Jean Navarrete - Academia.edu (original) (raw)

Jean Navarrete

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Graduate Center of the City University of New York

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Papers by Jean Navarrete

Research paper thumbnail of Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile

Mathematics

Extensive research has been conducted on models that utilize the Kumaraswamy distribution to desc... more Extensive research has been conducted on models that utilize the Kumaraswamy distribution to describe continuous variables with bounded support. In this study, we examine the trapezoidal Kumaraswamy model. Our objective is to propose a parameter estimation method for this model using the stochastic expectation maximization algorithm, which effectively tackles the challenges commonly encountered in the traditional expectation maximization algorithm. We then apply our results to the modeling of daily COVID-19 cases in Chile.

Research paper thumbnail of Supervised Learning Algorithm for Predicting Mortality Risk in Older Adults Using Cardiovascular Health Study Dataset

Applied Sciences

Multiple chronic conditions are an important factor influencing mortality in older adults. At the... more Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of al...

Research paper thumbnail of A Type I Generalized Logistic Distribution: Solving Its Estimation Problems with a Bayesian Approach and Numerical Applications Based on Simulated and Engineering Data

Symmetry, 2022

The family of logistic type distributions has been widely studied and applied in the literature. ... more The family of logistic type distributions has been widely studied and applied in the literature. However, certain estimation problems exist in some members of this family. Particularly, the three-parameter type I generalized logistic distribution presents these problems, where the parameter space must be restricted for the existence of their maximum likelihood estimators. In this paper, motivated by the complexities that arise in the inference under the likelihood approach utilizing this distribution, we propose a Bayesian approach to solve these problems. A simulation study is carried out to assess the performance of some posterior distributional characteristics, such as the mean, using Monte Carlo Markov chain methods. To illustrate the potentiality of the Bayesian estimation in the three-parameter type I generalized logistic distribution, we apply the proposed method to real-world data related to the copper metallurgical engineering area.

Research paper thumbnail of Estimation and prediction of time-varying GARCH models through a state-space representation: a computational approach

Journal of Statistical Computation and Simulation, 2017

Research paper thumbnail of Conditional Predictive Inference for Beta Regression Model with Autoregressive Errors

Springer Proceedings in Mathematics & Statistics, 2015

In this chapter, we study a partially linear model with autoregressive beta distributed errors [6... more In this chapter, we study a partially linear model with autoregressive beta distributed errors [6] from the Bayesian point of view. Our proposal also provides a useful method to determine the optimal order of the autoregressive processes through an adaptive procedure using the conditional predictive ordinate (CPO) statistic [9].

Research paper thumbnail of Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile

Mathematics

Extensive research has been conducted on models that utilize the Kumaraswamy distribution to desc... more Extensive research has been conducted on models that utilize the Kumaraswamy distribution to describe continuous variables with bounded support. In this study, we examine the trapezoidal Kumaraswamy model. Our objective is to propose a parameter estimation method for this model using the stochastic expectation maximization algorithm, which effectively tackles the challenges commonly encountered in the traditional expectation maximization algorithm. We then apply our results to the modeling of daily COVID-19 cases in Chile.

Research paper thumbnail of Supervised Learning Algorithm for Predicting Mortality Risk in Older Adults Using Cardiovascular Health Study Dataset

Applied Sciences

Multiple chronic conditions are an important factor influencing mortality in older adults. At the... more Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of al...

Research paper thumbnail of A Type I Generalized Logistic Distribution: Solving Its Estimation Problems with a Bayesian Approach and Numerical Applications Based on Simulated and Engineering Data

Symmetry, 2022

The family of logistic type distributions has been widely studied and applied in the literature. ... more The family of logistic type distributions has been widely studied and applied in the literature. However, certain estimation problems exist in some members of this family. Particularly, the three-parameter type I generalized logistic distribution presents these problems, where the parameter space must be restricted for the existence of their maximum likelihood estimators. In this paper, motivated by the complexities that arise in the inference under the likelihood approach utilizing this distribution, we propose a Bayesian approach to solve these problems. A simulation study is carried out to assess the performance of some posterior distributional characteristics, such as the mean, using Monte Carlo Markov chain methods. To illustrate the potentiality of the Bayesian estimation in the three-parameter type I generalized logistic distribution, we apply the proposed method to real-world data related to the copper metallurgical engineering area.

Research paper thumbnail of Estimation and prediction of time-varying GARCH models through a state-space representation: a computational approach

Journal of Statistical Computation and Simulation, 2017

Research paper thumbnail of Conditional Predictive Inference for Beta Regression Model with Autoregressive Errors

Springer Proceedings in Mathematics & Statistics, 2015

In this chapter, we study a partially linear model with autoregressive beta distributed errors [6... more In this chapter, we study a partially linear model with autoregressive beta distributed errors [6] from the Bayesian point of view. Our proposal also provides a useful method to determine the optimal order of the autoregressive processes through an adaptive procedure using the conditional predictive ordinate (CPO) statistic [9].

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