頁籤選單縮合
題 名 | Estimation of Distribution Function and Quantiles Using the Model-Calibrated Pseudo Empirical Likelihood Method |
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作 者 | Chen,Jiahua; Wu,Changbao; | 書刊名 | Statistica Sinica |
卷 期 | 12:4 2002.10[民91.10] |
頁 次 | 頁1223-1239 |
分類號 | 319.3 |
關鍵詞 | Auxiliary information; Bahadur representation; Design consistency; Finite population; Model-assisted approach; Model calibration; Variance estimation; |
語 文 | 英文(English) |
英文摘要 | We use the model-calibrated pseudo empirical likelihood method to construct estimators for the finite population distribution function. Under an assumed superpopulation working model, the proposed estimators have minimum model expectation of asymptotic design-based variance among a class of estimators and therefore are optimal in that class. The estimators are asymptotically design-unbiased irrespective of the working model and are also approximately model-unbiased under the model. They share the design-based asymptotic efficiency with that of a generalized regression estimator but, unlike the latter, the estimators are genuine distribution functions. Quantile estimation through direct inversion and using a model-calibrated difference estimator are studied, and their asymptotic efficiency is investigated through Bahadur representations. Variance estimation and confidence intervals for the distribution function are also addressed. Results of a limited simulation study regarding the finite sample performance of proposed estimators are reported. |
本系統中英文摘要資訊取自各篇刊載內容。