The Annals of Statistics

最新文章

• Inference under right censoring for transformation models with a change-point based on a covariate threshold AI翻译
  作者：Kosorok, Michael R.; Song, Rui
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：We consider linear transformation models applied to right censored survival data with a change-point in the regression coefficient based on a covariate threshold. We establish consistency and weak convergence of the nonparametric maximum likelihood estimators. The change-point parameter is shown to be n-consistent, while the remaining parameters are shown to have the expected root-n consistency. We show that the procedure is adaptive in the sense that the nonthreshold parameters are estimable ...
• On the number of support points of maximin and bayesian optimal designs AI翻译
  作者：Braess, Dietrich; Dette, Holger
  作者单位：Ruhr University Bochum
  摘要：We consider maximin and Bayesian D-optimal designs for nonlinear regression models. The maximin criterion requires the specification of a region for the nonlinear parameters in the model, while the Bayesian optimality criterion assumes that a prior for these parameters is available. On interval parameter spaces, it was observed empirically by many authors that an increase of uncertainty in the prior information (i.e., a larger range for the parameter space in the maximin criterion or a larger ...
• Nonparametric estimation when data on derivatives are available AI翻译
  作者：Hall, Peter; Yatchew, Adonis
  作者单位：University of Melbourne; University of Toronto
  摘要：We consider settings where data are available on a nonparametric function and various partial derivatives. Such circumstances arise in practice, for example in the joint estimation of cost and input functions in economics. We show that when derivative data are available, local averages can be replaced in certain dimensions by nonlocal averages, thus reducing the nonparametric dimension of the problem. We derive optimal rates of convergence and conditions under which dimension reduction is achi...
• Iterative estimating equations: Linear convergence and asymptotic properties AI翻译
  作者：Jiang, Jiming; Luan, Yihui; Wang, You-Gan
  作者单位：University of California System; University of California Davis; Shandong University; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  摘要：We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size increases to infinity. Furthermore, we show that the limiting estimator is consistent and asymptotically efficient, as expected. The method applies to semiparametric regression models with unspecified covariances among the observations. In the special case of linear...
• Monte Carlo likelihood inference for missing data models AI翻译
  作者：Sung, Yun Ju; Geyer, Charles J.
  作者单位：Washington University (WUSTL); University of Minnesota System; University of Minnesota Twin Cities
  摘要：We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer theta* of the Kullback-Leibler information, as both Monte Carlo and observed data...
• Rank-based estimation for all-pass time series models AI翻译
  作者：Andrews, Beth; Davis, Richard A.; Breidt, F. Jay
  作者单位：Northwestern University; Colorado State University System; Colorado State University Fort Collins
  摘要：An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying and modeling noncausal and noninvertible autoregressive-moving average processes. We establish asymptotic normality and consistency for rank-based estimators of all-pass model parameters. The estimators are obtained by minimizing the rank-based resid...
• Posterior convergence rates of dirichlet mixtures at smooth densities AI翻译
  作者：Ghosal, Subhashis; van der Vaart, Aad
  作者单位：North Carolina State University; Vrije Universiteit Amsterdam
  摘要：We study the rates of convergence of the posterior distribution for Bayesian density estimation with Dirichlet mixtures of normal distributions as the prior. The true density is assumed to be twice continuously differentiable. The bandwidth is given a sequence of priors which is obtained by scaling a single prior by an appropriate order. In order to handle this problem, we derive a new general rate theorem by considering a countable covering of the parameter space whose prior probabilities sat...
• Volatility estimators for discretely sampled Levy processes AI翻译
  作者：Ait-Sahalia, Yacine; Jacod, Jean
  作者单位：Princeton University; National Bureau of Economic Research; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Cite
  摘要：This paper studies the estimation of the volatility parameter in a model where the driving process is a Brownian motion or a more general symmetric stable process that is perturbed by another Levy process. We distinguish between a parametric case, where the law of the perturbing process is known, and a semiparametric case, where it is not. In the parametric case, we construct estimators which are asymptotically efficient. In the semiparametric case, we can obtain asymptotically efficient estim...
• On the maximum bias functions of MM-estimates and constrained M-estimates of regression AI翻译
  作者：Berrendero, Jose R.; Mendes, Beatriz V. M.; Tyler, David E.
  作者单位：Autonomous University of Madrid; Universidade Federal do Rio de Janeiro; Rutgers University System; Rutgers University New Brunswick
  摘要：We derive the maximum bias functions of the MM-estimates and the constrained M-estimates or CM-estimates of regression and compare them to the maximum bias functions of the S-estimates and the tau-estimates of regression. In these comparisons, the CM-estimates tend to exhibit the most favorable bias-robustness properties. Also, under the Gaussian model, it is shown how one can construct a CM-estimate which has a smaller maximum bias function than a given S-estimate, that is, the resulting CM-e...
• Convergence of adaptive mixtures of importance sampling schemes AI翻译
  作者：Douc, R.; Guillin, A.; Marin, J.-M.; Robert, C. P.
  作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; Centre National de la Recherche Scientifique (CNRS); Centre National de la Recherche Scientifique (CNRS); Universite PSL; Universite Paris-Dauphine; Universite Paris Saclay
  摘要：In the design of efficient simulation algorithms, one is often beset with a poor choice of proposal distributions. Although the performance of a given simulation kernel can clarify a posteriori how adequate this kernel is for the problem at hand, a permanent on-line modification of kernels causes concerns about the validity of the resulting algorithm. While the issue is most often intractable for MCMC algorithms, the equivalent version for importance sampling algorithms can be validated quite ...