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http://krishi.icar.gov.in/jspui/handle/123456789/61994Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Himadri Ghosh | en_US |
| dc.contributor.author | M. A. Iquebal | en_US |
| dc.contributor.author | Prajneshu | en_US |
| dc.date.accessioned | 2021-09-14T08:03:31Z | - |
| dc.date.available | 2021-09-14T08:03:31Z | - |
| dc.date.issued | 2008-09-28 | - |
| dc.identifier.citation | Himadri Ghosh, M. A. Iquebal & Prajneshu (2008) A Bootstrap Study of Variance Estimation under Heteroscedasticity Using Genetic Algorithm, Journal of Statistical Theory and Practice, 2:1, 55-69, DOI: 10.1080/15598608.2008.10411860 | en_US |
| dc.identifier.issn | Not Available | - |
| dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/61994 | - |
| dc.description | Not Available | en_US |
| dc.description.abstract | The conventional ordinary least squares (OLS) variance-covariance matrix estimator for a linear regression model under heteroscedastic errors is biased and inconsistent. Accordingly, several estimators have so far been proposed by various researchers. However, none of these perform well under the finite-sample situation. In this paper, the powerful optimization technique of Genetic algorithm (GA) is used to modify these estimators. Properties of these newly developed estimators are thoroughly studied by Monte Carlo method for various sample sizes. It is shown that GA-versions of the estimators are superior to corresponding non-GA versions as there are significant reductions in the Total relative bias as well as Total root mean square error. | en_US |
| dc.description.sponsorship | Not Available | en_US |
| dc.language.iso | English | en_US |
| dc.publisher | Not Available | en_US |
| dc.relation.ispartofseries | Not Available; | - |
| dc.subject | Linear regression model | en_US |
| dc.subject | Least squares estimators | en_US |
| dc.subject | Heteroscedasticit | en_US |
| dc.title | A Bootstrap Study of Variance Estimation under Heteroscedasticity Using Genetic Algorithm | en_US |
| dc.title.alternative | Not Available | en_US |
| dc.type | Research Paper | en_US |
| dc.publication.projectcode | Not Available | en_US |
| dc.publication.journalname | Journal of Statistical Theory and Practice | en_US |
| dc.publication.volumeno | 2 | en_US |
| dc.publication.pagenumber | 55-69 | en_US |
| dc.publication.divisionUnit | Statistical Genetics | en_US |
| dc.publication.sourceUrl | https://doi.org/10.1080/15598608.2008.10411860 | en_US |
| dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
| dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
| dc.publication.naasrating | 5.95 | en_US |
| dc.publication.naasrating | 5.95 | - |
| Appears in Collections: | AEdu-IASRI-Publication | |
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