Estimating 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 ...

norm <- 1/1e7 # time steps are included in the data itself, so no need to divide the number of days labelling_char<-c() for (i_light in c(1:length(unique(Conditional_incidence_quantiles[[variable]]))) ...

Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Winship Cancer Institute, Atlanta, USA. Department of Biostatistics and Computational Biology University of Rochester, ...

Abstract: We propose a class of alternative stochastic volatility models for electricity prices using the quantile function modeling approach. Specifically, we fit marginal distributions of power ...

A smooth alternative to the conventional sample quantile function as a nonparametric estimator of a population quantile function is proposed. The proposed estimator is essentially a kernel type of ...

ABSTRACT: In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite ...

Abstract: In many areas of science, technological advances have led to devices that produce data streams consisting of an enormous number of measurements per subject, including wearable devices, ...

A compound Poisson distribution is the sum of independent and identically distributed random variables over a count variable that follows a Poisson distribution. Generally, this distribution is not ...