Estimation of P( Z < Y) for correlated stochastic time series models

Let Z and Y represent two time series that are not necessarily independent, and Z n+ L , Y m+ k denote their values respectively at future times n+ L and m+ k, where n+ L= m+ k. Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA) models are employed both under stationa...

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Bibliographic Details
Published in:Applied mathematics and computation 1999, Vol.104 (2), p.179-189
Main Author: Aminzadeh, Mostafa S.
Format: Article
Language:English
Subjects:
Autoregressive models
Bivariate normal distribution
Exact sciences and technology
Inference from stochastic processes
time series analysis
Mathematics
Moving average models
Multivariate analysis
Probability and statistics
Reliability
Sciences and techniques of general use
Stationary and non-stationary stochastic process
Statistics
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Summary:Let Z and Y represent two time series that are not necessarily independent, and Z n+ L , Y m+ k denote their values respectively at future times n+ L and m+ k, where n+ L= m+ k. Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA) models are employed both under stationary and non-stationary conditions to estimate P(Z n+L
ISSN:0096-3003
1873-5649
DOI:10.1016/S0096-3003(98)10072-3