Strategies for Assessing Skill and Significance of Screening Regression Models with Emphasis on Monte Carlo Techniques
- 1 October 1984
- journal article
- research article
- Published by American Meteorological Society in Journal of Climate and Applied Meteorology
- Vol. 23 (10) , 1454-1458
- https://doi.org/10.1175/0733-3021-23.10.1454
Abstract
This paper reviews the considerations in evaluating the skill and significance of screening multiple linear regression (SMLR) models. Formulations and procedures are given along with relevant references to prior studies. Topics discussed include predictor selection, serial correlation, artificial skill, true skill, and Monte Carlo significance testing. New results with wide applicability in the assessment of SMLR model skill and significance are presented in graphical form. However, the results are restricted to situations involving predictors which are independent of one another and are serially uncorrelated. The methodology presented is suggested for use in both model evaluation and experimental design.Keywords
This publication has 1 reference indexed in Scilit:
- Monte Carlo Significance Testing as Applied to Statistical Tropical Cyclone Prediction ModelsJournal of Applied Meteorology, 1977