題 目：Functional Principal Component Analysis for
Generalized Quantile Regression
主講人：Prof. Dr. Wolfgang Karl Hardle
時 間：101年3月15日(星期四)下午14:00-14:50
地 點：交大綜合一館427室
Abstract
Both quantile regression and expectile regression are called the generalized
quantile regression. With a transformation, expectile regression is a special
case of quantile regression. Traditional generalized quantile regression
focuses on a single curve. When several random curves are available, we can
estimate the single curves by using the information from all the observations
instead of individually. With a novelty method functional principle component
analysis (FPCA) combining least asymmetric weighted squares (LAWS), we
estimate both the mean curve as the common factor curve and the departure
curves which measure the distance for each curve from the mean curve of the
generalized quantile curves via a penalized spline smoothing. We run both
simulations and real data analysis to investigate the performance of the FPCA
method in comparison with the traditional single curve estimation method.
Taking the temperature as an example, we estimate the generalized quantile
curves for the variation of the temperature in 30 cities in Germany for 2002
and 2006 via the FPCA method to analyze the different risk drivers for the
temperature.
※ 編輯: cilar 來自: 122.117.193.35 (03/12 01:37)