𝔖 Bobbio Scriptorium
✦   LIBER   ✦

The Role of the Likelihood Function in the Estimation of Chaos Models

✍ Scribed by T. Ozaki; J. C. Jimenez; V. Haggan-Ozaki

Book ID
108549424
Publisher
John Wiley and Sons
Year
2000
Tongue
English
Weight
525 KB
Volume
21
Category
Article
ISSN
0143-9782

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Maximum Likelihood Estimation of the Mor
Maximum Likelihood Estimation of the Mortality Rate Function
✍ B. N. Dimitrov; S. T. Rachev; Dr. Sci. A. Yu. Yakovlev πŸ“‚ Article πŸ“… 1985 πŸ› John Wiley and Sons 🌐 English βš– 465 KB πŸ‘ 2 views

Maximum likelihood estimator is obtained for the mortality rate function of a specific type appearing in survival data andysis. Strict consistency of this estimator is proved.

The use of approximating models in Monte
The use of approximating models in Monte Carlo maximum likelihood estimation
✍ Anthony Y.C Kuk πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 102 KB

To obtain the likelihood of a non-Gaussian state-space model, Durbin and Koopman (1997, Biometrika, 84, 669 -684) ΓΏrst calculate the likelihood under an approximating linear Gaussian model and then use Monte Carlo methods to estimate the necessary adjustment factor. We show that Durbin and Koopman's

Robust maximum likelihood estimation in
Robust maximum likelihood estimation in the linear model
✍ Giuseppe Calafiore; Laurent El Ghaoui πŸ“‚ Article πŸ“… 2001 πŸ› Elsevier Science 🌐 English βš– 181 KB

This paper addresses the problem of maximum likelihood parameter estimation in linear models a!ected by Gaussian noise, whose mean and covariance matrix are uncertain. The proposed estimate maximizes a lower bound on the worst-case (with respect to the uncertainty) likelihood of the measured sample,