Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/76459
Title: Estimation and diagnostic tools in reparameterized slashed Rayleigh regression model: an application to chemical data
Authors: Gallardo, Diego I.
Gómez Déniz, Emilio 
Leão, Jeremias
Gómez, Héctor W.
UNESCO Clasification: 530202 Modelos econométricos
Keywords: Expectation Maximization Algorithm
Linear Regression
Monte Carlo Simulation
Reparameterized Rayleigh Distribution
Issue Date: 2020
Project: Aportaciones A la Toma de Decisiones Bayesianas Óptimas: Aplicaciones Al Coste-Efectividad Con Datos Clínicos y Al Análisis de Riestos Con Datos Acturiales. 
Journal: Chemometrics and Intelligent Laboratory Systems 
Abstract: In this paper, we introduce a regression model where the response variable is reparameterized slashed Rayleigh (RSR) distributed and which is indexed by mean and precision parameters. The proposed regression model is useful for situations where the variable of interest is continuous and restricted to the positive real line and is related to other variables through the mean and precision parameters. In addition, the RSR model has properties that its competitor distributions of the exponential family do not have. Estimation is performed by expectation maximization (EM) and extensions. Furthermore, we discuss residuals and influence diagnostic tools. Finally, we also carry out two applications to real-world data that demonstrate the usefulness of the proposed model.
URI: http://hdl.handle.net/10553/76459
ISSN: 0169-7439
DOI: 10.1016/j.chemolab.2020.104189
Source: Chemometrics and Intelligent Laboratory Systems [ISSN 0169-7439], v. 207, 104189, (Diciembre 2020)
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