JSM 2004 - Toronto

Abstract #301069

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Activity Number: 20
Type: Contributed
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #301069
Title: Analyzing Censored Data Based on Autoregressive Models
Author(s): Jung Wook Park*+ and Sujit K. Ghosh and Marc G. Genton
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: Dept of Statistics, Raleigh, NC, 27695-8203,
Keywords: censored data ; imputation ; time series ; truncated data
Abstract:

Time series measurements are often observed with data irregularities, such as truncation (detection limit) or censoring. Practitioners often disregard censored-data cases which often result into biased estimates. We present an attractive remedy for handling censored or truncated data based on a class of autoregressive models. In particular, we introduce an imputation method particularly well-suited to fitting autoregressive models in the presence of censored data. We demonstrate the effectiveness of the technique for a problem common to many time series data and describe its adaptation to several other frequently encounted situations. For pedagogic purposes, our illustration of the approach based on a simulation study is limited to a simple AR(1) truncated data problem, but its potential for use beyond this problem is apparent.

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