1,721,461 research outputs found
Stima del numero delle nuove imprese con un modello integrato di campionamento areale ed esperimenti di cattura-ricattura
No full textIstituto Economico Aziendal
Parametric modeling of quantile regression coefficient functions
No full textEstimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile regression model depend on the order of the quantile being estimated. For example, the coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the regression coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package
An estimating equation for censored and truncated quantile regression
No full textAn estimation equation for censored, truncated quantile regression is introduced. The asymptotic covariance matrix has a relatively simple expression and can be estimated from the data. Simulation results are presented, and the described estimator is used to evaluate the effects of birth weight on percentiles of survival time after age 65 with a population-based cohort of Swedish men. The proposed method is efficiently implemented in the R package ctqr
Nuovi modelli di sviluppo demografico dei comuni toscani
No full textRivista "La congiuntura in Toscana
Conditional concordance of the signs of the residuals of quantile regressions of multivariate outcomes
No full text
Cancer cluster detection in semi-parametric models with random effects: A score-based testing approach
No full text
Nonlinear parametric quantile models
No full textQuantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper presents the general case of nonlinear parametric quantile models. These can be nonlinear with respect to the parameters, the covariates, or both. Some important features and asymptotic properties of the proposed estimator are described, and its finite-sample behavior is assessed in a simulation study. Nonlinear parametric quantile models are applied to estimate extreme quantiles of longitudinal measures of respiratory mechanics in asthmatic children from an epidemiological study and to evaluate a dose–response relationship in a toxicological laboratory experiment
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