表題番号:2018S-189 日付:2019/04/08
研究課題Model Selection for Quantile Regression and its Applications
研究者所属(当時) 資格 氏名
(代表者) 国際学術院 国際教養学部 助手 セツ ギョクケツ
研究成果概要
The least absolute shrinkage and selection operator (LASSO) is proposed by Tibishilani (1996) and is a popular technique for model selection and estimation in linear regression models. It has been shown that the correct subset of relevant variables can be selected efficiently. So far, literature on LASSO has mainly focused on short-memory dependent disturbances or variables which means the covariances of a discrete time stationary stochastic sequence of disturbances or variables decreases to zero as the lag tends to infinity and their absolute sum converges. However, in the fields of hydrology, economics and other sciences, the long-memory sequences arise, which means the absolute sum of covariances diverges compared to short-memory sequences. Thus, this research applies LASSO to the linear quantile regression model with long-memory disturbances. When the dimension of parameters is fixed, the asymptotic distribution of the modified LASSO estimators is derived under certain natural regularity conditions. Furthermore, when the dimension of parameters increases with respect to observation length , the consistency on the probability of correct selection of relevant variables is shown. It is shown that under certain regularity conditions, the probability of correct selection converges to   as the observation length  goes to infinity.