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A restricted r-k class estimator in the mixed regression model with autocorrelated disturbances
In this paper, a new estimator is proposed by combining basically the ordinary ridge regression estimator and the principal component regression estimator for a regression model which has multicollinearity and which satisfies some a priori stochastic linear restrictions. The performance of the propo...
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| Published in: | Statistical papers (Berlin, Germany) Germany), 2016-04, Vol.57 (2), p.429-449 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-p1044-9d0470a96e465bb482a2956c05293f5d11595b883b5b3ad60e152879a1cb8f23 |
| container_end_page | 449 |
| container_issue | 2 |
| container_start_page | 429 |
| container_title | Statistical papers (Berlin, Germany) |
| container_volume | 57 |
| creator | Chandra, Shalini Sarkar, Nityananda |
| description | In this paper, a new estimator is proposed by combining basically the ordinary ridge regression estimator and the principal component regression estimator for a regression model which has multicollinearity and which satisfies some
a priori
stochastic linear restrictions. The performance of the proposed
r
-
k
class estimator in this mixed regression model is compared with those of the mixed regression estimator, ridge regression estimator and the stochastic restricted ridge regression estimator in terms of the mean squared error matrix criterion. Tests for verifying the conditions of dominance of the proposed estimator over the others are also proposed. Furthermore, a Monte Carlo study and a numerical illustration are carried out to empirically study the performance of the estimators by the mean squared error values, and then to perform tests to verify if the conditions for superiority of the proposed estimator over the others hold. |
| doi_str_mv | 10.1007/s00362-015-0664-4 |
| format | article |
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a priori
stochastic linear restrictions. The performance of the proposed
r
-
k
class estimator in this mixed regression model is compared with those of the mixed regression estimator, ridge regression estimator and the stochastic restricted ridge regression estimator in terms of the mean squared error matrix criterion. Tests for verifying the conditions of dominance of the proposed estimator over the others are also proposed. Furthermore, a Monte Carlo study and a numerical illustration are carried out to empirically study the performance of the estimators by the mean squared error values, and then to perform tests to verify if the conditions for superiority of the proposed estimator over the others hold.</description><identifier>ISSN: 0932-5026</identifier><identifier>EISSN: 1613-9798</identifier><identifier>DOI: 10.1007/s00362-015-0664-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Finance ; Insurance ; Management ; Mathematics and Statistics ; Operations Research/Decision Theory ; Probability Theory and Stochastic Processes ; Regression analysis ; Regular Article ; Statistics ; Statistics for Business ; Variables</subject><ispartof>Statistical papers (Berlin, Germany), 2016-04, Vol.57 (2), p.429-449</ispartof><rights>Springer-Verlag Berlin Heidelberg 2015</rights><rights>Statistical Papers is a copyright of Springer, 2016.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-p1044-9d0470a96e465bb482a2956c05293f5d11595b883b5b3ad60e152879a1cb8f23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1884865802/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1884865802?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Chandra, Shalini</creatorcontrib><creatorcontrib>Sarkar, Nityananda</creatorcontrib><title>A restricted r-k class estimator in the mixed regression model with autocorrelated disturbances</title><title>Statistical papers (Berlin, Germany)</title><addtitle>Stat Papers</addtitle><description>In this paper, a new estimator is proposed by combining basically the ordinary ridge regression estimator and the principal component regression estimator for a regression model which has multicollinearity and which satisfies some
a priori
stochastic linear restrictions. The performance of the proposed
r
-
k
class estimator in this mixed regression model is compared with those of the mixed regression estimator, ridge regression estimator and the stochastic restricted ridge regression estimator in terms of the mean squared error matrix criterion. Tests for verifying the conditions of dominance of the proposed estimator over the others are also proposed. 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a priori
stochastic linear restrictions. The performance of the proposed
r
-
k
class estimator in this mixed regression model is compared with those of the mixed regression estimator, ridge regression estimator and the stochastic restricted ridge regression estimator in terms of the mean squared error matrix criterion. Tests for verifying the conditions of dominance of the proposed estimator over the others are also proposed. Furthermore, a Monte Carlo study and a numerical illustration are carried out to empirically study the performance of the estimators by the mean squared error values, and then to perform tests to verify if the conditions for superiority of the proposed estimator over the others hold.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00362-015-0664-4</doi><tpages>21</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0932-5026 |
| ispartof | Statistical papers (Berlin, Germany), 2016-04, Vol.57 (2), p.429-449 |
| issn | 0932-5026 1613-9798 |
| language | eng |
| recordid | cdi_proquest_journals_1884865802 |
| source | Business Source Ultimate; EBSCOhost Econlit with Full Text; ABI/INFORM Global; Springer Nature |
| subjects | Economic Theory/Quantitative Economics/Mathematical Methods Economics Finance Insurance Management Mathematics and Statistics Operations Research/Decision Theory Probability Theory and Stochastic Processes Regression analysis Regular Article Statistics Statistics for Business Variables |
| title | A restricted r-k class estimator in the mixed regression model with autocorrelated disturbances |
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