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We prove two consistency theorems for the lasso estimators of sparse linear regression models with exponentially β - mixing errors, in which the number of regressors p is large, even much larger than the sample size n . [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to study the consistency of the kernel density estimator pertaining to a continuous time stationary process , with an underlying density . More precisely, in a rather general dependency setting, where we use a martingale difference device and a technique based on a sequence of projections on -fields, we establish the almost sure pointwise and uniform consistencies with rates of the estimate of built upon the part of the process . [Copyright &y& Elsevier]

Abstract: We propose Bayesian model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability model, and is useful for dependent data, when asymptotic variance of the parameter estimator is difficult to estimate. [Copyright &y& Elsevier]

Abstract: The estimation of the abundance of a species using the presence or absence of the species over a grid of cells simplifies data collection but the resulting statistical analysis is challenging. Several estimators have been proposed but their properties are unknown. Here we consider a generalized gamma-Poisson model which allows dependencies across the grid and develop a new estimator for this model. It is shown that this estimator is consistent, allowing us to conclude that it is indeed possible to estimate abundance from presence–absence maps. [Copyright &y& Elsevier]

Abstract: We study the large-sample properties of the penalized maximum likelihood estimator of a multivariate stochastic regression model with contemporaneously correlated data. The penalty is in terms of the square norm of some (vector) linear function of the regression coefficients. The model subsumes the so-called common transfer function model useful for extracting common signals in a panel of short time series. We show that, under mild regularity conditions, the penalized maximum likelihood estimator is consistent and asymptotically normal. The asymptotic bias of the regression coefficient estimator is also derived. [ABSTRACT FROM AUTHOR]

Abstract: Reproductive dispersion linear models (RDLMs) include generalized linear model [Nelder and Wedderburn (1972). Generalized linear models. J. Roy. Statist. Soc. Ser. A 135, 370–384] as a special case. Some mild regularity conditions analogous to those given by Fahrmeir and Kaufmann [1985. Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models. Ann. Statist. 13, 342–368] are proposed. On the basis of the proposed mild conditions, the existence, weak consistency, strong consistency, and asymptotic normality of the maximum likelihood estimate (MLE) are provided in RDLMs. The asymptotic results of Fahrmeir and Kaufmann [1985. Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models. Ann. Statist. 13, 342–368] are generalized to RDLMs. [Copyright &y& Elsevier]

Abstract: This paper considers a wavelet method for the estimation of the density and hazard rate functions from randomly censored data. We establish the weak uniform consistency of the density and hazard rate estimators. [Copyright &y& Elsevier]