This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodolog
Maximum Likelihood Estimation and Inference (With Examples in R, SAS and ADMB) || Some Widely Used Applications of Maximum Likelihood
โ Scribed by Millar, Russell B.
- Publisher
- John Wiley & Sons, Ltd
- Year
- 2011
- Tongue
- English
- Weight
- 179 KB
- Edition
- 1
- Category
- Article
- ISBN
- 0470094826
No coin nor oath required. For personal study only.
โฆ Synopsis
Some widely used applications of maximum likelihood
The best thing about being a statistician is that you get to play in everyone's backyard.
๐ SIMILAR VOLUMES
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodolog
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodolog
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodolog
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodolog
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodolog