A new log-location regression model: estimation,influence diagnostics and residual analysis |
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Authors: | Rodrigo R Pescim Edwin M M Ortega Gauss M Cordeiro Morad Alizadeh |
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Institution: | 1. Departamento de Estatística, Universidade Estadual de Londrina, Londrina, PR, Brazilrrpescim@gmail.com;3. Departamento de Ciências Exatas, Universidade de S?o Paulo, Piracicaba, SP, Brazil;4. Departamento de Estatística, Universidade Federal de Pernambuco, Recife, PE, Brazil;5. Department of Statistics, Persian Gulf University, Bushehr, Iran |
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Abstract: | We introduce a log-linear regression model based on the odd log-logistic generalized half-normal distribution 7 G.M. Cordeiro, M. Alizadeh, R.R. Pescim, and E.M.M. Ortega, The odd log-logistic generalized half-normal lifetime distribution: Properties and applications, Comm. Statist. Theory Methods (2015), accepted for publication. Google Scholar]]. Some of its structural properties including explicit expressions for the density function, quantile and generating functions and ordinary moments are derived. We estimate the model parameters by the maximum likelihood method. For different parameter settings, proportion of censoring and sample size, some simulations are performed to investigate the behavior of the estimators. We derive the appropriate matrices for assessing local influence diagnostics on the parameter estimates under different perturbation schemes. We also define the martingale and modified deviance residuals to detect outliers and evaluate the model assumptions. In addition, we demonstrate that the extended regression model can be very useful in the analysis of real data and provide more realistic fits than other special regression models. The potentiality of the new regression model is illustrated by means of a real data set. |
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Keywords: | Censored data generalized half-normal distribution Monte Carlo simulation oddlog-logistic generalized family regression model residual analysis |
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