✦ LIBER ✦

Empirical likelihood confidence intervals for

✍ Scribed by Deyuan Li; Liang Peng; Yongcheng Qi

Book ID: 107498231
Publisher: CrossRef test prefix
Year: 2010
Tongue: English
Weight: 502 KB
Volume: 20
Category: Article
ISSN: 1234-5678
DOI: 10.1007/s11749-010-0204-4

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Empirical Likelihood Confidence Interval

Empirical Likelihood Confidence Intervals for Linear Regression Coefficients

✍ S.X. Chen 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 527 KB

Nonparametric versions of Wilks' theorem are proved for empirical likelihood estimators of slope and mean parameters for a simple linear regression model. They enable us to construct empirical likelihood confidence intervals for these parameters. The coverage errors of these confidence intervals are

Empirical likelihood confidence interval

Empirical likelihood confidence intervals for hazard and density functions under right censorship

✍ Junshan Shen; Shuyuan He 📂 Article 📅 2007 🏛 Springer Japan 🌐 English ⚖ 202 KB

Empirical likelihood confidence interval

Empirical likelihood confidence intervals for the differences of quantiles with missing data

✍ Yong-song Qin; Yong-jiang Qian 📂 Article 📅 2008 🏛 Institute of Applied Mathematics, Chinese Academy 🌐 English ⚖ 218 KB

Empirical likelihood confidence interval

Empirical likelihood confidence intervals for differences between two datasets with missing data

✍ Yongsong Qin; Shichao Zhang 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 196 KB

Semi-empirical likelihood confidence int

Semi-empirical likelihood confidence intervals for the differences of quantiles with missing data

✍ Yong Song Qin; Jun Chao Zhang 📂 Article 📅 2009 🏛 Institute of Mathematics, Chinese Academy of Scien 🌐 English ⚖ 216 KB

Empirical likelihood confidence interval

Empirical likelihood confidence intervals for M-functionals in the presence of auxiliary information

✍ Biao Zhang 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 606 KB

In this paper, we employ the method of empirical likelihood to construct confidence intervals for M-functionals in the presence of auxiliary information under a nonparametric setting. The modified empirical likelihood confidence intervals which make use of the knowledge of auxiliary information are