A computationally efficient model selection in the generalized linear mixed model
β Scribed by Takuma Yoshida; Masaru Kanba; Kanta Naito
- Publisher
- Springer
- Year
- 2010
- Tongue
- English
- Weight
- 502 KB
- Volume
- 25
- Category
- Article
- ISSN
- 0943-4062
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A new method is presented to generate reduced order models (ROMs) in Fluid Dynamics problems. The method is based on the expansion of the flow variables on a Proper Orthogonal Decomposition (POD) basis, calculated from a limited number of snapshots, which are obtained via Computational Fluid Dynamic
## Abstract Without realizing the fact that the timeβdependent covariates corresponding to the repeated discrete responses under a generalized linear longitudinal model (GLLM) cause nonβstationary (time dependent) correlations for the repeated responses, many existing studies use a stationary (eith
On a Mixed Linear Model when the Data are Subject to Selection s. IY Station de Biom6trie e t d'htelligence Artificielle, INRA, France.
## Abstract This paper introduces a mixed effects model for an application of the hedonic price regression model for panel data. Thus far, the development of hedonic pricing regression for repeated measurements has received relatively little attention. This approach is applied to compare different
Computationally efficient serial and parallel algorithms for estimating the general linear model are proposed. The sequential block-recursive algorithm is an adaptation of a known Givens strategy that has as a main component the Generalized QR decomposition. The proposed algorithm is based on orthog