Information bounds and nonparametric maximum likelihood estimation
P. Groeneboom · 2012
Where to buy
Amazon’s price changes daily, so the most accurate place to see it is there, at the moment you buy.
View on AmazonThis is an affiliate link: if you buy through it, the site earns a small commission at no extra cost to you, which helps us bring quality content and maintain the site. As an Amazon Associate we earn from qualifying purchases.
About the book
The book gives an account of recent developments in the theory of nonparametric and semiparametric estimation. The first part deals with information lower bounds and differentiable functionals. The second part focuses on nonparametric maximum likelihood estimators for interval censoring and deconvolution. The distribution theory of these estimators is developed and new algorithms for computing them are introduced. The models apply frequently in biostatistics and epidemiology and although they have been used as a data-analytic tool for a long time, their properties have been largely unknown. Contents: Part I. Information Bounds: 1. Models, scores, and tangent spaces • 2. Convolution and asymptotic minimax theorems • 3. Van der Vaart's Differentiability Theorem • PART II. Nonparametric Maximum Likelihood Estimation: 1. The interval censoring problem • 2. The deconvolution problem • 3. Algorithms • 4. Consistency • 5. Distribution theory • References
Book details
- Year
- 2012
- Language
- English
- Publisher
- Birkhauser Verlag
- Editions
- 2
- ISBN
- 9783034886215
Data source: Wikidata
