頁籤選單縮合
題 名 | A General Asymptotic Theory for Maximum Likelihood Estimation in Semiparametric Regression Models with Censored Data |
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作 者 | Zeng, Donglin; Lin, D. Y.; | 書刊名 | Statistica Sinica |
卷 期 | 20:2 2010.04[民99.04] |
頁 次 | 頁871-910 |
分類號 | 319.22 |
關鍵詞 | Counting process; Empirical process; Multivariate failure times; Nonparametric likelihood; Profile likelihood; Survival data; |
語 文 | 英文(English) |
英文摘要 | Abstract: We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semiparametric regression models with right censored data. We identify a set of regularity conditions under which the nonparametric maximum likelihood estimators are consistent, asymptotically normal, and asymptotically efficient with a covariance matrix that can be consistently estimated by the inverse information matrix or the profile likelihood method. The general theory allows one to obtain the desired asymptotic properties of the nonparametric maximum likelihood estimators for any specific problem by verifying a set of conditions rather than by proving technical results from first principles. We demonstrate the usefulness of this powerful theory through a variety of examples. |
本系統中英文摘要資訊取自各篇刊載內容。