Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
We extend the mean empirical likelihood inference for response mean with data missing at random.
The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small.
Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean.
As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset.
The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse.
Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established.
The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.
American Psychological Association (APA)
He, Hanji& Deng, Guangming. 2020. Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1153583
Modern Language Association (MLA)
He, Hanji& Deng, Guangming. Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1153583
American Medical Association (AMA)
He, Hanji& Deng, Guangming. Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1153583
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153583