Nonparametric EM Algorithms for estimating prior distributions
β Scribed by Alan Schumitzky
- Publisher
- Elsevier Science
- Year
- 1991
- Tongue
- English
- Weight
- 647 KB
- Volume
- 45
- Category
- Article
- ISSN
- 0096-3003
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In this paper, we will investigate thc nonparametric estimation of the distribution function F of an absolutely continuous random variable. Two methods are analyzed: the first one based on the empirical distribution function, expressed in terms of i.i.d, lattice random variables and, secondly, the k
The Normal-Inverse Gaussian distribution arises as a Normal variance-mean mixture with an Inverse Gaussian mixing distribution. This article deals with Maximum Likelihood estimation of the parameters of the Normal-Inverse Gaussian distribution. Due to the complexity of the likelihood, direct maximiz
We describe parameter estimation for the multivariate sub-Gaussian symmetric stable distribution using Monte Carlo EM algorithm. Two augmented vectors are employed in the construction of the posterior joint density of the stable parameters. Gibbs sampling enables the generation of these vectors from