University of Illinois Urbana-Champaign

Quantile autoregression with censored data

Choi, Seokwoo

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Seokwoo_Choi.pdf

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https://hdl.handle.net/2142/50583

Description

Title
Quantile autoregression with censored data
Author(s)
Choi, Seokwoo
Issue Date
2014-09-16
Director of Research (if dissertation) or Advisor (if thesis)
Portnoy, Stephen L.
Doctoral Committee Chair(s)
Portnoy, Stephen L.
Committee Member(s)
  • Monrad, Ditlev
  • Koenker, Roger W.
  • Chen, Xiaohui
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2014-09-16T17:24:08Z
Keyword(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.
Graduation Semester
2014-08
Permalink
http://hdl.handle.net/2142/50583
Copyright and License Information
Copyright 2014 Seokwoo Choi

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