Short course: Introduction to Julia
JF-Brazil, Departament of Statistics
Doctoral Scholarship from CNPq. Graduated in Statistics - Universidad Nacional Mayor de San Marcos (2010), Masters in Statistics from the State University of Campinas (2014), PhD in Progress in Statistics from the University of Sao Paulo (USP) (2014-2018). His Statistical interests areas are asymmetric-elliptical distributions, Linear/nonlinear mixed effects models, finite mixture of distributions, Censored models.
Meeting organizer of São Paulo R Users Group (1767 participants), São Paulo, Brazil.
JF-Brazil, Departament of Statistics
USP-Brazil, Institute of Mathematics and Statistics
USP-Brazil, Institute of Mathematics and Statistics
UNICAMP-Brazil, Institute of Mathematics, Statistics and Computing Science
UNICAMP-Brazil, Institute of Mathematics, Statistics and Computing Science
UNMSM-Perú, Professional Academic School of Statistics
UNMSM-Perú, Professional Academic School of Statistics
Ph.D. in Statistics (in progress)
University of São Paulo - Brasil
Master in Statistics
State University of Campinas - Brasil
Bachelor in Statistics
Universidad Nacional Mayor de San Marcos - Perú
1- (Contributed) Linear regression models with finite mixtures of skew heavy-tailed erros. Oral presentation at 22º SINAPE – Simpósio Nacional de Probabilidade e Estatística (2016, Jul.), Porto Alegre, Brazil.
2- (Contributed) Bivariate Birnbaum-Saunders distribution. Poster presentation at 22º SINAPE – Simpósio Nacional de Probabilidade e Estatístic (2016, Jul.), Porto Alegre, Brazil.
3- (Invited talk) Linear regression models with finite mixtures of skew heavy-tailed erros (2016, Jun.), Departament of Statistics, University of São Paulo, São Paulo, Brazil.
4- (Invited talk) Linear regression models with finite mixtures of skew heavy-tailed erros (2016, May.), Departament of Statistics, National University of San Marcos, Lima, Perú.
5- (Invited talk) Linear regression models with finite mixtures of skew heavy-tailed erros (2016, May.), Departament of Statistics, PUCP, Lima, Perú.
6- (Invited talk) Bivariate Birnbaum-Saunders distribution. (2015, Jul.), Departamento of Statistics, UFMG, Minas Gerais, Brazil.
7- (Contributed) Robust Regression Modeling for Censored Data based on Mixtures of Student-t Distributions. Poster presentation at IV WASA – Workshop em Análise de Sobrevivência e Aplicações (2015, Dez.), Minas Gerais, Brazil.
8- (Contributed) Likelihood Based Inference for Quantile Regression Using the Asymmetric Laplace Distribution. Oral presentation at 21º SINAPE – Simpósio Nacional de Probabilidade e Estatístic (2014, Jul.), Natal, Brazil.
9- (Contributed) Likelihood Based Inference for Quantile Regression Using the Asymmetric Laplace Distribution. Poster presentation at III WASA – Workshop em Análise de Sobrevivência e Aplicações (2013, Dez.), Campinas, São Paulo, Brazil.
Fit censored linear regression models where the random errors follow a finite mixture of Normal or Student-t distributions. Fit censored linear models of finite mixture multivariate Student-t and Normal distributions.
EM algorithm for estimation of parameters and other methods in a quantile regression.
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016)
It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Bottai et.al.(2009)
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
In statistical analysis, particularly in econometrics, the finite mixture of regression models based on the normality assumption is routinely used to analyze censored data. In this work, an extension of this model is proposed by considering scale mixtures of normal distributions (SMN). This approach allows us to model data with great flexibility, accommodating multimodality and heavy tails at the same time. The main virtue of considering the finite mixture of regression models for censored data under the SMN class is that this class of models has a nice hierarchical representation which allows easy implementation of inference. We develop a simple EM-type algorithm to perform maximum likelihood inference of the parameters in the proposed model. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset. The proposed algorithm and methods are implemented in the new R package CensMixReg.
In the framework of censored regression models, the distribution of the error terms departs significantly from normality, for instance, in the presence of heavy tails, skewness and/or atypical observation. In this paper we extend the censored linear regression model with normal errors to the case where the random errors follow a finite mixture of Student-t distributions. This approach allows us to model data with great flexibility, accommodating multimodality, heavy tails and also skewness depending on the structure of the mixture components. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student-t distributions. The efficacy of the method is verified through the analysis of simulated and real datasets. The proposed algorithm and methods are implemented in the new R package CensMixReg.
To make inferences about the shape of a population distribution, the widely popular mean regression model, for example, is inadequate if the distribution is not approximately Gaussian (or symmetric). Compared to conventional mean regression (MR), quantile regression (QR) can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We present a likelihood-based approach to the estimation of the regression quantiles based on the asymmetric Laplace distribution (ALD), which has a hierarchical representation that facilitates the implementation of the EM algorithm for the maximum-likelihood estimation. We develop a case-deletion diagnostic analysis for QR models based on the conditional expectation of the complete-data log-likelihood function related to the EM algorithm. The techniques are illustrated with both simulated and real data sets, showing that our approach out-performed other common classic estimators. The proposed algorithm and methods are implemented in the R package ALDqr.
We consider estimation of regression models whose error terms follow a finite mixture of scale mixtures of skew-normal (SMSN) distributions, a rich class of distributions that contains the skew-normal, skew-t, skew-slash and skew-contaminated normal distributions as proper elements. This approach allows us to model data with great flexibility, accommodating simultaneously multimodality, skewness and heavy tails. We developed a simple EM-type algorithm to perform maximum likelihood (ML) inference of the parameters of the proposed model with closed-form expression at the E-step. Furthermore, the standard errors of the ML estimates can be obtained as a byproduct. The practical utility of the new method is illustrated with the analysis of real dataset and several simulation studies. The proposed algorithm and methods are implemented in the R package FMsmsnReg().
Reaction time (RT) is one of the most common types of measure used in experimental psychology. Its distribution is not normal (Gaussian) but resembles a convolution of normal and exponential distributions (Ex-Gaussian). One of the major assumptions in parametric tests (such as ANOVAs) is that variables are normally distributed. Hence, it is acknowledged by many that the normality assumption is not met. This paper presents different procedures to normalize data sampled from an Ex-Gaussian distribution in such a way that they are suitable for parametric tests based on the normality assumption. Using simulation studies, various outlier elimination and transformation procedures were tested against the level of normality they provide. The results suggest that the transformation methods are better than elimination methods in normalizing positively skewed data and the more skewed the distribution then the transformation methods are more effective in normalizing such data. Specifically, transformation with parameter lambda -1 leads to the best results.
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I would be happy to talk to you if you need my assistance in your research.