A selective review of statistical methods using calibration information from similar studies
In the era of big data, divide-and-conquer, parallel, and distributed inference methods have become increasingly popular. How to effectively use the calibration information from each machine in parallel computation has become a challenging task for statisticians and computer scientists. Many newly d...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-08-01
|
| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2022.2096426 |
| _version_ | 1797677048844517376 |
|---|---|
| author | Jing Qin Yukun Liu Pengfei Li |
| author_facet | Jing Qin Yukun Liu Pengfei Li |
| author_sort | Jing Qin |
| collection | DOAJ |
| description | In the era of big data, divide-and-conquer, parallel, and distributed inference methods have become increasingly popular. How to effectively use the calibration information from each machine in parallel computation has become a challenging task for statisticians and computer scientists. Many newly developed methods have roots in traditional statistical approaches that make use of calibration information. In this paper, we first review some classical statistical methods for using calibration information, including simple meta-analysis methods, parametric likelihood, empirical likelihood, and the generalized method of moments. We further investigate how these methods incorporate summarized or auxiliary information from previous studies, related studies, or populations. We find that the methods based on summarized data usually have little or nearly no efficiency loss compared with the corresponding methods based on all-individual data. Finally, we review some recently developed big data analysis methods including communication-efficient distributed approaches, renewal estimation, and incremental inference as examples of the latest developments in methods using calibration information. |
| first_indexed | 2024-03-11T22:39:20Z |
| format | Article |
| id | doaj.art-efcbd1dc1cc0405582c92fe0536b54de |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:39:20Z |
| publishDate | 2022-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-efcbd1dc1cc0405582c92fe0536b54de2023-09-22T09:19:46ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772022-08-010011610.1080/24754269.2022.20964262096426A selective review of statistical methods using calibration information from similar studiesJing Qin0Yukun Liu1Pengfei Li2National Institutes of HealthEast China Normal UniversityUniversity of WaterlooIn the era of big data, divide-and-conquer, parallel, and distributed inference methods have become increasingly popular. How to effectively use the calibration information from each machine in parallel computation has become a challenging task for statisticians and computer scientists. Many newly developed methods have roots in traditional statistical approaches that make use of calibration information. In this paper, we first review some classical statistical methods for using calibration information, including simple meta-analysis methods, parametric likelihood, empirical likelihood, and the generalized method of moments. We further investigate how these methods incorporate summarized or auxiliary information from previous studies, related studies, or populations. We find that the methods based on summarized data usually have little or nearly no efficiency loss compared with the corresponding methods based on all-individual data. Finally, we review some recently developed big data analysis methods including communication-efficient distributed approaches, renewal estimation, and incremental inference as examples of the latest developments in methods using calibration information.http://dx.doi.org/10.1080/24754269.2022.2096426calibration informationempirical likelihoodestimating equationsgeneralized method of momentsmeta-analysis |
| spellingShingle | Jing Qin Yukun Liu Pengfei Li A selective review of statistical methods using calibration information from similar studies Statistical Theory and Related Fields calibration information empirical likelihood estimating equations generalized method of moments meta-analysis |
| title | A selective review of statistical methods using calibration information from similar studies |
| title_full | A selective review of statistical methods using calibration information from similar studies |
| title_fullStr | A selective review of statistical methods using calibration information from similar studies |
| title_full_unstemmed | A selective review of statistical methods using calibration information from similar studies |
| title_short | A selective review of statistical methods using calibration information from similar studies |
| title_sort | selective review of statistical methods using calibration information from similar studies |
| topic | calibration information empirical likelihood estimating equations generalized method of moments meta-analysis |
| url | http://dx.doi.org/10.1080/24754269.2022.2096426 |
| work_keys_str_mv | AT jingqin aselectivereviewofstatisticalmethodsusingcalibrationinformationfromsimilarstudies AT yukunliu aselectivereviewofstatisticalmethodsusingcalibrationinformationfromsimilarstudies AT pengfeili aselectivereviewofstatisticalmethodsusingcalibrationinformationfromsimilarstudies AT jingqin selectivereviewofstatisticalmethodsusingcalibrationinformationfromsimilarstudies AT yukunliu selectivereviewofstatisticalmethodsusingcalibrationinformationfromsimilarstudies AT pengfeili selectivereviewofstatisticalmethodsusingcalibrationinformationfromsimilarstudies |