Displaying similar documents to “Univariate parametric survival analysis using GS-distributions.”

A multimodal beta distribution with application to economic data

Saralees Nadarajah, Samuel Kotz (2007)

Applicationes Mathematicae

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Beta distributions are popular models for economic data. In this paper, a new multimodal beta distribution with bathtub shaped failure rate function is introduced. Various structural properties of this distribution are derived, including its cdf, moments, mean deviation about the mean, mean deviation about the median, entropy, asymptotic distribution of the extreme order statistics, maximum likelihood estimates and the Fisher information matrix. Finally, an application to consumer price...

Two lognormal models for real data.

Vernic, Raluca, Teodorescu, Sandra, Pelican, Elena (2009)

Analele Ştiinţifice ale Universităţii “Ovidius" Constanţa. Seria: Matematică

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A Bayesian look at nuisance parameters.

A. Philip Dawid (1980)

Trabajos de Estadística e Investigación Operativa

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The elimination of nuisance parameters has classically been tackled by various ad hoc devices, and has led to a number of attemps to define partial sufficiency and ancillarity. The Bayesian approach is clearly defined. This paper examines some classical procedures in order to see when they can be given a Bayesian justification.

Robustness of estimation of first-order autoregressive model under contaminated uniform white noise

Karima Nouali (2009)

Discussiones Mathematicae Probability and Statistics

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The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.

A Bayesian Spatial Mixture Model for FMRI Analysis

Geliazkova, Maya (2010)

Serdica Journal of Computing

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We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics...

Application of MCMC to change point detection

Jaromír Antoch, David Legát (2008)

Applications of Mathematics

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A nonstandard approach to change point estimation is presented in this paper. Three models with random coefficients and Bayesian approach are used for modelling the year average temperatures measured in Prague Klementinum. The posterior distribution of the change point and other parameters are estimated from the random samples generated by the combination of the Metropolis-Hastings algorithm and the Gibbs sampler.

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.