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Displaying similar documents to “Statistical Modelling: Application to the financial sector”

The likelihood ratio test for the number of components in a mixture with Markov regime

Elisabeth Gassiat, Christine Keribin (2010)

ESAIM: Probability and Statistics

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We study the LRT statistic for testing a single population i.i.d. model against a mixture of two populations with Markov regime. We prove that the LRT statistic converges to infinity in probability as the number of observations tends to infinity. This is a consequence of a convergence result of the LRT statistic for a subproblem where the parameters are restricted to a subset of the whole parameter set.

Hidden Markov random fields and the genetic structure of the scandinavian brown bear population

Sophie Ancelet, Gilles Guillot, Olivier François (2007)

Journal de la société française de statistique

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Spatial bayesian clustering algorithms can provide correct inference of population genetic structure when applied to populations for which continuous variation of allele frequencies is disrupted by small discontinuities. Here we review works which used bayesian clustering algorithms for studying the Scandinavian brown bears, with particular attention to a recent method based on hidden Markov random field. We provide a summary of current knowledge about the genetic structure of this endangered...

Population genetics models for the statistics of DNA samples under different demographic scenarios - Maximum likelihood versus approximate methods

Andrzej Polański, Marek Kimmel (2003)

International Journal of Applied Mathematics and Computer Science

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The paper reviews the basic mathematical methodology of modeling neutral genetic evolution, including the statistics of the Fisher-Wright process, models of mutation and the coalescence method under various demographic scenarios. The basic approach is the use of maximum likelihood techniques. However, due to computational problems, intuitive or approximate methods are also of great importance.

Survival probabilities for HIV infected patients through semi-Markov processes

Giovanni Masala, Giuseppina Cannas, Marco Micocci (2014)

Biometrical Letters

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In this paper we apply a parametric semi-Markov process to model the dynamic evolution of HIV-1 infected patients. The seriousness of the infection is rendered by the CD4+ T-lymphocyte counts. For this purpose we introduce the main features of nonhomogeneous semi-Markov models. After determining the transition probabilities and the waiting time distributions in each state of the disease, we solve the evolution equations of the process in order to estimate the interval transition probabilities....

An estimation method for the reliability of "consecutive-k-out-of-n system"

Ksir, Brahim (2012)

Serdica Mathematical Journal

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2010 Mathematics Subject Classification: 60K10, 60K20, 60J10, 60J20, 62G02, 62G05, 68M15, 62N05, 68M15. This paper is concerned with consecutive-k-out-of-n system in which all the components have the same q lifetime probability, so, it's possible to estimate q from a sample by using the maximum likelihood principle. In the reliability formula of the consecutive-k-out-of-n system appears the term q^k. The goal in this work is to propose a direct estimation of q^k to avoid...

Local degeneracy of Markov chain Monte Carlo methods

Kengo Kamatani (2014)

ESAIM: Probability and Statistics

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We study asymptotic behavior of Markov chain Monte Carlo (MCMC) procedures. Sometimes the performances of MCMC procedures are poor and there are great importance for the study of such behavior. In this paper we call degeneracy for a particular type of poor performances. We show some equivalent conditions for degeneracy. As an application, we consider the cumulative probit model. It is well known that the natural data augmentation (DA) procedure does not work well for this model and the...

Central limit theorem for hitting times of functionals of Markov jump processes

Christian Paroissin, Bernard Ycart (2004)

ESAIM: Probability and Statistics

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A sample of i.i.d. continuous time Markov chains being defined, the sum over each component of a real function of the state is considered. For this functional, a central limit theorem for the first hitting time of a prescribed level is proved. The result extends the classical central limit theorem for order statistics. Various reliability models are presented as examples of applications.

Second Order optimality in Markov decision chains

Karel Sladký (2017)

Kybernetika

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The article is devoted to Markov reward chains in discrete-time setting with finite state spaces. Unfortunately, the usual optimization criteria examined in the literature on Markov decision chains, such as a total discounted, total reward up to reaching some specific state (called the first passage models) or mean (average) reward optimality, may be quite insufficient to characterize the problem from the point of a decision maker. To this end it seems that it may be preferable if not...