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Censored time series analysis with autoregressive moving average models
Author(s) -
Park Jung Wook,
Genton Marc G.,
Ghosh Sujit K.
Publication year - 2007
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550350113
Subject(s) - autoregressive model , autoregressive–moving average model , autocorrelation , censoring (clinical trials) , time series , computer science , statistics , moving average , econometrics , series (stratigraphy) , autoregressive integrated moving average , imputation (statistics) , context (archaeology) , mathematics , missing data , geography , paleontology , archaeology , biology
Abstract The authors consider time series observations with data irregularities such as censoring due to a detection limit. Practitioners commonly disregard censored data cases which often result in biased estimates. The authors present an attractive remedy for handling autocorrelated censored data based on a class of autoregressive and moving average (ARMA) models. In particular, they introduce an imputation method well suited for fitting ARMA models in the presence of censored data. They demonstrate the effectiveness of their technique in terms of bias, efficiency, and information loss. They also describe its adaptation to a specific context of meteorological time series data on cloud ceiling height, which are measured subject to the detection limit of the recording device.