MLE with datasets from populations having shared parameters
We consider maximum likelihood estimation with two or more datasets sampled from different populations with shared parameters. Although more datasets with shared parameters can increase statistical accuracy, this paper shows how to handle heterogeneity among different populations for correctness of...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-07-01
|
| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2023.2180185 |
| _version_ | 1797677031766360064 |
|---|---|
| author | Jun Shao Xinyan Wang |
| author_facet | Jun Shao Xinyan Wang |
| author_sort | Jun Shao |
| collection | DOAJ |
| description | We consider maximum likelihood estimation with two or more datasets sampled from different populations with shared parameters. Although more datasets with shared parameters can increase statistical accuracy, this paper shows how to handle heterogeneity among different populations for correctness of estimation and inference. Asymptotic distributions of maximum likelihood estimators are derived under either regular cases where regularity conditions are satisfied or some non-regular situations. A bootstrap variance estimator for assessing performance of estimators and/or making large sample inference is also introduced and evaluated in a simulation study. |
| first_indexed | 2024-03-11T22:39:02Z |
| format | Article |
| id | doaj.art-b2183c82bfae45d1b76b12dca0f1f03e |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:39:02Z |
| publishDate | 2023-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-b2183c82bfae45d1b76b12dca0f1f03e2023-09-22T09:19:47ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772023-07-017321322210.1080/24754269.2023.21801852180185MLE with datasets from populations having shared parametersJun Shao0Xinyan Wang1East China Normal UniversityUniversity of WisconsinWe consider maximum likelihood estimation with two or more datasets sampled from different populations with shared parameters. Although more datasets with shared parameters can increase statistical accuracy, this paper shows how to handle heterogeneity among different populations for correctness of estimation and inference. Asymptotic distributions of maximum likelihood estimators are derived under either regular cases where regularity conditions are satisfied or some non-regular situations. A bootstrap variance estimator for assessing performance of estimators and/or making large sample inference is also introduced and evaluated in a simulation study.http://dx.doi.org/10.1080/24754269.2023.2180185accuracyasymptotic relative efficiencybootstrappopulation heterogeneityregularity conditions |
| spellingShingle | Jun Shao Xinyan Wang MLE with datasets from populations having shared parameters Statistical Theory and Related Fields accuracy asymptotic relative efficiency bootstrap population heterogeneity regularity conditions |
| title | MLE with datasets from populations having shared parameters |
| title_full | MLE with datasets from populations having shared parameters |
| title_fullStr | MLE with datasets from populations having shared parameters |
| title_full_unstemmed | MLE with datasets from populations having shared parameters |
| title_short | MLE with datasets from populations having shared parameters |
| title_sort | mle with datasets from populations having shared parameters |
| topic | accuracy asymptotic relative efficiency bootstrap population heterogeneity regularity conditions |
| url | http://dx.doi.org/10.1080/24754269.2023.2180185 |
| work_keys_str_mv | AT junshao mlewithdatasetsfrompopulationshavingsharedparameters AT xinyanwang mlewithdatasetsfrompopulationshavingsharedparameters |