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Diagnostic checking ARMA time series models using squared-residual autocorrelations
A. I. MCLEOD,
Wai Keung LI
Department of Mathematics and Information Technology (MIT)
Research output
:
Contribution to journal
›
Articles
›
peer-review
715
Citations (Scopus)
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Dive into the research topics of 'Diagnostic checking ARMA time series models using squared-residual autocorrelations'. Together they form a unique fingerprint.
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Keyphrases
Time Series Model
100%
Diagnostic Checking
100%
Residual Autocorrelation
100%
Autoregressive Moving Average
100%
Copyright
33%
Miller
33%
Simulation Experiment
33%
Granger
33%
Autoregressive Integrated Moving Average (ARIMA)
33%
Multivariate Normal
33%
Sample Validity
33%
Statistical Dependence
33%
Mathematics
Residuals
100%
Autocorrelation
100%
Time Series Model
100%
Moving Average
100%
Autoregressive Moving
100%
Smaller Sample
25%
Autoregressive Moving Average Model
25%
Multivariate Normal
25%
Statistical Dependence
25%
Nursing and Health Professions
Diagnosis
100%
Time Series Analysis
100%