2006

最新文章

• Risk bounds for statistical learning AI翻译
  作者：Massart, Pascal; Nedelec, Elodie
  作者单位：Universite Paris Saclay
  摘要：We propose a general theorem providing upper bounds for the risk of an empirical risk minimizer (ERM). We essentially focus on the binary classification framework. We extend Tsybakov's analysis of the risk of an ERM under margin type conditions by using concentration inequalities for conveniently weighted empirical processes. This allows us to deal with ways of measuring the size of a class of classifiers other than entropy with bracketing as in Tsybakov's work. In particular, we derive new ri...
• Optimal designs which are efficient for lack of fit tests AI翻译
  作者：Bischoff, Wolfgang; Miller, Frank
  作者单位：AstraZeneca
  摘要：Linear regression models are among the models most used in practice, although the practitioners are often not sure whether their assumed linear regression model is at least approximately true. In such situations, only designs for which the linear model can be checked are accepted in practice. For important linear regression models such as polynomial regression, optimal designs do not have this property. To get practically attractive designs, we suggest the following strategy. One part of the d...
• Efficient likelihood estimation in state space models AI翻译
  作者：Fuh, Cheng-Der
  作者单位：Academia Sinica - Taiwan
  摘要：Motivated by studying asymptotic properties of the maximum likelihood estimator (MLE) in stochastic volatility (SV) models, in this paper we investigate likelihood estimation in state space models. We first prove, under some regularity conditions, there is a consistent sequence of roots of the likelihood equation that is asymptotically normal with the inverse of the Fisher information as its variance. With an extra assumption that the likelihood equation has a unique root for each n, then ther...
• Consistent estimation of the basic neighborhood of Markov random fields AI翻译
  作者：Csiszár, I; Talata, Z
  作者单位：Hungarian Academy of Sciences; HUN-REN; HUN-REN Alfred Renyi Institute of Mathematics
  摘要：For Markov random fields on Z(d) with finite state space, we address the statistical estimation of the basic neighborhood, the smallest region that determines the conditional distribution at a site on the condition that the values at all other sites are given. A modification of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated ba...
• Poisson calculus for spatial neutral to the right processes AI翻译
  作者：James, Lancelot F.
  作者单位：Hong Kong University of Science & Technology
  摘要：Neutral to the right (NTR) processes were introduced by Doksum in 1974 as Bayesian priors on the class of distributions on the real line. Since that time there have been numerous applications to models that arise in survival analysis subject to possible right censoring. However, unlike the Dirichlet process, the larger class of NTR processes has not been used in a wider range of more complex statistical applications. Here, to circumvent some of these limitations, we describe a natural extensio...
• Efficient prediction for linear and nonlinear autoregressive models AI翻译
  作者：Mueller, Ursula U.; Schick, Anton; Wefelmeyer, Wolfgang
  作者单位：Texas A&M University System; Texas A&M University College Station; State University of New York (SUNY) System; Binghamton University, SUNY; University of Cologne
  摘要：Conditional expectations given past observations in stationary time series are usually estimated directly by kernel estimators, or by plugging in kernel estimators for transition densities. We show that, for linear and nonlinear autoregressive models driven by independent innovations, appropriate smoothed and weighted von Mises statistics of residuals estimate conditional expectations at better parametric rates and are asymptotically efficient. The proof is based on a uniform stochastic expans...
• Bayesian analysis for reversible Markov chains AI翻译
  作者：Diaconis, Persi; Rolles, Silke W. W.
  作者单位：Stanford University; Eindhoven University of Technology
  摘要：We introduce a natural conjugate prior for the transition matrix of a reversible Markov chain. This allows estimation and testing. The prior arises from random walk with reinforcement in the same way the Dirichlet prior arises from Polya's urn. We give closed form normalizing constants, a simple method of simulation from the posterior and a characterization along the lines of W. E. Johnson's characterization of the Dirichlet prior.
• Estimation for almost periodic processes AI翻译
  作者：Lii, Keh-Shin; Rosenblatt, Murray
  作者单位：University of California System; University of California Riverside; University of California System; University of California San Diego
  摘要：Processes with almost periodic covariance functions have spectral mass on lines parallel to the diagonal in the two-dimensional spectral plane. Methods have been given for estimation of spectral mass on the lines of spectral concentration if the locations of the lines are known. Here methods for estimating the intercepts of the lines of spectral concentration in the Gaussian case are given under appropriate conditions. The methods determine rates of convergence sufficiently fast as the sample ...
• Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes AI翻译
  作者：Dahlhaus, Rainer; Polonik, Wolfgang
  作者单位：Ruprecht Karls University Heidelberg; University of California System; University of California Davis
  摘要：This paper deals with nonparametric maximum likelihood estimation for Gaussian locally stationary processes. Our nonparametric MLE is constructed by minimizing a frequency domain likelihood over a class of functions. The asymptotic behavior of the resulting estimator is studied. The results depend on the richness of the class of functions. Both sieve estimation and global estimation are considered. Our results apply, in particular, to estimation under shape constraints. As an example, autoregr...
• Poisson inverse problems AI翻译
  作者：Antoniadis, Anestis; Bigot, Jeremie
  作者单位：Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Universite de Toulouse; Universite Toulouse III - Paul Sabatier
  摘要：In this paper we focus on nonparametric estimators in inverse problems for Poisson processes involving the use of wavelet decompositions. Adopting an adaptive wavelet Galerkin discretization, we find that our method combines the well-known theoretical advantages of wavelet-vaguelette decompositions for inverse problems in terms of optimally adapting to the unknown smoothness of the solution, together with the remarkably simple closed-form expressions of Galerkin inversion methods. Adapting the...