Displaying similar documents to “Modeling abilities in 3-IRT models.”

The extreme value Birnbaum-Saunders model, its moments and an application in biometry

M. Ivette Gomes, Marta Ferreira, Víctor Leiva (2012)

Biometrical Letters

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The Birnbaum-Saunders (BS) model is a life distribution that has been widely studied and applied. Recently, a new version of the BS distribution based on extreme value theory has been introduced, named the extreme value Birnbaum-Saunders (EVBS) distribution. In this article we provide some further details on the EVBS models that can be useful as a supplement to the existing results. We use these models to analyse real survival time data for patients treated with alkylating agents for...

Selection of variables in Discrete Discriminant Analysis

Anabela Marques, Ana Sousa Ferreira, Margarida G.M.S. Cardoso (2013)

Biometrical Letters

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In Discrete Discriminant Analysis one often has to deal with dimensionality problems. In fact, even a moderate number of explanatory variables leads to an enormous number of possible states (outcomes) when compared to the number of objects under study, as occurs particularly in the social sciences, humanities and health-related elds. As a consequence, classi cation or discriminant models may exhibit poor performance due to the large number of parameters to be estimated. In the present...

Estimation of nuisance parameters for inference based on least absolute deviations

Wojciech Niemiro (1995)

Applicationes Mathematicae

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Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.

Scaling of model approximation errors and expected entropy distances

Guido F. Montúfar, Johannes Rauh (2014)

Kybernetika

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We compute the expected value of the Kullback-Leibler divergence of various fundamental statistical models with respect to Dirichlet priors. For the uniform prior, the expected divergence of any model containing the uniform distribution is bounded by a constant 1 - γ . For the models that we consider this bound is approached as the cardinality of the sample space tends to infinity, if the model dimension remains relatively small. For Dirichlet priors with reasonable concentration parameters...