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Information Bounds and Nonparametric Maximum Likelihood Estimation : DMV Seminar - Piet Groeneboom

Information Bounds and Nonparametric Maximum Likelihood Estimation

DMV Seminar


Published: 1992
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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

Part 1 Information bounds: models, scores and tangent spaces; convolution and asymptotic minimax theorems; Van der Vaart's differentiability theorem. Part 2 Nonparametric maximum likelihood: the interval censoring problem; the deconvolution problem; algorithms; consistency; distribution theory.

ISBN: 9783764327941
ISBN-10: 3764327944
Series: DMV Seminar
Audience: General
Format: Paperback
Language: English
Number Of Pages: 128
Published: 1992
Publisher: Birkhauser Verlag AG
Country of Publication: CH
Dimensions (cm): 24.41 x 16.99  x 0.74
Weight (kg): 0.23