Modeling Satellite Ozone Data
作者:
Xufeng Niu,
GeorgeC. Tiao,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 431
页码: 969-983
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476598
出版商: Taylor & Francis Group
关键词: Circular matrices;Conditional likelihood function;Space-time regression models;Vector AR processes
数据来源: Taylor
摘要:
Starting in the early 1970s, the decline in column ozone over much of the earth has received much attention. Satellite ozone data, with the advantage of global coverage, now play an important role in assessing long-term trends in ozone distributions. We consider a class of space-time regression models for the analysis of satellite data on a fixed latitude, which take into account temporal and longitudinal dependence of the observations. The models can be used to test the uniformity of long-term trends in different longitudinal ozone series. Using the property of circular matrices, explicit expressions of the likelihood functions are obtained. Asymptotic properties of the parameter estimates are briefly discussed. A diagnostic method is proposed to tentatively select the orders in the noise terms of the models. The space-time regression models are applied to the total ozone mapping spectrometer (TOMS) data for trend assessment.
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