2期

最新文章

• Multiscale likelihood analysis and complexity penalized estimation AI翻译
  作者：Kolaczyk, ED; Nowak, RD
  作者单位：Boston University; University of Wisconsin System; University of Wisconsin Madison
  摘要：We describe here a framework for a certain class of multiscale likelihood factorizations wherein, in analogy to a wavelet decomposition of an L-2 function, a given likelihood function has an alternative representation as a product of conditional densities reflecting information in both the data and the parameter vector localized in position and scale. The framework is developed as a set of sufficient conditions for the existence of such factorizations, formulated in analogy to those underlying...
• Least angle regression - Rejoinder AI翻译
  作者：Efron, B; Hastie, T; Johnstone, I; Tibshirani, R
  作者单位：Stanford University
• The efficiency of the estimators of the parameters in GARCH processes AI翻译
  作者：Berkes, I; Horváth, L
  作者单位：Hungarian Academy of Sciences; HUN-REN; HUN-REN Alfred Renyi Institute of Mathematics; Utah System of Higher Education; University of Utah
  摘要：We propose a class of estimators for the parameters of a GARCH(p, q) sequence. We show that our estimators are consistent and asymptotically normal under mild conditions. The quasi-maximum likelihood and the likelihood estimators are discussed in detail. We show that the maximum likelihood estimator is optimal. If the tail of the distribution of the innovations is polynomial, even a quasi-maximum likelihood estimator based on exponential density performs better than the standard normal density...
• Least angle regression - Discussion AI翻译
  作者：Loubes, JM; Massart, P
  作者单位：Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
• Mean squared error of empirical predictor AI翻译
  作者：Das, K; Jiang, JM; Rao, JNK
  作者单位：University of Calcutta; Carleton University; University of California System; University of California Davis
  摘要：The term empirical predictor refers to a two-stage predictor of a linear combination of fixed and random effects. In the first stage, a predictor is obtained but it involves unknown parameters; thus, in the second stage, the unknown parameters are replaced by their estimators. In this paper, we consider mean squared errors (MSE) of empirical predictors under a general setup, where ML or REML estimators are used for the second stage. We obtain second-order approximation to the MSE as well as an...
• Finite sample properties of multiple imputation estimators AI翻译
  作者：Kim, JK
  作者单位：Yonsei University
  摘要：Finite sample properties of multiple imputation estimators under the linear regression model are studied. The exact bias of the multiple imputation variance estimator is presented. A method of reducing the bias is presented and simulation is used to make comparisons. We also show that the suggested method can be used for a general class of linear estimators.
• Missing at random, likelihood ignorability and model completeness AI翻译
  作者：Lu, GB; Copas, JB
  作者单位：University of Bristol; University of Warwick
  摘要：This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM). If the response model is given by a complete density family, then frequentist inference from the likelihood function ignoring the MDM is valid if and only if the MDM is MAR. This necessary and suffic...
• Minimax estimation of linear functionals over nonconvex parameter spaces AI翻译
  作者：Cai, TT; Low, MG
  作者单位：University of Pennsylvania
  摘要：The minimax theory for estimating linear functionals is extended to the case of a finite union of convex parameter spaces. Upper and lower bounds for the minimax risk can still be described in terms of a modulus of continuity. However in contrast to the theory for convex parameter spaces rate optimal procedures are often required to be nonlinear. A construction of such nonlinear procedures is given. The results developed in this paper have important applications to the theory of adaptation.
• Least angle regression AI翻译
  作者：Efron, B; Hastie, T; Johnstone, I; Tibshirani, R
  作者单位：Stanford University
  摘要：The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forwa...
• Selecting optimal multistep predictors for autoregressive processes of unknown order AI翻译
  作者：Ing, CK
  作者单位：Academia Sinica - Taiwan
  摘要：We consider the problem of choosing the optimal (in the sense of mean-squared prediction error) multistep predictor for an autoregressive (AR) process of finite but unknown order. If a working AR model (which is possibly misspecified) is adopted for multistep predictions, then two competing types of multistep predictors (i.e., plug-in and direct predictors) can be obtained from this model. We provide some interesting examples to show that when both plug-in and direct predictors are considered,...