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Title:Quantile autoregression with censored data
Author(s):Choi, Seokwoo
Director of Research:Portnoy, Stephen L.
Doctoral Committee Chair(s):Portnoy, Stephen L.
Doctoral Committee Member(s):Monrad, Ditlev; Koenker, Roger W.; Chen, Xiaohui
Department / Program:Statistics
Discipline:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Censored time-series
Autoregression
Quantile
Self-consistent
Kaplan-Meier
Abstract:Quantile autoregression (QAR) provides an alternative way to study asymmetric dynamics and local persistence in time series. It is particularly attractive for censored data, where the classical autoregressive models are unidentifiable without further parametric assumptions on the distributions. There have been prominent works by Powell (1986), Portnoy (2003) and Peng and Huang (2008) on estimating the conditional quantile functions with censored data. However, unlike the standard regression models, the autoregressive models should take account of censoring on both response and regressors. In this dissertation, we show that the existing censored quantile regression methods produce empirically consistent estimator on QAR models when using only observed part of regressors. A new algorithm is proposed to improve a censored quantile autoregression (CQAR) estimator by adopting an idea of imputation methods. The algorithm distributes probability mass of each censored point to any sufficiently large value appropriately, and iterates towards self-consistent solutions. Monte Carlo simulations are conducted to examine the empirical consistency of the CQAR estimator. Also, empirical applications of the algorithm to the Samish river water quality study and dry decomposition of NH4 demonstrate the merits of the proposed method.
Issue Date:2014-09-16
URI:http://hdl.handle.net/2142/50583
Rights Information:Copyright 2014 Seokwoo Choi
Date Available in IDEALS:2014-09-16
Date Deposited:2014-08


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