Nonlinear Additive Models for Environmental Time Series, with Applications to Ground-Level Ozone Data Analysis
作者:
Xu-Feng Niu,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 435
页码: 1310-1321
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10477000
出版商: Taylor & Francis Group
关键词: Autoregressive moving average processes;Backfitting algorithms;Box-Jenkins modeling procedure;Smoothing splines
数据来源: Taylor
摘要:
Environmental time series usually vary systematically in response to meteorological conditions and thus often are not stationary. In this article a class of additive models are introduced for environmental time series, in which both mean levels and variances of the series are nonlinear functions of relevant meteorological variables. Backfitting algorithms in nonlinear regression are adopted to estimate the unknown functions in the model, and the maximum likelihood method is used to estimate the parameters in the noise component. Asymptotic properties of the parameter estimates, including consistency and limiting distribution, are derived under mild conditions. The model is applied to daily maxima of ground-level ozone concentrations in the Chicago area for possible long-term trend assessment. Compared to alternative models, the proposed models gave more accurate estimations for the 95th and 99th percentiles of the ozone distribution.
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