Displaying similar documents to “Bias correction on censored least squares regression models”

Bayesian nonparametric estimation of hazard rate in monotone Aalen model

Jana Timková (2014)

Kybernetika

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This text describes a method of estimating the hazard rate of survival data following monotone Aalen regression model. The proposed approach is based on techniques which were introduced by Arjas and Gasbarra [4]. The unknown functional parameters are assumed to be a priori piecewise constant on intervals of varying count and size. The estimates are obtained with the aid of the Gibbs sampler and its variants. The performance of the method is explored by simulations. The results indicate...

Robust median estimator for generalized linear models with binary responses

Tomáš Hobza, Leandro Pardo, Igor Vajda (2012)

Kybernetika

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The paper investigates generalized linear models (GLM's) with binary responses such as the logistic, probit, log-log, complementary log-log, scobit and power logit models. It introduces a median estimator of the underlying structural parameters of these models based on statistically smoothed binary responses. Consistency and asymptotic normality of this estimator are proved. Examples of derivation of the asymptotic covariance matrix under the above mentioned models are presented. Finally...

An efficient estimator for Gibbs random fields

Martin Janžura (2014)

Kybernetika

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An efficient estimator for the expectation ∫ f P ̣ is constructed, where P is a Gibbs random field, and f is a local statistic, i. e. a functional depending on a finite number of coordinates. The estimator coincides with the empirical estimator under the conditions stated in Greenwood and Wefelmeyer [6], and covers the known special cases, namely the von Mises statistic for the i.i.d. underlying fields and the case of one-dimensional Markov chains.

Modified power divergence estimators in normal models – simulation and comparative study

Iva Frýdlová, Igor Vajda, Václav Kůs (2012)

Kybernetika

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Point estimators based on minimization of information-theoretic divergences between empirical and hypothetical distribution induce a problem when working with continuous families which are measure-theoretically orthogonal with the family of empirical distributions. In this case, the φ -divergence is always equal to its upper bound, and the minimum φ -divergence estimates are trivial. Broniatowski and Vajda [3] proposed several modifications of the minimum divergence rule to provide a solution...