modeling of long-memory processes AI翻译
  作者：Chan, NH; Palma, W
  作者单位：Carnegie Mellon University; Pontificia Universidad Catolica de Chile
  摘要：This paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the Kalman filter, the exact likelihood function can be computed recursively in a finite number of steps. Furthermore, an approximation to the likelihood function based on the truncated state space equation is considered. Asymptotic properties of these approximate maximum likelihood estimates are esta...
• Limiting distributions for L1 regression estimators under general conditions AI翻译
  作者：Knight, K
  作者单位：University of Toronto
  摘要：It is well known that L-1-estimators of regression parameters are asymptotically normal if the distribution function has a positive derivative at 0. In this paper, we derive the asymptotic distributions under more general conditions on the behavior of the distribution function near 0.
• Exponential posterior consistency via generalized Polya urn schemes in finite semiparametric mixtures AI翻译
  作者：Ishwaran, H
  作者单位：Cleveland Clinic Foundation
  摘要：Advances in Markov chain Monte Carlo (MCMC) methods now make it computationally feasible and relatively straightforward to apply the Dirichlet process prior in a wide range of Bayesian nonparametric problems. The feasibility of these methods rests heavily on the fact that the MCMC approach avoids direct sampling of the Dirichlet process and is instead based on sampling the finite-dimensional posterior which is obtained from marginalizing out the process. In application, it is the integrated po...
• Maximum likelihood estimates via duality for log-convex models when cell probabilities are subject to convex constraints AI翻译
  作者：El Barmi, H; Dykstra, R
  作者单位：Kansas State University; University of Iowa
  摘要：The purpose of this article is to derive and illustrate a method for fitting models involving both convex and log-convex constraints on the probability vector(s) of a (product) multinomial distribution. We give a two-step algorithm to obtain maximum likelihood estimates of the probability vector(s) and show that it is guaranteed to converge to the true solution. Some examples are discussed which illustrate the procedure.
• Validation of linear regression models AI翻译
  作者：Dette, H; Munk, A
  作者单位：Ruhr University Bochum
  摘要：A new test is proposed in order to verify that a regression function, say g, has a prescribed (Linear) parametric form. This procedure is based on the large sample behavior of an empirical L-2-distance between g and the subspace U spanned by the regression functions to be verified. The asymptotic distribution of the test statistic is shown to be normal with parameters depending only on the variance of the observations and the L-2-distance between the regression function g and the model space U...
• Efficient estimation from right-censored data when failure indicators are missing at random AI翻译
  作者：van der Laan, MJ; McKeague, IW
  作者单位：University of California System; University of California Berkeley; State University System of Florida; Florida State University
  摘要：The Kaplan-Meier estimator of a survival function is well known to be asymptotically efficient when cause of failure is always observed. It has been an open problem, however, to find an efficient estimator when failure indicators are missing at random. Lo showed that nonparametric maximum likelihood estimators are inconsistent, and this has led to several proposals of ad hoc estimators, none of which are efficient. We now introduce a sieved nonparametric maximum likelihood estimator, and show ...
• Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models AI翻译
  作者：Bickel, PJ; Ritov, Y; Rydén, T
  作者单位：University of California System; University of California Berkeley; Lund University; Hebrew University of Jerusalem
  摘要：Hidden Markov models (HMMs) have during the last decade become a widespread tool for modeling sequences of dependent random variables. Inference for such models is usually based on the maximum-likelihood estimator (MLE), and consistency of the MLE for general HMMs was recently proved by Leroux. In this paper me show that under mild conditions the MLE is also asymptotically normal and prove that the observed information matrix is a consistent estimator of the Fisher information.
• Direct estimation of low-dimensional components in additive models AI翻译
  作者：Fan, JQ; Härdle, W; Mammen, E
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Humboldt University of Berlin; Ruprecht Karls University Heidelberg
  摘要：Additive regression models have turned out to be a useful statistical tool in analyses of high-dimensional data sets. Recently, an estimator of additive components has been introduced by Linton and Nielsen which is based on marginal integration. The explicit definition of this estimator makes possible a fast computation and allows an asymptotic distribution theory. In this paper an asymptotic treatment of this estimate is offered for several models. A modification of this procedure is introduc...