Robust designs for misspecified logistic models
| Autor: | Adeniyi J. Adewale, Douglas P. Wiens |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Statistics and Probability
Logistic distribution Applied Mathematics Sampling (statistics) Linear prediction Regression analysis Function (mathematics) Logistic regression symbols.namesake Simulated annealing Statistics Econometrics symbols Statistics Probability and Uncertainty Fisher information Mathematics |
| Zdroj: | Journal of Statistical Planning and Inference. 139:3-15 |
| ISSN: | 0378-3758 |
| Popis: | We develop criteria that generate robust designs and use such criteria for the construction of designs that insure against possible misspecifications in logistic regression models. The design criteria we propose are different from the classical in that we do not focus on sampling error alone. Instead we use design criteria that account as well for error due to bias engendered by the model misspecification. Our robust designs optimize the average of a function of the sampling error and bias error over a specified misspecification neighbourhood. Examples of robust designs for logistic models are presented, including a case study implementing the methodologies using beetle mortality data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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