Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/8893594 |
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| _version_ | 1832547682770485248 |
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| author | Hanji He Guangming Deng |
| author_facet | Hanji He Guangming Deng |
| author_sort | Hanji He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0784c68d5e7f407d9a10d98ba0acb629 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0784c68d5e7f407d9a10d98ba0acb6292025-02-03T06:43:43ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/88935948893594Mean Empirical Likelihood Inference for Response Mean with Data Missing at RandomHanji He0Guangming Deng1College of Science, Guilin University of Techology, Guilin 541004, ChinaCollege of Science, Guilin University of Techology, Guilin 541004, ChinaWe 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.http://dx.doi.org/10.1155/2020/8893594 |
| spellingShingle | Hanji He Guangming Deng Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random Discrete Dynamics in Nature and Society |
| title | Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random |
| title_full | Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random |
| title_fullStr | Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random |
| title_full_unstemmed | Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random |
| title_short | Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random |
| title_sort | mean empirical likelihood inference for response mean with data missing at random |
| url | http://dx.doi.org/10.1155/2020/8893594 |
| work_keys_str_mv | AT hanjihe meanempiricallikelihoodinferenceforresponsemeanwithdatamissingatrandom AT guangmingdeng meanempiricallikelihoodinferenceforresponsemeanwithdatamissingatrandom |