| 书目名称 | Information Bounds and Nonparametric Maximum Likelihood Estimation | | 编辑 | Piet Groeneboom,Jon A. Wellner | | 视频video | | | 丛书名称 | Oberwolfach Seminars | | 图书封面 |  | | 描述 | This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner‘s lectures, gives a brief sketch of information lower bound theory: Hajek‘s convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has‘minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991). The differentiability theorem is illustrated with the examples of interval censoring and deconvolution (which are pursued from the estimation perspective in part II). The differentiability theorem gives a way of clearly distinguishing situations in which 1 2 the parameter of interest can be estimated at rate n / and situations in which this is not the case. However it says nothing about which rates to expect when the functional is not differentiable. Even the casual reader will notice that several m | | 出版日期 | Book 1992 | | 关键词 | Censoring; Estimator; Finite; Likelihood; Random variable; Variable; expectation–maximization algorithm; fu | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-0348-8621-5 | | isbn_softcover | 978-3-7643-2794-1 | | isbn_ebook | 978-3-0348-8621-5Series ISSN 1661-237X Series E-ISSN 2296-5041 | | issn_series | 1661-237X | | copyright | Springer Basel AG 1992 |
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发表于 2025-3-21 18:29:03
