Displaying similar documents to “On the relation between gnostical and probability theories”

Survival analysis with coarsely observed covariates.

Soren Feodor Nielsen (2003)

SORT

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In this paper we consider analysis of survival data with incomplete covariate information. We model the incomplete covariates as a random coarsening of the complete covariate, and an overview of the theory of coarsening at random is given. Various ways of estimating the parameters of the model for the survival data given the covariates are discussed and compared.

Univariate parametric survival analysis using GS-distributions.

Albert Sorribas, José M. Muiño, Montserrat Rué, Joan Fibla (2006)

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The GS-distribution is a family of distributions that provide an accurate representation of any unimodal univariate continuous distribution. In this contribution we explore the utility of this family as a general model in survival analysis. We show that the survival function based on the GS-distribution is able to provide a model for univariate survival data and that appropriate estimates can be obtained. We develop some hypotheses tests that can be used for checking the underlying survival...

Indirect inference for survival data.

Bruce W. Turnbull, Wenxin Jiang (2003)

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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....

Three methods for constructing reference prior distributions.

Eusebio Gómez Sánchez-Manzano, Miguel A. Gómez Villegas (1990)

Revista Matemática de la Universidad Complutense de Madrid

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Three methods are proposed for constructing reference prior densities for certain biparametric distribution families. These densities represent approximations to the Bayesian concept of noninformative distribution.