97 research outputs found
A dynamic structural equation approach to estimate the short-term effects of air pollution on human health.
No full textDetailed knowledge on the effects of air pollutants on human health is a prerequisite for the development of effective policies to reduce the adverse impact of ambient air pollution. However, measuring the effect of exposure on health outcomes is an extremely difficult task as the health impact of air pollution is known to vary over space and over different exposure periods. In general, standard approaches aggregate the information over space or time to simplify the study but this strategy fails to recognize important regional differences and runs into the well-known risk of confounding the effects. However, modelling directly with the original, disaggregated data requires a highly dimensional model with the curse of dimensionality making inferences unstable; in these cases, the models tend to retain many irrelevant components and most relevant effects tend to be attenuated. The situation clearly calls for an intermediate solution that does not blindly aggregate data while preserving important regional features. We propose a dimension-reduction approach based on latent factors driven by the data. These factors naturally absorb the relevant features provided by the data and establish the link between pollutants and health outcomes, instead of forcing a necessarily high-dimensional link at the observational level. The dynamic structural equation approach is particularly suited for this task. The latent factor approach also provides a simple solution to the spatial misalignment caused by using variables with different spatial resolutions and the state-space representation of the model favours the application of impulse response analysis. Our approach is discussed through the analysis of the short-term effects of air pollution on hospitalization data from Lombardia and Piemonte regions (Italy)
Space–time modelling of coupled spatiotemporal environmental variables
No full textThe paper is concerned with a dynamic factor model for spatiotemporal coupled
environmental variables. The model is proposed in a state space formulation which, through
Kalman recursions, allows a unified approach to prediction and estimation. Full probabilistic inference
for the model parameters is facilitated by adapting standard Markov chain Monte Carlo
algorithms for dynamic linear models to our model formulation.The predictive ability of the model
is discussed for two different data sets with variables measured at two different scales. Some
possibilities for further research are also outlined
A bayesian approach to hybrid splines non-parametric regression
No full textA Bayesian approach is considered to estimate the number of basis functions and the smoothing parameter of the hybrid splines non-parametric regression procedure. The method used to obtain the estimate of the regression curve and its Bayesian confidence intervals is based on the reversible jump MCMC (Green 1995). Illustrations with simulated data are provided and show good performance of the proposed approach over the existing methods.A Bayesian approach is considered to estimate the number of basis functions and the smoothing parameter of the hybrid splines non-parametric regression procedure. The method used to obtain the estimate of the regression curve and its Bayesian confidence i724285297FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPERJ - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DO RIO DE JANEIRO95=4996-3; 99=00261-0259=98450560=98; 300644=94-9SEM INFORMAÇÃOWe would like to thank the Associate Editor and the referees for the comments and suggestions that made this work better and clearer. Also, the first author would like to thank Prof. Michael Newton and Prof. Mary Lindstrom for all suggestion made during
Dynamic analysis of survival models and related processes.
No full textThis thesis presents new methods of analysis of survival data based on a Dynamic Bayesian approach. The models allow the parameters to change with time. The analysis is tractable and emphasises predictive aspects of the models. The survival problems covered include linear and non-linear regression, analysis of random samples, time-dependent covariates, life tables and competing risks. The analysis is also extended to a number of point processes. Numerical applications are provided and the microcomputer software to perform them is described
Modelos dinâmicos e simulação estocástica
No full textThis paper presents new methodology for making Bayesian inference about dy~ o!s for exponential famiIy observations. The approach is simulation-based _~t> use of ~vlarkov chain Monte Carlo techniques. A yletropolis-Hastings i:U~UnLlllll 1::; combined with the Gibbs sampler in repeated use of an adjusted version of normal dynamic linear models. Different alternative schemes are derived and compared. The approach is fully Bayesian in obtaining posterior samples for state parameters and unknown hyperparameters. Illustrations to real data sets with sparse counts and missing values are presented. Extensions to accommodate for general distributions for observations and disturbances. intervention. non-linear models and rnultivariate time series are outlined
Exchangeability of binary random quantities and the gambler\'s fallacy
No full textO elemento central deste estudo é o problema de predição em seqüências de variáveis aleatórias binárias (0-1). Modelos são estudados para esse tipo de situação e então relacionados com a Falácia do Apostador - um famoso caso de estudo da Psicologia (também conhecida como Lei da Maturidade). Estudos estatísticos anteriores propõem tal modelagem sob a perspectiva bayesiana. Neles, tem-se a suposição de permutabilidade infinita e, como conseqüência, a maturidade é um comportamento inadmissível. Neste estudo, um novo modelo é apresentado, no qual a crença do apostador não é necessariamente uma falácia. Este é o modelo preditivista usual de população finita e, portanto, somente quantidades com significado operacional (parâmetros operacionais) são envolvidas. Uma classe de prioris para o parâmetro operacional que resulta em modelos não estendíveis é apresentada. Trata-se de uma classe de distribuições que definimos como mais estreitas que a Binomial. Maturidade é uma conseqüência da crença em prioris dessa classe. Apresenta-se ainda uma subclasse referente às distribuições mais estreitas de segunda ordem que a Binomial. Para prioris dessa subclasse tem-se taxa de falha preditiva crescente, que pode ser interpretado como o resultado mais extremo de maturidade. Os resultados deste estudo podem contribuir para o julgamento de quão razoável é a suposição de permutabilidade infinita em relação ao típico comportamento humano. Outra principal contribuição está associada ao estudo de condições de estendibilidade em processos binários.We study the problem of prediction in sequences of binary random variables. Models are studied for this kind of situation and then considered vis-à-vis the Gambler\'s Fallacy - a famous case study in Psychology (also known as Law of Maturity). Previous statistical studies proposed such modeling under the bayesian perspective. In them there is the assumption of exchangeability and, as a result, maturity is a inadmissible behavior. In this study, a new model in which the Gambler\'s belief need not be a fallacy is presented. This one is the usual finite population model and, therefore, only operationally meaningful quantities (operational parameters) are involved. A class of prior distributions for the operational parameter which yield non-extendable models is presented. It is a class of distributions which we defined as tighter than the Binomial. Maturity is a consequence of the belief in the prior distributions of this class. Furthermore, a subclass which refers to the distributions that are second-order tighter than the Binomial is presented. For prior distributions of this subclass the predictive failure rate is increasing, which can be interpreted as the most extreme case of maturity. The results of this study may contribute on the judgment of how reasonable the assumption of infinite exchangeability is relative to typical human perception. Another major contribution is related to the study on extendibility conditions in binary processes
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