background: low birth weight is one of the key indicators to assess the health of infants, and appropriate birth weight is one of the most important goals of any health system which also reflects the quality of prenatal care. objectives: the present research aimed to study some of the factors associated with low birth weight using quantile regression analysis. methods: a cross-sectional study w...

background: poverty and low socioeconomic status are the most important reasons of increasing the global burden of tuberculosis, not only in developing countries but also in developed countries for particular groups. the purpose of this study was to assess the association between socioeconomic factors and the number of tuberculosis patients using quantile regression for count data.   methods: t...

the main idea of this paper is to study the dependence between the probability of default and the recovery rate on credit portfolio and to seek empirically this relationship. we examine the dependence between pd and rr by theoretical approach. for the empirically methodology, we use the bootstrapped quantile regression and the simultaneous quantile regression. these methods allow to determinate...

aim : to determine the associated factors of reflux duration, using quantile regression model. background : reflux is one of the most prevalent gastrointestinal disorders.  statistical tools are widely used to assess the associated factors on reflux severity and frequency. patients and methods : a door to door questionnaire has been used to evaluate gastrointestinal symptoms including reflux an...

Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression address the issue of right censoring of the response variable...

Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression address the issue of right censoring of the response variable...

Interrupted Time Series (ITS) analysis represents a powerful quasi-experime-ntal design in which a discontinuity is enforced at a specific intervention point in a time series, and separate regression functions are fitted before and after the intervention point. Segmented linear/quantile regression can be used in ITS designs to isolate intervention effects by estimating the sudden/level change (...

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest...

Quantiles play an essential role in modern statistics, as emphasized by the fundamental work of Parzen (1978) and Tukey (1977). Quantile regression was introduced by Koenker and Bassett (1978) as a complement to least squares estimation (LSE) or maximum likelihood estimation (MLE) and leads to far-reaching extensions of ”classical” regression analysis by estimating families of conditional quant...