Displaying similar documents to “Asymptotic behavior of the likelihood function of covariance matrices of spatial Gaussian processes.”

Indirect inference for survival data.

Bruce W. Turnbull, Wenxin Jiang (2003)

SORT

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In this paper we describe the so-called indirect method of inference, originally developed from the econometric literature, and apply it to survival analyses of two data sets with repeated events. This method is often more convenient computationally than maximum likelihood estimation when handling such model complexities as random effects and measurement error, for example; and it can also serve as a basis for robust inference with less stringent assumptions on the data generating mechanism....

Bayesian joint modelling of the mean and covariance structures for normal longitudinal data.

Edilberto Cepeda-Cuervo, Vicente Nunez-Anton (2007)

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We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We propose a new and computationally efficient classic estimation method based on the Fisher scoring algorithm to obtain the maximum likelihood estimates of the parameters. In addition, we also propose a new and innovative Bayesian methodology based on the Gibbs sampling, properly adapted for...

Empirical likelihood for quantile regression models with response data missing at random

S. Luo, Shuxia Pang (2017)

Open Mathematics

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This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random. It follows that a class of quantile empirical log-likelihood ratios including quantile empirical likelihood ratio with complete-case data, weighted quantile empirical likelihood ratio and imputed quantile empirical likelihood ratio are defined for the...

On non-nested regression models

Jiří Anděl (1993)

Commentationes Mathematicae Universitatis Carolinae

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A generalization of a test for non-nested models in linear regression is derived for the case when there are several regression models with more regressors.