Bayesian prediction of mean square errors with covariates
By: Donald P. Gaver, Patricia A. Jacobs
Contributors: Jacobs, Patricia A.; Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1992
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 34
Subjects: MATHEMATICAL MODELS; MATHEMATICAL  PREDICTION; COVARIANCE; STATISTICAL ANALYSIS
Other titles: NPS-OR-93-004.
Description: Estimation of mean square prediction error of wind components is required in the optimal      interpolation (OI) process in numerical prediction of atmospheric variables. Previous work      has suggested that statistical models with log-linear scale parameters which include      covariates can be used to predict mean square prediction errors. However, the parameters      of the statistical relationships appear to change over time. A procedure is described to      recursively update the estimated parameters. Data from July of 1991 are used to fit the      model parameters and to study the predictive ability of the recursive procedure. This      preliminary investigation indicates that observational and first guess wind components can be      helpful in predicting mean square prediction error for wind components.... Hierarchical      model, Gaussian model with log-linear scale parameters.
Notes: Cover title.

"NPS-OR-93-004."

"November 1992."

AD A259 585.

Includes bibliographical references (p.16-17).

aq/aq cc:9116 07/08/97