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• Hazard models with varying coefficients for multivariate failure time data AI翻译
  作者：Cai, Jianwen; Fan, Jianqing; Zhou, Haibo; Zhou, Yong
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Princeton University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
  摘要：Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency and asymptotic normality of the proposed estimators are established and standard error formulas for th...
• 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...
• 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 ...
• Convergence rates of posterior distributions for noniid observations 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 consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor identically distributed. We give general results on the rate of convergence of the posterior measure relative to distances derived from a testing criterion. We then specialize our results to independent, nonidentically distributed observations, Markov processes, stationary Gaussian time series and the white noise model. We apply our general ...
• Inference for mixtures of symmetric distributions AI翻译
  作者：Hunter, David R.; Wang, Shaoli; Hettmansperger, Thomas P.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Yale University
  摘要：This article discusses the problem of estimation of parameters in finite mixtures when the mixture components are assumed to be symmetric and to come from the same location family. We refer to these mixtures as semi-parametric because no additional assumptions other than symmetry are made regarding the parametric form of the component distributions. Because the class of symmetric distributions is so broad, identifiability of parameters is a major issue in these mixtures. We develop a notion of...