منابع مشابه
Quantile Regression for Longitudinal Data
The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of “fixed effects”. The introduction of a large number of individual fixed effects can significantly inflate the variability of estimates of other covariate effects. Regularization, or shrinkage of these individual effects toward...
متن کاملBayesian Quantile Regression with Adaptive Elastic Net Penalty for Longitudinal Data
Longitudinal studies include the important parts of epidemiological surveys, clinical trials and social studies. In longitudinal studies, measurement of the responses is conducted repeatedly through time. Often, the main goal is to characterize the change in responses over time and the factors that influence the change. Recently, to analyze this kind of data, quantile regression has been taken ...
متن کاملSemi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses
Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...
متن کاملQuantile Regression Estimation of Nonlinear Longitudinal Data
This paper examines a weighted version of the quantile regression estimator defined by Koenker and Bassett (1978), adjusted to the case of nonlinear longitudinal data. Different weights are used and compared by computer simulation using a four-parameter logistic growth function and error terms following an AR(1) model. It is found that the estimator is performing quite well, especially for the ...
متن کاملA New Nonparametric Regression for Longitudinal Data
In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2004
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2004.05.006