Statistical Decision Theory Techniques for the Revision of Mean Flood Flow Regression Estimates | Zendy

Shane Richard M. | Zendy; Gaver Donald P. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Statistical Decision Theory Techniques for the Revision of Mean Flood Flow Regression Estimates

Author(s) -

Shane Richard M.,

Gaver Donald P.

Publication year - 1970

Publication title -

water resources research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.863

H-Index - 217

eISSN - 1944-7973

pISSN - 0043-1397

DOI - 10.1029/wr006i006p01649

Subject(s) - statistics , mathematics , regression , linear regression , regression analysis , mean squared error , bayesian linear regression , regression dilution , flood myth , regression toward the mean , bayesian probability , bayesian multivariate linear regression , econometrics , bayesian inference , geography , archaeology

Two statistical estimating procedures are presented for using regression information along with direct observations to obtain estimates of the expected value of peak flood discharge rates exceeding a constant base. The first type of estimate represents the minimum mean squared error linear combination of regression and direct estimates, whereas the second is a Bayesian estimate based on an objective prior distribution associated with the regression model. A comparison of combined estimates to regression and direct estimates used alone indicates that a significant reduction in the mean squared error is obtained by using combined estimates. A comparison of the two methods for obtaining a combined estimate indicates that in many cases they both give essentially the same result.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research