𝔖 Bobbio Scriptorium
✦   LIBER   ✦

An EM type algorithm for maximum likelihood estimation of the normal–inverse Gaussian distribution

✍ Scribed by Dimitris Karlis

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
134 KB
Volume
57
Category
Article
ISSN
0167-7152

No coin nor oath required. For personal study only.

✦ Synopsis

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 maximization is di cult. An EM type algorithm is provided for the Maximum Likelihood estimation of the Normal-Inverse Gaussian distribution. This algorithm overcomes numerical di culties occurring when standard numerical techniques are used. An application to a data set concerning the general index of the Athens Stock Exchange is given. Some operating characteristics of the algorithm are discussed.

📜 SIMILAR VOLUMES

An algorithm for maximum likelihood esti
An algorithm for maximum likelihood estimation in the three parameter Burr XII distribution
✍ Alan J. Watkins 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 90 KB

This paper outlines an algorithm for calculating the maximum likelihood estimates of the three parameter Burr XII distribution. The algorithm exploits the link between this distribution and the two parameter Weibull distribution, which emerges as a limiting case of the distribution, and also calcula

A comparison of a mixture likelihood met
A comparison of a mixture likelihood method and the EM algorithm for an estimation problem in animal carcinogenicity studies
✍ Hojin Moon; Hongshik Ahn; Ralph L. Kodell; Bruce A. Pearce 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 226 KB

Both a mixture likelihood method and the EM algorithm are implemented to estimate the time-toonset-of and the time-to-death-from the tumor of interest in animal carcinogenicity studies. Both methods are implemented using Box's Complex Method for ÿnding the maximum likelihood estimates of parameters