Statistical inference on series of atmospheric chemistry data

被引:0
作者
Mohapl, J [1 ]
机构
[1] Environm Canada, Atomospher Environm Serv, ARQM, Toronto, ON M3H 5T4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
non-stationary time series; stochastic differential equation; Ito equation; ANOVA for correlated data; multivariate random processes; maximum likelihood estimation;
D O I
10.1023/A:1026571515769
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study is motivated by a problem concerning comparison of atmospheric chemistry data obtained from two different measurement networks. The data describe pollutant concentrations in air. One network analyzes gases and particles captured on filters daily, the other weekly. The networks use similar but not identical instrumentation and measurement methods. Chemical concentrations form usually non-stationary time series. The goal of the paper is to present a multivariate linear Gaussian model with time dependent correlation matrix for description of such series, a method for parameter estimation and fitting of the model and the procedure used for inference about the mentioned data.
引用
收藏
页码:357 / 384
页数:28
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