A log-extended Weibull regression model

A bathtub-shaped failure rate function is very useful in survival analysis and reliability studies. The well-known lifetime distributions do not have this property. For the first time, we propose a location-scale regression model based on the logarithm of an extended Weibull distribution which has the ability to deal with bathtub-shaped failure rate functions. We use the method of maximum likelihood to estimate the model parameters and some inferential procedures are presented. We reanalyze a real data set under the new model and the log-modified Weibull regression model. We perform a model check based on martingale-type residuals and generated envelopes and the statistics AIC and BIC to select appropriate models. (C) 2009 Elsevier B.V. All rights reserved.

A Model for comparing reduced impact logging with conventional logging for an Eastern Amazonian Forest

Using data from a logging experiment in the eastern Brazilian Amazon region, we develop a matrix growth and yield model that captures the dynamic effects of harvest system choice on forest structure and composition. Multinomial logistic regression is used to estimate the growth transition parameters for a 10-year time step, while a Poisson regression model is used to estimate recruitment parameters. The model is designed to be easily integrated with an economic model of decisionmaking to perform tropical forest policy analysis. The model is used to compare the long-run structure and composition of a stand arising from the choice of implementing either conventional logging techniques or more carefully planned and executed reduced-impact logging (RIL) techniques, contrasted against a baseline projection of an unlogged forest. Results from log and leave scenarios show that a stand logged according to Brazilian management requirements will require well over 120 years to recover its initial commercial volume, regardless of logging technique employed. Implementing RIL, however, accelerates this recovery. Scenarios imposing a 40-year cutting cycle raise the possibility of sustainable harvest volumes, although at significantly lower levels than is implied by current regulations. Meeting current Brazilian forest policy goals may require an increase in the planned total area of permanent production forest or the widespread adoption of silvicultural practices that increase stand recovery and volume accumulation rates after RIL harvests. Published by Elsevier B.V.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Size and power properties of some tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the non-null distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the Birnbaum-Saunders regression model. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the shape parameter. Monte Carlo simulation is presented in order to compare the finite-sample performance of these tests. We also present two empirical applications. (C) 2010 Elsevier B.V. All rights reserved.

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented. (C) 2010 Elsevier B.V. All rights reserved.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Beta-binomial/Poisson regression models for repeated bivariate counts

We analyze data obtained from a study designed to evaluate training effects on the performance of certain motor activities of Parkinson`s disease patients. Maximum likelihood methods were used to fit beta-binomial/Poisson regression models tailored to evaluate the effects of training on the numbers of attempted and successful specified manual movements in 1 min periods, controlling for disease stage and use of the preferred hand. We extend models previously considered by other authors in univariate settings to account for the repeated measures nature of the data. The results suggest that the expected number of attempts and successes increase with training, except for patients with advanced stages of the disease using the non-preferred hand. Copyright (c) 2008 John Wiley & Sons, Ltd.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.

Mortalidade cardiovascular por sexo e faixa etária em São Paulo, Brasil: 1996 a 1998 e 2003 a 2005

FUNDAMENTO: O conhecimento da evolução da mortalidade cardiovascular é importante para levantar hipóteses sobre a sua ocorrência e subsidiar medidas de prevenção e controle. OBJETIVOS: Comparar a mortalidade pelo conjunto das doenças cardiovasculares e seus principais subgrupos: doença isquêmica do coração e cerebrovasculares (DIC e DCBV), no município de São Paulo, por sexo e idade, de 1996 a 1998 e 2003 a 2005. MÉTODOS: Foram usados dados de óbitos do Programa de Aprimoramento das Informações de Mortalidade para o Município (PROAIM) e estimativas populacionais da Fundação Sistema Estadual de Análise de Dados (SEADE) do Estado de São Paulo. A magnitude na mortalidade e as mudanças entre os triênios foram medidas pela descrição de coeficientes e variação percentual relativa. O modelo de regressão de Poisson foi usado também para estimar a mudança na mortalidade entre os períodos. RESULTADOS: Observou-se redução importante da mortalidade cardiovascular. Os coeficientes aumentam com a idade em ambos os sexos. Também são mais elevados na população masculina, na faixa a partir dos 70 anos. Os coeficientes de mortalidade por DIC são maiores que aqueles por DCBV, tanto nos homens como nas mulheres de 50 anos ou mais. O declínio pelo conjunto das doenças cardiovasculares foi maior em mulheres de 20 a 29 anos (-30%) e em homens de 30 a 39 anos (-26%). CONCLUSÃO: A força da intensidade da mortalidade cardiovascular diminuiu entre 1996 e 1998, a 2003 e 2005. Ainda assim há diferenças entre os grupos. Essa redução pode significar, em parte, um maior acesso aos métodos diagnósticos e terapêuticos.

Anxiety Disorders and Rheumatic Fever: Is There an Association?

Introduction: Findings suggest that obsessive-compulsi e disorder (OCD) and related disorders, referred to as obsessive-compulsive spectrum disorders (OCSDs), are more common in patients with rheumatic fever (RF). Objectives: To determine whether RF or Sydenham`s chorea increases the probability of anxiety disorders in the relatives of individuals with RF with and without SC. Methods: This was a case-Control family study in which 98 probands and 389 first-degree relatives (FDRs) were assessed using structured psychiatric interviews. A Poisson regression model was used to determine whether the presence of any disorder in one family member influences the rate of disorders in the remaining family members. Results: Generalized anxiety disorder (GAD) occurred more frequently in the FDRs of RF probands than in those of control probands (P=.018). The presence of RF,GAD, or separation anxiety disorder in one family member significantly increased the chance of OCSDs in another member of the family.

Práticas de alimentação complementar no primeiro ano de vida e fatores associados

OBJETIVO: Investigar a prevalência de consumo de alimentos complementares e os fatores associados à alimentação complementar oportuna em menores de um ano. MÉTODOS: Participaram do estudo 1 176 crianças, durante a Campanha Nacional de Vacinação de 2003, em São Bernardo do Campo (SP), cujos acompanhantes responderam questionário que incluiu questões sobre a alimentação da criança nas 24 horas precedentes. A estimativa da prevalência de consumo dos alimentos complementares foi realizada por um modelo de regressão logística ajustado por idade; as medianas de introdução de alimentos por análise de sobrevida e os fatores associados à alimentação complementar oportuna por regressão de Poisson com ajuste robusto de variância e seleção hierarquizada de variáveis. RESULTADOS: Observou-se introdução precoce de alimentos complementares: no quarto mês, cerca de um terço das crianças recebiam suco de fruta e um quarto das crianças recebiam mingau, fruta ou sopa, ao passo que a probabilidade de consumir a comida da família aos oito meses foi baixa (48%). A mediana de idade para o consumo de frutas foi de 266 dias (IC95% 256-275), de papa de legumes foi 258 dias (IC95% 250-264) e comida da família, 292 dias (IC 95% 287-303). Os fatores associados ao consumo de alimentos sólidos antes dos seis meses de idade foram: sistema de assistência à saúde; idade materna; trabalho materno e uso de chupeta. CONCLUSÃO: O consumo precoce de alimentos sólidos, um risco potencial para a saúde infantil e para o desenvolvimento de doenças crônicas na idade adulta, evidenciam a necessidade de ações programáticas para reversão deste quadro.