Consistent variable selection for functional regression models
| Autor: | Ronaldo Dias, Julian A. A. Collazos, Adriano Zanin Zambom |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
False discovery rate Numerical Analysis 05 social sciences Scalar (mathematics) Feature selection Grid 01 natural sciences 010104 statistics & probability Sample size determination Likelihood-ratio test 0502 economics and business Covariate Statistics Econometrics Statistics::Methodology 0101 mathematics Statistics Probability and Uncertainty 050205 econometrics Mathematics Statistical hypothesis testing |
| Zdroj: | Journal of Multivariate Analysis. 146:63-71 |
| ISSN: | 0047-259X |
| Popis: | The dual problem of testing the predictive significance of a particular covariate, and identification of the set of relevant covariates is common in applied research and methodological investigations. To study this problem in the context of functional linear regression models with predictor variables observed over a grid and a scalar response, we consider basis expansions of the functional covariates and apply the likelihood ratio test. Based on p -values from testing each predictor, we propose a new variable selection method, which is consistent in selecting the relevant predictors from set of available predictors that is allowed to grow with the sample size n . Numerical simulations suggest that the proposed variable selection procedure outperforms existing methods found in the literature. A real dataset from weather stations in Japan is analyzed. |
| Databáze: | OpenAIRE |
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