Link Function for Quantile Estimation in Regression Settings

This paper studies the quantile estimation by using the link function under a broad family of asymmetric densities known as a generalized quantile-based asymmetric family. We proposed a link function and quantile estimation in regression settings. The estimator’s asymptotic properties of the estimators are also discussed here. To demonstrate the proposed methods for estimating the quantile function, an actual data application including the proportion of daily SARS-Cov-2 infected persons tested for COVID-19 infection and meteorological factors such as temperature and humidity is included. We discovered that the amount of daily SARS-Cov-2 infected people tested for COVID-19 infection is significantly influenced by temperature and humidity.