Displaying similar documents to “Mixed parametrization for curved exponential models in homogeneous Markov processes with a finite state space.”

Information Matrix for Beta Distributions

Aryal, Gokarna, Nadarajah, Saralees (2004)

Serdica Mathematical Journal

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2000 Mathematics Subject Classification: 33C90, 62E99. The Fisher information matrix for three generalized beta distributions are derived.

Number of hidden states and memory: a joint order estimation problem for Markov chains with Markov regime

Antoine Chambaz, Catherine Matias (2009)

ESAIM: Probability and Statistics

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This paper deals with order identification for Markov chains with Markov regime (MCMR) in the context of finite alphabets. We define the joint order of a MCMR process in terms of the number of states of the hidden Markov chain and the memory of the conditional Markov chain. We study the properties of penalized maximum likelihood estimators for the unknown order of an observed MCMR process, relying on information theoretic arguments. The novelty of our work relies in the joint...

Knowledge revision in Markov networks.

Jörg Gebhardt, Christian Borgelt, Rudolf Kruse, Heinz Detmer (2004)

Mathware and Soft Computing

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A lot of research in graphical models has been devoted to developing correct and efficient evidence propagation methods, like join tree propagation or bucket elimination. With these methods it is possible to condition the represented probability distribution on given evidence, a reasoning process that is sometimes also called focusing. In practice, however, there is the additional need to revise the represented probability distribution in order to reflect some knowledge changes by satisfying...

Likelihood for random-effect models (with discussion).

Youngjo Lee, John A. Nelder (2005)

SORT

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For inferences from random-effect models Lee and Nelder (1996) proposed to use hierarchical likelihood (h-likelihood). It allows influence from models that may include both fixed and random parameters. Because of the presence of unobserved random variables h-likelihood is not a likelihood in the Fisherian sense. The Fisher likelihood framework has advantages such as generality of application, statistical and computational efficiency. We introduce an extended likelihood framework and...

Bounds on tail probabilities for negative binomial distributions

Peter Harremoës (2016)

Kybernetika

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In this paper we derive various bounds on tail probabilities of distributions for which the generated exponential family has a linear or quadratic variance function. The main result is an inequality relating the signed log-likelihood of a negative binomial distribution with the signed log-likelihood of a Gamma distribution. This bound leads to a new bound on the signed log-likelihood of a binomial distribution compared with a Poisson distribution that can be used to prove an intersection...

Shao's theorem on the maximum of standardized random walk increments for multidimensional arrays

Zakhar Kabluchko, Axel Munk (2009)

ESAIM: Probability and Statistics

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We generalize a theorem of Shao [ (1995) 575–582] on the almost-sure limiting behavior of the maximum of standardized random walk increments to multidimensional arrays of i.i.d. random variables. The main difficulty is the absence of an appropriate strong approximation result in the multidimensional setting. The multiscale statistic under consideration was used recently for the selection of the regularization parameter in a number of statistical algorithms as well...

Assessing influence in survival data with a cured fraction and covariates.

Edwin M. M. Ortega, Vicente G. Cancho, Victor Hugo Lachos (2008)

SORT

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Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms...