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Search Results: 1 - 10 of 40366 matches for " Jorge Alberto Achcar "
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Estudo da prevalência da tuberculose: uso de métodos bayesianos
Achcar, Jorge Alberto;Ruffino Netto, Antonio;
Revista Brasileira de Epidemiologia , 2003, DOI: 10.1590/S1415-790X2003000400012
Abstract: in this paper we present bayesian estimators of the prevalence of tuberculosis using computational methods for simulation of samples of posterior distribution of interest. we especially considered the gibbs sampling algorithm to generate samples of posterior distribution, and from these samples we obtained accurate inferences for the prevalence of tuberculosis. in an application, we analyzed the results of lung x-ray tests in the diagnosis of tuberculosis. with this application, we verified that bayesian estimators are more accurate than some existing estimators usually considered by health researchers. the use of computational methods for simulation of samples as the case of the gibbs sampling algorithm is becoming very popular for bayesian analysis in biostatistics. these simulation techniques using the gibbs sampling algorithm are easily implemented and do not require great computational expertise and usually can be performed using available existing software. we could also consider these techniques for studying the prevalence of other diseases.
A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application
SANTOS,CARLOS APARECIDO; ALBERTO ACHCAR,JORGE;
Revista Colombiana de Estadística , 2011,
Abstract: in this paper, we introduce a bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. different "frailties" or latent variables are considered to capture the correlation among the survival times for the same individual. we assume weibull or generalized gamma distributions considering right censored lifetime data. we develop the bayesian analysis using markov chain monte carlo (mcmc) methods.
Regression Models with Heteroscedasticity using Bayesian Approach
CEPEDA CUERVO,EDILBERTO; ACHCAR,JORGE ALBERTO;
Revista Colombiana de Estadística , 2009,
Abstract: in this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. in the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. in the second approach, we use hierarchical models. in the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. applying these methodologies, we analyze a colombian tallness data set to find differences that can be explained by socioeconomic conditions. we also present some theoretical and empirical results concerning the two models. from this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. we also observe that the convergence of the gibbs sampling chain used in the markov chain monte carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.
A BAYESIAN ANALYSIS IN THE PRESENCE OF COVARIATES FOR MULTIVARIATE SURVIVAL DATA: AN EXAMPLE OF APPLICATION ANáLISIS BAYESIANO EN PRESENCIA DE COVARIABLES PARA DATOS DE SOBREVIVENCIA MULTIVARIADOS: UN EJEMPLO DE APLICACIóN
Santos Carlos Aparecido,Achcar Jorge Alberto
Revista Colombiana de Estadística , 2011,
Abstract: In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different “frailties” or latent variables are considered to capture the correlation among the survival times for the same individual. We assumeWeibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. En este artículo, se introduce un análisis bayesiano para datos multivariados de sobrevivencia en presencia de un vector de covariables y observaciones censuradas. Diferentes “fragilidades” o variables latentes son consideradas para capturar la correlación entre los tiempos de sobrevivencia para un mismo individuo. Asumimos distribuciones Weibull o Gamma generalizadas considerando datos de tiempo de vida a derecha. Desarrollamos el análisis bayesiano usando métodos Markov Chain Monte Carlo (MCMC).
INDEXES TO MEASURE DEPENDENCE BETWEEN CLINICAL DIAGNOSTIC TESTS: A COMPARATIVE STUDY INDICES PARA MEDIR DEPENDENCIA ENTRE PRUEBAS PARA DIAGNóSTICO CLíNICO: UN ESTUDIO COMPARATIVO
Tovar José Rafael,Achcar Jorge Alberto
Revista Colombiana de Estadística , 2011,
Abstract: In many practical situations, clinical diagnostic procedures include two or more biological traits whose outcomes are expressed on a continuous scale and are then dichotomized using a cut point. As measurements are performed on the same individual there is a likely correlation between the continuous underlying traits that can go unnoticed when the parameter estimation is done with the resulting binary variables. In this paper, we compare the performance of two different indexes developed to evaluate the dependence between diagnostic clinical tests that assume binary structure in the results with the performance of the binary covariance and two copula dependence parameters. Muchos procedimientos de diagnóstico clínico médico exigen la evaluación de dos o mas rasgos biológicos que se ven alterados ante la presencia de fenómenos de enfermedad o infección, los cuales se expresan en una escala continúa de medición con posterior dicotomización usando de un valor límite o punto de corte. Dado que las mediciones son realizadas en el mismo individuo, los resultados probablemente presenten dependencia de algún tipo, lo cual puede ser ignorado en la etapa de análisis de datos dada la presentación binaria de los datos. En este estudio comparamos el comportamiento de dos parámetros de dependencia presentes en funciones de cópula con el de la covarianza binaria y dos índices creados para medir dependencia entre pruebas diagnósticas de respuesta dicótoma.
REGRESSION MODELS WITH HETEROSCEDASTICITY USING BAYESIAN APPROACH MODELOS DE REGRESIóN HETEROCEDáSTICOS USANDO APROXIMACIóN BAYESIANA
Cepeda Cuervo Edilberto,Achcar Jorge Alberto
Revista Colombiana de Estadística , 2009,
Abstract: In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved. En este artículo, comparamos el desempe o de dos aproximaciones estadísticas para el análisis de datos obtenidos en el área de investigación social. En la primera, utilizamos modelos normales con modelación conjunta de media y de heterogeneidad de varianza. En la segunda, utilizamos modelos jerárquicos. En el primer caso, se incluyen variables del individuo y de su entorno social en los modelos de media y varianza, como variables explicativas, mientras que, en el segundo, la variación en nivel 1 del modelo jerárquico depende de los individuos (edad de los individuos). En el nivel 2 del modelo jerárquico, se asume que la variación depende del estrato socioeconómico. Aplicando estas metodologías, analizamos un conjunto de datos de talla de los colombianos, para encontrar diferencias que pueden explicarse por sus condiciones socioeconómicas. También presentamos resultados teóricos y empíricos relacionados con los dos modelos considerados. A partir de este estudio comparativo concluimos que, en todos los casos, es “mejor” la modelación conjunta de media y varianza. Además de una interpretación muy sencilla, observamos una rápida convergencia de las cadenas generadas con la metodología propuesta para el ajuste de estos modelos.
Association between Air Cane Field Burning Pollution and Respiratory Diseases: A Bayesian Approach  [PDF]
Jorge Alberto Achcar, Mayara Piani Luna da Silva Sicchieri, Edson Zangiacomi Martinez
Journal of Environmental Protection (JEP) , 2013, DOI: 10.4236/jep.2013.48A1018
Abstract:

Respiratory diseases and air pollution are the goals of many scientific works, but studies of the relations between these diseases and cane field burning pollution are still not well studied in the literature. In this work, we consider the times between days of extrapolations of the number of daily hospitalizations due to respiratory diseases as our data. To analyze this data set, we introduce different statistical models related to burning focus pollution and their relations with the counting of hospitalizations due to respiratory diseases. Under a Bayesian approach and with the help of the free available WinBUGS software, we get posterior summaries of interest using standard MCMC (Markov Chain Monte Carlo) methods.

Climate changes and their effects in the public health: use of poisson regression models
Alonso, Jonas Bodini;Achcar, Jorge Alberto;Hotta, Luiz Koodi;
Pesquisa Operacional , 2010, DOI: 10.1590/S0101-74382010000200010
Abstract: in this paper, we analyze the daily number of hospitalizations in s?o paulo city, brazil, in the period of january 01, 2002 to december 31, 2005. this data set relates to pneumonia, coronary ischemic diseases, diabetes and chronic diseases in different age categories. in order to verify the effect of climate changes the following covariates are considered: atmosphere pressure, air humidity, temperature, year season and also a covariate related to the week day when the hospitalization occurred. the possible effects of the assumed covariates in the number of hospitalization are studied using a poisson regression model in the presence or not of a random effect which captures the possible correlation among the hospitalization accounting for the different age categories in the same day and the extra-poisson variability for the longitudinal data. the inferences of interest are obtained using the bayesian paradigm and mcmc (markov chain monte carlo) methods.
Modeling quality control data using mixture of parametrical distributions
Jorge Alberto Achcar,Claudio Luis Piratelli,Roberto Molina de Souza
International Journal of Industrial Engineering Computations , 2013, DOI: 10.5267/j.ijiec.2013.03.003
Abstract: In this paper, we present a Bayesian analysis of a data set selected from a Brazilian food company. This data set represents the times taken for different quality control analysts to test manufactured products arriving at the company’s quality control department. The samples selected from each batch contain mixtures of different products, which may be submitted to quality testing taking different times. From preliminary analysis of the data, it was observed that the histograms presented two clusters, indicating a mixture of distributions. A mixture of parametrical distributions was thus assumed in the presence of a covariate in order to analyze the data set and to establish standards to be used by the company for the times taken by the analysts. Inferences and predictions are obtained using a Bayesian approach with standard existing Markov Chain Monte Carlo (MCMC) methods.
Robust Linear Regression Models: Use of a Stable Distribution for the Response Data  [PDF]
Jorge A. Achcar, Angela Achcar, Edson Zangiacomi Martinez
Open Journal of Statistics (OJS) , 2013, DOI: 10.4236/ojs.2013.36048
Abstract:

In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software.


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