Displaying similar documents to “Predictive distribution for polytomous logistic models with nonlinear link functions.”

Univariate parametric survival analysis using GS-distributions.

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

SORT

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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...

Testing in locally conic models, and application to mixture models

Didier Dacunha-Castelle, Elisabeth Gassiat (2010)

ESAIM: Probability and Statistics

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In this paper, we address the problem of testing hypotheses using maximum likelihood statistics in non identifiable models. We derive the asymptotic distribution under very general assumptions. The key idea is a local reparameterization, depending on the underlying distribution, which is called locally conic. This method enlights how the general model induces the structure of the limiting distribution in terms of dimensionality of some derivative space. We present various applications...

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...

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.

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.