2期

最新文章

• Inference in components of variance models with low replication AI翻译
  作者：Hall, P; Yao, QW
  作者单位：Australian National University; University of London; London School Economics & Political Science
  摘要：in components of variance models the data are viewed as arising through a sum of two random variables, representing between- and within-group variation, respectively. The former is generally interpreted as a group effect, and the latter as error. It is assumed that these variables are stochastically independent and that the distributions of the group effect and the error do not vary from one instance to another. If each group effect can be replicated a large number of times, then standard meth...
• Estimating multiplicative and additive hazard functions by kernel methods AI翻译
  作者：Linton, OB; Nielsen, JP; Van de Geer, S
  作者单位：University of London; London School Economics & Political Science; Leiden University - Excl LUMC; Leiden University
  摘要：We propose new procedures for estimating the component functions in both additive and multiplicative nonparametric marker-dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring and time-varying covariates. Our procedures are based on kernel hazard estimation as developed by Nielsen and Linton and on the idea of marginal integration. We provide a central limit theorem for the marginal integration estimator. We then define esti...
• Compound decision theory and empirical Bayes methods AI翻译
  作者：Zhang, CH
  作者单位：Rutgers University System; Rutgers University New Brunswick
• Estimation in a Cox regression model with a change-point according to a threshold in a covariate AI翻译
  作者：Pons, O
  作者单位：Universite Paris Saclay; INRAE; Universite Paris Cite
  摘要：We consider a nonregular Cox model for independent and identically distributed right censored survival times, with a change-point according to the unknown threshold of a covariate. The maximum partial likelihood estimators of the parameters and the estimator of the baseline cumulative hazard are studied. We prove that the estimator of the change-point is n-consistent and the estimator of the regression parameters are n(1/2)-consistent, and we establish the asymptotic distributions of the estim...
• Robbins, empirical Bayes and microarrays AI翻译
  作者：Efron, B
  作者单位：Stanford University
  摘要：Empirical Bayes was Herbert Robbins' most influential contribution to statistical theory. It is also an idea of great practical potential. That potential is realized in the analysis of microarrays, a new biogenetic technology for the simultaneous measurement of thousands of gene expression levels.
• The publications and writings of Herbert Robbins AI翻译
  作者：[Anonymous]
• Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution AI翻译
  作者：Belitser, E; Ghosal, S
  作者单位：Utrecht University; North Carolina State University
  摘要：We consider the problem of estimating the mean of an infinite-dimensional normal distribution from the Bayesian perspective. Under the assumption that the unknown true mean satisfies a smoothness condition, we first derive the convergence rate of the posterior distribution for a prior that is the infinite product of certain normal distributions and compare with the minimax rate of convergence for point estimators. Although the posterior distribution can achieve the optimal rate of convergence,...
• Efficient detection of random coefficients in autoregressive models AI翻译
  作者：Akharif, A; Hallin, M
  作者单位：Abdelmalek Essaadi University of Tetouan; Universite Libre de Bruxelles
  摘要：The problem of detecting randomness in the coefficients of an AR(p) model, that is, the problem of testing ordinary AR(p) dependence against the alternative of a random coefficient autoregressive [RCAR(p)] model is considered. A nonstandard LAN property is established for RCAR(p) models in the vicinity of AR(p) ones. Two main problems arise in this context. The first problem is related to the statistical model itself: Gaussian assumptions are highly unrealistic in a nonlinear context, and inno...
• Bayesian analysis of proportional hazard models AI翻译
  作者：Kim, Y; Lee, J
  作者单位：Ewha Womans University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：This paper is concerned with Bayesian analysis of the proportional hazard model with left truncated and right censored data. We use a process neutral to the right as the prior of the baseline survival function and a finite-dimensional prior is placed on the regression coefficient, We then obtain the exact form of the joint posterior distribution of the regression coefficient and the baseline cumulative hazard function. As a by-product, we prove the propriety of the posterior distribution with ...
• Current status and right-censored data structures when observing a marker at the censoring time AI翻译
  作者：Van der Laan, MJ; Jewell, NP
  作者单位：University of California System; University of California Berkeley
  摘要：We study nonparametric estimation with two types of data structures. In the first data structure n i.i.d. copies of (C, N(C)) are observed, where N is a finite state counting process jumping at time-variables of interest and C a random monitoring time. In the second data structure n i.i.d. copies of (C boolean AND T, I(T less than or equal to C), N (C boolean AND T)) are observed, where N is a counting process with a final jump at time T (e.g., death). This data structure includes observing ri...