Dynamic Weights in Gaussian Mixture Models: A Bayesian Approach

This paper proposes a generalization of Gaussian mixture models, where the mixture weight is allowed to behave as an unknown function of time. This model is capable of successfully capturing the features of the data, as demonstrated by simulated and real datasets. It can be useful in studies such as clustering, change-point and process control. In order to estimate the mixture weight function, we propose two new Bayesian nonlinear dynamic approaches for polynomial models, that can be extended to other problems involving polynomial nonlinear dynamic models. One of the methods, called here component-wise Metropolis-Hastings, apply the Metropolis-Hastings algorithm to each local level component of the state equation. It is more general and can be used in any situation where the observation and state equations are nonlinearly connected. The other method tends to be faster, but is applied specifically to binary data (using the probit link function). The performance of these methods of estimation, in the context of the proposed dynamic Gaussian mixture model, is evaluated through simulated datasets. Also, an application to an array Comparative Genomic Hybridization (aCGH) dataset from glioblastoma cancer illustrates our proposal, highlighting the ability of the method to detect chromosome aberrations

Bayesian Analysis of a Health Insurance Model

We consider the problem of determining health insurance premiums based on past information on size of loss, number of losses, and size of population at risk. The size of loss and the number of losses are treated as mutually independent random variables. The number of losses is assumed to follow a Poisson process, and the loss sizes are independent and identically distributed non-negative random variables, and the population at risk is assumed to follow a non-linear growth model. An expression for the premium is obtained through maximization of the insurer\u27s expected utility under a Bayesian model. The parameter estimation process is based on Monte Carlo Markov chain (MCMC). Our methology is applied to two real data sets

Statistical inference : an integrated approach

This book presents an account of the Bayesian and frequentist approaches to statistical inference. In this second edition this book provides students with an integrated understanding of statistical inference from both classical and Bayesian perspectives, describes the strengths and weaknesses of the two viewpoints, emphasizing the importance of a comparative approach to inference, covers all the main topics of standard inference courses, including point and interval estimation, hypothesis testing, prediction, approximation, and linear models, includes real data examples and numerous exercises, some with solution

Bayesian binary regression model: an application to in-hospital death after AMI prediction

A Bayesian binary regression model is developed to predict death of patients after acute myocardial infarction (AMI). Markov Chain Monte Carlo (MCMC) methods are used to make inference and to evaluate Bayesian binary regression models. A model building strategy based on Bayes factor is proposed and aspects of model validation are extensively discussed in the paper, including the posterior distribution for the c-index and the analysis of residuals. Risk assessment, based on variables easily available within minutes of the patients' arrival at the hospital, is very important to decide the course of the treatment. The identified model reveals itself strongly reliable and accurate, with a rate of correct classification of 88% and a concordance index of 83%.<br>Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%