14 results on '"Achcar, Jorge Alberto"'
Search Results
2. Use of non-homogeneous Poisson process (NHPP) in presence of change-points to analyze drought periods: a case study in Brazil
- Author
-
Achcar, Jorge Alberto, Coelho-Barros, Emílio Augusto, and de Souza, Roberto Molina
- Published
- 2016
- Full Text
- View/download PDF
3. METHODS OF ESTIMATION IN MULTIPLE LINEAR REGRESSION: APPLICATION TO CLINICAL DATA MÉTODOS DE ESTIMACIÓN EN REGRESIÓN LINEAL MÚLTIPLE: APLICACIÓN A DATOS CLÍNICOS MÉTODOS DE ESTIMAÇÃO EM REGRESSÃO LINEAR MÚLTIPLA: APLICAÇÃO A DADOS CLÍNICOS
- Author
-
Coelho-Barros Emílio Augusto, Simões Priscila Angelotti, Achcar Jorge Alberto, Martinez Edson Zangiacomi, and Shimano Antônio Carlos
- Subjects
Multiple linear regression model ,Least square method ,Bayesian inference ,Bootstrap inference ,Statistics ,HA1-4737 - Abstract
In this paper, we show different parameters estimation forms for multiple linear regression model. We used clinical data, where the interest was to verify the relationship among the mechanical assay maximum stress with femoral mass, femoral diameter and group of ovariectomized Wistar rats. We used three inference methods: Classic inference, based on the least square method; bayesian inference, based on the Bayes theorem; and bootstrap inference, based on resampling processes.En este trabajo se muestran diferentes formas de estimación de parámetros para el modelo de regresión lineal múltiple. Para estimar los parámetros del modelo se utilizaron los datos de un ensayo clínico donde el interés era verificar la relación entre la tensión mecánica máxima del fémur con la masa y el diámetro femoral en un grupo experimental de ratas ovariectomizadas de la raza Rattus norvegicus albinos, variedad Wistar. Para estimar los parámetros del modelo se compararon tres métodos: la metodología clásica basada en el método de mínimos cuadrados, la metodología bayesiana, basada en el teorema de Bayes y la inferencia bootstrap basada en los procesos de remuestreo.Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.
- Published
- 2008
4. Discrete Bilal Distribution in the Presence of Right-Censored Data and a Cure Fraction.
- Author
-
de Freitas, Bruno Caparroz Lopes, Achcar, Jorge Alberto, de Oliveira Peres, Marcos Vinicius, and Martinez, Edson Zangiacomi
- Subjects
DISTRIBUTION (Probability theory) ,CONTINUOUS distributions ,MARKOV chain Monte Carlo ,GAMMA distributions ,MAXIMUM likelihood statistics ,FRACTIONS - Abstract
The statistical literature presents many continuous probability distributions with only one parameter, which are extensively used in the analysis of lifetime data, such as the exponential, the Lindley, and the Rayleigh distributions. Alternatively, the use of discretized versions of these distributions can provide a better fit for the data in many applications. As the novelty of this study, we present inferences for the discrete Bilal distribution (DB) with one parameter introduced by Altun et al. (2020) in the presence of right-censored data and cure fraction. We assume standard maximum likelihood methods based on asymptotic normality of the maximum likelihood estimators and also a Bayesian approach based on MCMC (Markov Chain Monte Carlo) simulation methods to get inferences for the parameters of the discrete BD distribution. The use of the proposed model was illustrated with three examples considering real medical lifetime data sets. From these applications, we concluded that the proposed model based on the discrete DB distribution has good performance even with the inclusion of a cure fraction in comparison to other existing discrete models, such as the DsFx-I, Lindley, Rayleigh, and Burr-Hatke probability distributions. Moreover, the model can be easily implemented in standard existing software, such as the R package. Under a Bayesian approach, we assumed a gamma prior distribution for the parameter of the DB discrete distribution. We also provided a brief sensitivity analysis assuming the half-normal distribution in place of the gamma distribution for the parameter of the DB distribution. From the obtained results of this study, we can conclude that the proposed methodology can be very useful for researchers dealing with medical discrete lifetime data in the presence of right-censored data and cure fraction. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Comparing the Number of Ozone Exceedances in Different Seasons of the Year in Mexico City
- Author
-
Achcar, Jorge Alberto, Sousa, Douglas Ernesto Fazioni, Rodrigues, Eliane R., and Tzintzun, Guadalupe
- Published
- 2011
- Full Text
- View/download PDF
6. Non-homogeneous Poisson models with a change-point: an application to ozone peaks in Mexico city
- Author
-
Achcar, Jorge Alberto, Rodrigues, Eliane R., Paulino, Carlos Daniel, and Soares, Paulo
- Published
- 2010
- Full Text
- View/download PDF
7. Non-homogeneous Poisson and linear regression models as approaches to study time series with change-points.
- Author
-
Oliveira, Ricardo Puziol de, Achcar, Jorge Alberto, Chen, Charles, and Rodrigues, Eliane R.
- Subjects
- *
TIME series analysis , *REGRESSION analysis , *POISSON processes , *STATISTICAL models , *GAUSSIAN distribution , *TUBERCULOSIS , *POISSON regression - Abstract
In this study we consider some stochastic models and a Bayesian approach to analyze count time series data in the presence of one or more change-points. When the observed count data are all different of zero, it is possible to perform an analysis using linear regression models with normal distribution errors for the log-transformed data. When we have the presence of zero counts at different times, the statistical model based on the log-transformation is not suitable. Hence, in this case, it is possible to consider non-homogeneous Poisson processes (NHPP) models with a suitable rate function. In the present work we consider both models (linear regression and Poisson) to analyze three data sets. These sets are data recording the monthly visitors to New Zealand with the purpose of education, yearly tuberculosis incidence data from New York City, and monthly measles incidence data from Brazil. When the NHPP model is used a PLP (power law process) model is assumed for the intensity function. Additionally, in both models, the presence of change-points will be allowed. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. Identifying water factors that are related to ammonia nitrogen concentrations in Columbia River using a reversed hazard exponential model.
- Author
-
de Abreu dos Santos, Danrley, Martins, Andrey Cassiano, da Silva, Kauana Mara, Nunes, Amanda Correa, Miranda, Yara Campos, Battistelli, André Aguiar, de Oliveira, Ricardo Puziol, Camilo, Rodrigo, and Achcar, Jorge Alberto
- Subjects
WATER quality ,AMMONIA ,SEWAGE ,INDUSTRIAL wastes ,WATER pollution - Abstract
Ammonia nitrogen is a compound that in its abundance is related to the inadequate discharge of industrial and domestic effluents. Since high concentrations of ammonia nitrogen can directly affect the water quality or disturb the marine environment, this study examined the ammonia nitrogen concentrations in Columbia River, USA in a fixed period (from January, 2014 to January, 2020) related to some water factors. Some interesting conclusions were obtained in this study as the identification of dissolved oxygen (in mg/L), pH, specific conductivity (in umhos/cm), and temperature (in °C) as potential factors affecting the ammonia nitrogen concentrations in Columbia River which may alert authorities to keep restrictive strategies to control the advance of this kind of water pollution. The results of this study could be extended to other regions of the world, especially in more impoverished countries usually with precarious water pollution control systems with implications for public health. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
9. Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction.
- Author
-
Lopes de Freitas, Bruno Caparroz, de Oliveira Peres, Marcos Vinicius, Achcar, Jorge Alberto, and Martinez, Edson Zangiacomi
- Subjects
BAYESIAN field theory ,MAXIMUM likelihood statistics ,CENSORING (Statistics) ,DATA modeling ,DISTRIBUTION (Probability theory) - Abstract
In this paper, we introduce maximum likelihood and Bayesian parameter estimation for the exponentiated discrete Weibull (EDW) distribution in the presence of randomly right-censored data. We also consider the inclusion of a cure fraction in the model. The performance of the maximum likelihood estimation approach is assessed by conducting an extensive simulation study with different sample sizes and different values for the parameters of the EDW distribution. The uselfuness of the proposed model is illustrated with two examples considering real data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
10. A new flexible bivariate discrete Rayleigh distribution generated by the Marshall-Olkin family.
- Author
-
de Oliveira, Ricardo Puziol and Achcar, Jorge Alberto
- Subjects
RAYLEIGH model ,BIVARIATE analysis ,RANDOM variables ,STATISTICAL correlation ,STATISTICS - Abstract
In the statistical analysis of bivariate data, it is possible to have discrete observations instead of continuous data, as observed in many studies with survival data. In this study it is introduced a new bivariate discrete distribution derived from two Rayleigh distributions using a method proposed by Marshall and Olkin (1997) where an additional parameter is is introduced to a family of distributions related to the dependence structure of two discrete random variables X 1 and X 2. The study results show that this new bivariate distribution has good statistical properties and a simple mathematical expression for its correlation coefficient. The usual classical and Bayesian estimators for the parameters of the new distribution are also presented. A simulation study is carried out in order to evaluate some frequentist properties of the proposed model. The usefulness of the proposed model is illustrated using a real medical dataset introduced in the literature in presence of censoring and covariates. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
11. Métodos de estimação em regressão linear múltipla: aplicação a dados clínicos
- Author
-
COELHO-BARROS, EMÍLIO AUGUSTO, SIMÕES, PRISCILA ANGELOTTI, ACHCAR, JORGE ALBERTO, MARTINEZ, EDSON ZANGIACOMI, and SHIMANO, ANTÔNIO CARLOS
- Subjects
análise bayesiana ,mínimos quadrados ,regressão linear múltipla ,Bayesian inference ,Multiple linear regression model ,bootstrap ,Least square method ,Bootstrap inference - Abstract
Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem. In this paper, we show different parameters estimation forms for multiple linear regression model. We used clinical data, where the interest was to verify the relationship among the mechanical assay maximum stress with femoral mass, femoral diameter and group of ovariectomized Wistar rats. We used three inference methods: Classic inference, based on the least square method; bayesian inference, based on the Bayes theorem; and bootstrap inference, based on resampling processes.
- Published
- 2008
12. The use of asymmetric distributions in average bioequivalence.
- Author
-
Souza, Roberto Molina, Achcar, Jorge Alberto, Martinez, Edson Zangiacomi, and Mazucheli, Josmar
- Subjects
- *
GENERIC drugs , *PHARMACOKINETICS , *PROBABILITY theory , *STATISTICS , *DATA analysis - Abstract
Generic drugs have been commercialized in numerous countries. Most of these countries approve the commercialization of a generic drug when there is evidence of bioequivalence between the generic drug and the reference drug. Generally, the pharmaceutical industry is responsible for the bioequivalence test under the supervision of a regulatory agency. This procedure is concluded after a statistical data analysis. Several agencies adopt a standard statistical analysis based on procedures that were previously established. In practice, we face situations in which this standard model does not fit to some sets of bioequivalence data. In this study, we propose an evaluation of bioequivalence using univariate and bivariate models based on an extended generalized gamma distribution and a skew-t distribution, under a Bayesian perspective. We introduce a study of the empirical power of hypothesis tests for univariate models, showing advantages in the use of an extended generalized gamma distribution. Three sets of bioequivalence data were analyzed under these new procedures and compared with the standard model proposed by the majority of regulatory agencies. In order to verify that the asymmetrical distributions are usually better fitted for the data, when compared with the standard model, model discrimination methods were used, such as the Deviance Information Criterion (DIC) and quantile-quantile plots. The research concluded that, in general, the use of the extended generalized gamma distribution may be more appropriate to model bioequivalence data in the original scale. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
13. Modelagem da dependência entre testes para diagnóstico clínico usando funções cópula
- Author
-
Tovar Cuevas, José Rafael, Achcar, Jorge Alberto, Curi, Mariana, Garcia, Nancy Lopes, Moala, Fernando Antonio, Wechsler, Sergio, Universidade Estadual de Campinas. Instituto de Matemática, Estatística e Computação Científica, Programa de Pós-Graduação em Estatística, and UNIVERSIDADE ESTADUAL DE CAMPINAS
- Subjects
Simulation methods ,Diagnóstico clínico ,Inferência bayesiana ,Cópulas (Estatística matemática) ,Estimativa de parâmetro ,Bayesian inference ,Parameter estimation ,Métodos de simulação ,Copula functions ,Clinical diagnosis - Abstract
Orientador: Jorge Alberto Achcar Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica Resumo: A maioria dos estudos sobre estimação da prevalência e parâmetros de desempenho de testes para diagnóstico clínico não tem considerado que muitos dos métodos de diagnóstico incluem a medição de traços biológicos cuja resposta é expressa em escala contínua e que, devido ao fato de serem medidos no mesmo indivíduo, esses traços necessariamente apresentam algum tipo de dependência que pode ou não ser explicada como um fenômeno de comportamento linear ou de concordância. Além disso, a análise de dados realizada nesses estudos parte do pressuposto de que a estrutura dos testes é binária sem considerar o fato de que as observações assumem essa apresentação depois de serem dicotomizadas usando um ponto de corte estabelecido a partir de critérios clínicos. Nesta tese, apresenta-se uma proposta de abordagem Bayesiana ao problema da estimação da prevalência, da sensibilidade e da especificidade dos testes dentro de planejamentos que incluem a aplicação de dois ou três testes diagnósticos de triagem, os quais são produto da medição de igual número de traços biológicos expressos em escala contínua com ponto de corte para dicotimização e um padrão-ouro para verificação. Embora o objetivo principal do modelo estatístico proposto seja estudar o efeito da dependência entre resultados dos testes de triagem sobre as estimativas da prevalência e os parâmetros de desempenho, também se consideram alternativas para contornar outras dificuldades comuns neste tipo de estudos, como a falta de identificabilidade e o viés devido à não verificação com padrão-ouro de indivíduos com resultado negativo em ambos os testes de triagem (viés de verificação). A proposta considera o uso de três funções cópula para modelar a dependência e a avaliação de três níveis da mesma. Dado o enfoque Bayesiano do estudo, foi necessário desenvolver um procedimento para elicitar distribuições a priori em situações de total ausência de informação sobre o parâmetro de interesse, o que acontece com as cópulas, funções bastante desconhecidas na pesquisa médica. Os resultados obtidos com o modelo proposto foram comparados com aqueles obtidos utilizando a covariância como parâmetro de dependência e o pressuposto de independência. O modelo apresenta uma reparametrização que, a diferença da maioria dos métodos apresentados na literatura sobre o tema, permite obter diretamente as estimativas de interesse sem a necessidade de complexos procedimentos analíticos e computacionais. A presença de dependência tem pouco efeito sobre a estimativa da prevalência e afeta as estimativas dos parâmetros de desempenho, o efeito é mais forte quando o planejamento apresenta viés de verificação. Dependências fracas subestimam as sensibilidades e os outros parâmetros não apresentam viés, enquanto que dependências fortes superestimam todos os parâmetros. Nos casos em que os traços biológicos medidos não apresentam fortes modificações devido à presença da enfermidade (ou infecção) no indivíduo, as estimativas podem chegar a tomar valores 50% maiores que o valor real, o que pode implicar importantes erros na tomada de decisões relacionadas à forma de tratar a doença Abstract: Most studies to estimate the prevalence and performance clinical diagnostic test parameters have not considered that many of the diagnostic methods include the measurement of biological traits with outcome expressed in a continuous scale and that, due to these traits, are measured on the same individual, they necessarily have some kind of dependence that may or not be explained as a phenomenon of linear relation or agreement. Generally authors assume that the data have binary structure and they do not consider the fact the data take that form after of they be dichotomized using a cut-off. In this thesis, it is developed a proposal based on a Bayesian approach to the problem of estimating the prevalence, the sensitivity and the specificity of tests within designs that include the application of a gold standard for verification and two or three screening diagnostic tests each of them resulting from a measurement of a biological trait expressed in a continuous scale and dichotomization. Although the main objective of the proposed statistical model is to study the effect of dependence between test results on prevalence and performance test parameter estimates, it is also studied some alternatives found in the literature to address common difficulties in diagnostic test studies such as the lack of identifiability and the verification bias (specially when individuals with negative results in both screening tests are not verified by "gold standard"procedure). The proposed estimation method considers the use of three copula functions to study the effect of same number of dependence levels on the parameter test estimates. Since the study is based on a Bayesian approach, it was necessary to develop a procedure to eliciting prior distributions in situations of total absence of information about the parameter of interest, as is the case of copula functions that have been very little used in medical research. The obtained results are compared with those obtained using binary covariance and independence between test outcomes assumption, methods frequently used by other authors. Unlike the majority of methods presented in the literature on the matter, the proposed estimation model has a reparametrization that gives the estimates of interest directly without need of use complex analytical and computational methods and the results are easily obtained using a Winbugs 1.4. program. The dependence affects the estimates of the test parameters and it has little effect on the prevalence estimate. The effect is stronger in presence of verification bias. Weak dependences underestimate the sensitivities and other parameters are unbiased, while strong dependencies overestimate all parameters. In situations in which the biological traits measured did not show strong changes due to the presence of the disease (or infection) on the individual, the estimates can reach values close to 50% higher than the real value, which may involve important errors in decision making related to how to treat the disease Doutorado Estatística e Probabilidade Doutor em Estatística
- Published
- 2021
14. SEIR models with time in-homogeneous removal rate
- Author
-
Márcio Augusto Diniz, Achcar, Jorge Alberto, Hotta, Luiz Koodi, 1952, Pinheiro, Aluísio de Souza, Martinez, Edson Zangiacomi, Universidade Estadual de Campinas. Instituto de Matemática, Estatística e Computação Científica, Programa de Pós-Graduação em Estatística, and UNIVERSIDADE ESTADUAL DE CAMPINAS
- Subjects
Métodos MCMC (Estatística) ,MCMC methods ,Inferência bayesiana ,Epidemiology ,SEIR epidemic models ,Modelos epidemiológicos SEIR ,Bayesian inference ,Epidemiologia - Abstract
Orientadores: Jorge Alberto Achcar, Luiz Koodi Hotta Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica Resumo: A modelagem matemática de epidemias apresenta grande relevância para a área de epidemiologia por possibilitar uma melhor compreensão do desenvolvimento da doença na população e permitir analisar o impacto de medidas de controle e erradicação. Neste contexto, os modelos compartimentais SEIR (Suscetíveis - Expostos - Infectantes - Removidos) que foram introduzidos por Kermarck e Mckendrick (1927 apud YANG, 2001, Capítulo 1) são extremamente utilizados. Esta dissertação discute o modelo SEIR encontrado em Lekone e Finkenstädt (2006) que considera a introdução das medidas de intervenção na taxa de contato entre suscetíveis e infectantes, e é aplicado aos dados parcialmente observados da epidemia de febre hemorrágica Ebola, ocorrida no Congo em 1995, através de métodos bayesianos. Em uma segunda etapa, o modelo é modificado a fim de considerar a introdução das medidas de intervenção também na taxa de remoção. A utilização da taxa de remoção não homogênea permite uma quantificação do impacto das medidas de intervenção mais próxima da realidade. Além disso, nos dois modelos considerados, uma análise da incerteza gerada pela observação parcial dos dados e uma análise de sensibilidade da escolha das distribuições a priori são realizadas a partir de simulações. E também, é apresentada uma discussão sobre erros de especificação da taxa de remoção Por fim, os dois modelos são aplicados aos dados da epidemia de febre hemorrágica Ebola e os resultados são discutidos Abstract: Mathematical modeling of epidemics has great relevance to the area of epidemiology by enabling a better understanding of the development of the disease in the population and allow the analysis of the impact of eradication and control measures. In this context, SEIR (Susceptible-Exposed-Infectious-Removed) compartmental models that were introduced by Kermarck e Mckendrick (1927 apud YANG, 2001, Chapter 1) are extremely used. This essay discusses the SEIR model found in Lekone e Finkenstädt (2006) that considers the introduction of intervention measures in the contact rate between susceptible and infectious, and is applied to data partially observed the outbreak of Ebola hemorrhagic fever in Congo, held in 1995, by Bayesian methods. In a second step, the model is modified in order to consider the introduction of intervention measures in the removal rate. The use of time in-homogeneous removal rate allows quantification of the impact of intervention measures closer to reality. In addition, both models considered, an analysis of uncertainty generated by partial observation and a sensitivity analysis of the choice of a priori distributions are made from simulations. And also, a discussion about errors of removal rate specification. Finally, the two models are applied to the data of Ebola hemorrhagic fever epidemic and the results are discussed Mestrado Métodos Estatísticos Mestre em Estatística
- Published
- 2011
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.