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The generalized log-gamma mixture model with covariates: local influence and residual analysis

ORTEGA, Edwin M. M.; RIZZATO, Fernanda B.; DEMETRIO, Clarice G. B.
Fonte: SPRINGER HEIDELBERG Publicador: SPRINGER HEIDELBERG
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.14%
In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Influence diagnostics for polyhazard models in the presence of covariates

FACHINI, Juliana B.; ORTEGA, Edwin M. M.; LOUZADA-NETO, Francisco
Fonte: SPRINGER HEIDELBERG Publicador: SPRINGER HEIDELBERG
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.15%
In this paper, we present various diagnostic methods for polyhazard models. Polyhazard models are a flexible family for fitting lifetime data. Their main advantage over the single hazard models, such as the Weibull and the log-logistic models, is to include a large amount of nonmonotone hazard shapes, as bathtub and multimodal curves. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. A discussion of the computation of the likelihood displacement as well as the normal curvature in the local influence method are presented. Finally, an example with real data is given for illustration.

On estimation and influence diagnostics for zero-inflated negative binomial regression models

GARAY, Aldo M.; HASHIMOTO, Elizabeth M.; ORTEGA, Edwin M. M.; LACHOS, Victor H.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
45.95%
The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); CNPq - Brazil

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

CANCHO, Vicente G.; DEY, Dipak K.; LACHOS, Victor; ANDRADE, Marinho G.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.07%
The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.; FAPESP; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); CNPq...

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

CANCHO, Vicente G.; ORTEGA, Edwin M. M.; PAULA, Gilberto A.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.07%
The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.; FAPESP; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influence diagnostics in nonlinear mixed-effects elliptical models

RUSSO, Cibele M.; PAULA, Gilberto A.; AOKI, Reiko
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.15%
In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.; FAPESP; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); CNPq...

Inference and local influence assessment in skew-normal null intercept measurement error model

LACHOS, V. H.; MONTENEGRO, L. C.; BOLFARINE, H.
Fonte: TAYLOR & FRANCIS LTD Publicador: TAYLOR & FRANCIS LTD
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.1%
In this article, we discuss inferential aspects of the measurement error regression models with null intercepts when the unknown quantity x (latent variable) follows a skew normal distribution. We examine first the maximum-likelihood approach to estimation via the EM algorithm by exploring statistical properties of the model considered. Then, the marginal likelihood, the score function and the observed information matrix of the observed quantities are presented allowing direct inference implementation. In order to discuss some diagnostics techniques in this type of models, we derive the appropriate matrices to assessing the local influence on the parameter estimates under different perturbation schemes. The results and methods developed in this paper are illustrated considering part of a real data set used by Hadgu and Koch [1999, Application of generalized estimating equations to a dental randomized clinical trial. Journal of Biopharmaceutical Statistics, 9, 161-178].

Local influence in estimating equations

VENEZUELA, Maria Kelly; SANDOVAL, Monica Carneiro; BOTTER, Denise Aparecida
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.12%
Local influence diagnostics based on estimating equations as the role of a gradient vector derived from any fit function are developed for repeated measures regression analysis. Our proposal generalizes tools used in other studies (Cook, 1986: Cadigan and Farrell, 2002), considering herein local influence diagnostics for a statistical model where estimation involves an estimating equation in which all observations are not necessarily independent of each other. Moreover, the measures of local influence are illustrated with some simulated data sets to assess influential observations. Applications using real data are presented. (C) 2010 Elsevier B.V. All rights reserved.; CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); FAPESP; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Influence Diagnostics for a Skew Extension of the Grubbs Measurement Error Model

MONTENEGRO, Lourdes C.; BOLFARINE, Heleno; LACHOS, Victor H.
Fonte: TAYLOR & FRANCIS INC Publicador: TAYLOR & FRANCIS INC
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.12%
Influence diagnostics methods are extended in this article to the Grubbs model when the unknown quantity x (latent variable) follows a skew-normal distribution. Diagnostic measures are derived from the case-deletion approach and the local influence approach under several perturbation schemes. The observed information matrix to the postulated model and Delta matrices to the corresponding perturbed models are derived. Results obtained for one real data set are reported, illustrating the usefulness of the proposed methodology.; CNPQ/FAPESP/FAPEMIG, Brasil; CNPQ/FAPESP/FAPEMIG, Brasil

Influence diagnostics in Birnbaum-Saunders nonlinear regression models

LEMONTE, Artur J.; PATRIOTA, Alexandre G.
Fonte: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD Publicador: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
45.99%
We consider the issue of assessing influence of observations in the class of Birnbaum-Saunders nonlinear regression models, which is useful in lifetime data analysis. Our results generalize those in Galea et al. [8] which are confined to Birnbaum-Saunders linear regression models. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are discussed. Additionally, the normal curvatures for studying local influence are derived under some perturbation schemes. We also give an application to a real fatigue data set.; FAPESP (Brazil); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A note on influence diagnostics in nonlinear mixed-effects elliptical models

PATRIOTA, Alexandre G.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.1%
This paper provides general matrix formulas for computing the score function, the (expected and observed) Fisher information and the A matrices (required for the assessment of local influence) for a quite general model which includes the one proposed by Russo et al. (2009). Additionally, we also present an expression for the generalized leverage on fixed and random effects. The matrix formulation has notational advantages, since despite the complexity of the postulated model, all general formulas are compact, clear and have nice forms. (C) 2010 Elsevier B.V. All rights reserved.; FAPESP; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

Ortega, Edwin M. M.; Cordeiro, Gauss M.; Lemonte, Artur J.
Fonte: ELSEVIER SCIENCE BV; AMSTERDAM Publicador: ELSEVIER SCIENCE BV; AMSTERDAM
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.07%
The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.; CNPq; CNPq; FAPESP (Brazil); FAPESP (Brazil)

Influence diagnostics in Gaussian spatial linear models

Uribe-Opazo, Miguel Angel; Borssoi, Joelmir André; Galea, Manuel
Fonte: TAYLOR & FRANCIS LTD; ABINGDON Publicador: TAYLOR & FRANCIS LTD; ABINGDON
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.1%
Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmental sciences, among many others. Spatial dependence structure modelling, using a geostatistical approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. The purpose of this paper is to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariance functions and linear predictor to small perturbations in the data and/or the spatial linear model assumptions. The methodology is illustrated with two real data sets. The results allowed us to conclude that the presence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.; Universidad de Valparaiso [DIPUV 11/2006]; Fondecyt, Chile [1070919]; Fundacao Araucaria do State of Parana; CNPq (Brazil)

Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

Paula, Gilberto A.; Leiva, Victor; Barros, Michelli; Liu, Shuangzhe
Fonte: WILEY-BLACKWELL; HOBOKEN Publicador: WILEY-BLACKWELL; HOBOKEN
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.19%
In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model. Copyright (c) 2011 John Wiley & Sons, Ltd.; CNPq [Casadinho 620150/2008-4, Universal-477747/2008-6, PROSUL-490429/2008-4, INCTMat 57.33523/2008-8]; CNPq; FAPESP, Brazil; FAPESP (Brazil); FONDECYT (Chile); FONDECYT, Chile [1080326]

Diagnostico de influencia em modelos de volatilidade estocastica; Influence diagnostics in stochastic volatility models

Simoni Fernanda Martim
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 31/08/2009 PT
Relevância na Pesquisa
56.27%
O diagnóstico de modelos é uma etapa fundamental para avaliar a qualidade do ajuste dos modelos. Nesse sentido, uma das ferramentas de diagnóstico mais importantes é a análise de influência. Peña (2005) introduziu uma forma de analisar a influência em modelos de regressão, a qual avalia como cada ponto é influenciado pelos outros na amostra. Essa estratégia de diagnóstico foi adaptada por Hotta e Motta (2007) na análise de influência dos modelos de volatilidade estocástica univariados. Nesta dissertação, é realizado um estudo de diagnóstico de influência para modelos de volatilidade estocástica univariados assimétricos, assim como para modelos de volatilidade estocástica multivariados. As metodologias propostas são ilustradas através da análise de dados simulados e séries reais de retornos financeiros.; Model diagnostics is a key step to assess the quality of fitted models. In this sense, one of the most important tools is the analysis of influence. Peña (2005) introduced a way of assessing influence in linear regression models, which evaluates how each point is influenced by the others in the sample. This diagnostic strategy was adapted by Hotta and Motta (2007) on the influence analysis of univariate stochastic volatility models. In this dissertation...

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

CANCHO, Vicente G.; DEY, Dipak K.; LACHOS, Victor; ANDRADE, Marinho G.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.02%
The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influence Diagnostics for a Skew Extension of the Grubbs Measurement Error Model

MONTENEGRO, Lourdes C.; BOLFARINE, Heleno; LACHOS, Victor H.
Fonte: TAYLOR & FRANCIS INC Publicador: TAYLOR & FRANCIS INC
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.1%
Influence diagnostics methods are extended in this article to the Grubbs model when the unknown quantity x (latent variable) follows a skew-normal distribution. Diagnostic measures are derived from the case-deletion approach and the local influence approach under several perturbation schemes. The observed information matrix to the postulated model and Delta matrices to the corresponding perturbed models are derived. Results obtained for one real data set are reported, illustrating the usefulness of the proposed methodology.; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation

Suzuki,A.K.; Louzada,F.; Cancho,V.G.
Fonte: Sociedade Brasileira de Matemática Aplicada e Computacional Publicador: Sociedade Brasileira de Matemática Aplicada e Computacional
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2013 EN
Relevância na Pesquisa
66.16%
In this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data.

Bayesian Case Influence Diagnostics for Survival Models

Cho, Hyunsoon; Ibrahim, Joseph G.; Sinha, Debajyoti; Zhu, Hongtu
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.26%
We propose Bayesian case influence diagnostics for complex survival models. We develop case deletion influence diagnostics for both the joint and marginal posterior distributions based on the Kullback–Leibler divergence (K–L divergence). We present a simplified expression for computing the K–L divergence between the posterior with the full data and the posterior based on single case deletion, as well as investigate its relationships to the conditional predictive ordinate. All the computations for the proposed diagnostic measures can be easily done using Markov chain Monte Carlo samples from the full data posterior distribution. We consider the Cox model with a gamma process prior on the cumulative baseline hazard. We also present a theoretical relationship between our case-deletion diagnostics and diagnostics based on Cox’s partial likelihood. A simulated data example and two real data examples are given to demonstrate the methodology.

Influence diagnostics in Birnbaum-Saunders nonlinear regression models

Lemonte, Artur J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 27/04/2009
Relevância na Pesquisa
46.15%
We consider the issue of assessing influence of observations in the class of Birnbaum-Saunders nonlinear regression models, which is useful in lifetime data analysis. Our results generalize those in Galea et al. [2004, Influence diagnostics in log-Birnbaum-Saunders regression models. Journal of Applied Statistics, 31, 1049-1064] which are confined to Birnbaum-Saunders linear regression models. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are discussed. Additionally, the normal curvatures of local influence are derived under various perturbation schemes.; Comment: 10 pages, submitted for publication