Algorithms for the likelihood-based estimation of the random coefficient model

✍ Scribed by Chungyeol Shin; Yasuo Amemiya

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 721 KB
• Volume: 32
• Category: Article
• ISSN: 0167-7152
• DOI: 10.1016/s0167-7152(96)00072-7

✦ Synopsis

The existing algorithms for fitting the random coefficient models tend to have difficulties associated with the covariance matrix parameter space. New ML and REML algorithms are developed, explicitly addressing the parameter space problem. Theoretical justification and numerical results are presented.