Estimation of mixing proportions in the presence of autoregressively correlated training data:the case of two univariate normal populations
作者:
C.R.O Lawoko,
G.J Mclachlan,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1994)
卷期:
Volume 23,
issue 3
页码: 591-613
ISSN:0361-0918
年代: 1994
DOI:10.1080/03610919408813189
出版商: Marcel Dekker, Inc.
关键词: Classification;mixture distributions;EM algorithm;autoregressive time series;asymptotic expansions;simulation
数据来源: Taylor
摘要:
This paper considers the estimation of mixing proportions when, in addition to the mixture sample, there are available autoregressively dependent data of known origin from each of the classes which make up the mixture population. An asymptotic variance of the usual discriminant analysis (or confusion matrix) estimator of the mixing proportion is obtained under this condition of correlated training data. The maximum likelihood estimator of the mixing proportion and the associated asymptotic variance are also obtained. A simulation experiment is used to investigate the behaviour of these estimators
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