| contributor author | Mielke, Paul W. | |
| contributor author | Berry, Kenneth J. | |
| contributor author | Landsea, Christopher W. | |
| contributor author | Gray, William M. | |
| date accessioned | 2017-06-09T14:51:45Z | |
| date available | 2017-06-09T14:51:45Z | |
| date copyright | 1996/06/01 | |
| date issued | 1996 | |
| identifier issn | 0882-8156 | |
| identifier other | ams-2841.pdf | |
| identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4165523 | |
| description abstract | The results of a simulation study of multiple regression prediction models for meteorological forecasting are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations (LAD) and least (sum of) squared deviations (LSD) regression models are examined on five populations constructed from meteorological data. Artificial skill is shown to be a product of small sample size, LSD regression, and nonrepresentative data. Validation of sample results is examined, and LAD regression is found to be superior to LSD regression when sample size is small and nonrepresentative data are present. | |
| publisher | American Meteorological Society | |
| title | Artificial Skill and Validation in Meteorological Forecasting | |
| type | Journal Paper | |
| journal volume | 11 | |
| journal issue | 2 | |
| journal title | Weather and Forecasting | |
| identifier doi | 10.1175/1520-0434(1996)011<0153:ASAVIM>2.0.CO;2 | |
| journal fristpage | 153 | |
| journal lastpage | 169 | |
| tree | Weather and Forecasting:;1996:;volume( 011 ):;issue: 002 | |
| contenttype | Fulltext | |