The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
PERIODIC AUTOREGRESSIVE‐MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES1
Abstract
Results involving correlation properties and parameter estimation for autoregressive‐moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum‐likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.
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Citations
Cites
Powell, M. J. D. (1964) · The Computer Journal · doi:10.1093/comjnl/7.2.155 · Cited by 4,632
Cited by
Spectral Correlation of Modulated Signals: Part I--Analog Modulation
Gardner, W. (1987) · IEEE Transactions on Communications · doi:10.1109/tcom.1987.1096820 · Cited by 302
Metadata
- Authors
- Vecchia, A. V.
- Venue
- JAWRA Journal of the American Water Resources Association
- Publisher
- Wiley
- Published
- 1985-10
- Volume
- 21
- Issue
- 5
- Pages
- 721-730
- ISSN
- 1093-474X (ISSN-L)
- 1752-1688
- OpenAlex
- W1992488239
- Cited by
- 183
- References
- 1
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: PERIODIC AUTOREGRESSIVE‐MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES1. https://app.cochat.ai/papers/10.1111/j.1752-1688.1985.tb00167.x
- The paper
- Vecchia, A. V. (1985). PERIODIC AUTOREGRESSIVE‐MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES1. JAWRA Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.1985.tb00167.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
