Influence diagnostics in generalized log-gamma regression models

Ortega, Edwin M. M.; Bolfarine, Heleno; Paula, Gilberto A.

doi:10.1016/s0167-9473(02)00104-4

Cited by 99 publications

(58 citation statements)

References 26 publications

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“…Previous works on local influence curvatures in regression models for censored data are due to (Escobar & Meeker, 1992), (Ortega, Bolfarine & Paula, 2003), (Ortega, Cancho & Paula, 2009), (Ortega, Cordeiro & Hashimoto, 2011), (Silva, Ortega, Garibay & Barreto, 2008) and (Hashimoto, Ortega, Cancho, & Cordeiro, 2013). We consider model (7) and its log-likelihood function given by (8).…”

Section: Local Influencementioning

confidence: 99%

Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

Santana

Ortega²,

Cordeiro³

et al. 2017

IJSP

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A new regression model based on the exponentiated Weibull with the structure distribution and the structure of the generalized linear model, called the generalized exponentiated Weibull linear model (GEWLM), is proposed. The GEWLM is composed by three important structural parts: the random component, characterized by the distribution of the response variable; the systematic component, which includes the explanatory variables in the model by means of a linear structure; and a link function, which connects the systematic and random parts of the model. Explicit expressions for the logarithm of the likelihood function, score vector and observed and expected information matrices are presented. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. To detect influential observations in the new model, we use diagnostic measures based on the local influence and Bayesian case influence diagnostics. Also, we show that the estimates of the GEWLM are robust to deal with the presence of outliers in the data. Additionally, to check whether the model supports its assumptions, to detect atypical observations and to verify the goodness-of-fit of the regression model, we define residuals based on the quantile function, and perform a Monte Carlo simulation study to construct confidence bands from the generated envelopes. We apply the new model to a dataset from the insurance area.

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Section: Local Influencementioning

confidence: 99%

Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

Santana

Ortega²,

Cordeiro³

et al. 2017

IJSP

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“…The perturbation scheme used had been proposed by Ortega et al (2002), as presented in the equation (8) below (8) where, z max * is the new value maximum of the vector and z max is the maximum value of vector z;…”

Section: Simulation Studymentioning

confidence: 99%

Diagnostic techniques applied in geostatistics for agricultural data analysis

Borssoi

Uribe-Opazo

Rojas

2009

Rev. Bras. Ciênc. Solo

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SUMMARYThe structural modeling of spatial dependence, using a geostatistical approach, is an indispensable tool to determine parameters that define this structure, applied on interpolation of values at unsampled points by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations in sampled data. The purpose of this study was to use diagnostic techniques in Gaussian spatial linear models in geostatistics to evaluate the sensitivity of maximum likelihood and restrict maximum likelihood estimators to small perturbations in these data. For this purpose, studies with simulated and experimental data were conducted. Results with simulated data showed that the diagnostic techniques were efficient to identify the perturbation in data. The results with real data indicated that atypical values among the sampled data may have a strong influence on thematic maps, thus changing the spatial dependence structure. The application of diagnostic techniques should be part of any geostatistical analysis, to ensure a better quality of the information from thematic maps.Index terms: local influence, maximum likelihood, restricted maximum likelihood.(

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“…Furthermore, in order to provide an indication of bad model fitting or influential observations, an influential analysis was made, see for example (Ortega et al 2003;Fachini et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis

Santos

Granzotto

Tomazella

et al. 2018

JRFM

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Abstract:In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. Also, the TLL model was formulated by using the quadratic transmutation map, that is a simple way of derivating new distributions, and it adds a new parameter λ, which one introduces a skewness in the new distribution and preserves the moments of the baseline model. The Bayesian model was formulated by using the half-Cauchy prior which is an alternative prior to a inverse Gamma distribution. In order to fit the model, a real data set, which consist of the time up to first calving of polled Tabapua race, was used. Finally, after the model was fitted, an influential analysis was made and excluding only 0.1% of observations (influential points), the reestimated model can fit the data better.

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Influence diagnostics in generalized log-gamma regression models

Cited by 99 publications

References 26 publications

Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

Diagnostic techniques applied in geostatistics for agricultural data analysis

Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis

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