In recent years an impressive array of publications has appeared claiming considerable successes of neural networks in modelling ยฎnancial data but sceptical practitioners and statisticians are still raising the question of whether neural networks really are `a major breakthrough or just a passing fa
Model uncertainty and variable selection in
โ Scribed by Chris Hans
- Publisher
- Springer US
- Year
- 2009
- Tongue
- English
- Weight
- 378 KB
- Volume
- 20
- Category
- Article
- ISSN
- 0960-3174
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
In this paper we consider the problem of selecting the covariables within individuals in the growth curve model. We propose two modifications of AIC and MIC (Cp-static), which have improvements on the bias properties. Asymptotic distributions of variable slection criteria are derived under a general
Variable selection and transformation selection are two commonly encountered problems in the linear model. It is often of interest to combine these two procedures in an analysis. Due to recent developments in computing technology, such a procedure is now feasible. In this paper, we propose two varia
## Abstract Variable selection is growing in importance with the advent of high throughput genotyping methods requiring analysis of hundreds to thousands of single nucleotide polymorphisms (SNPs) and the increased interest in using these genetic studies to better understand common, complex diseases