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To overcome the multicollinearity problem, two new classes of estimators called the almost unbiased ridge-type principal component estimator (AURPCE) and the almost unbiased Liu-type principal component estimator (AULPCE) are proposed, respectively. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a Monte Carlo simulation study is given to illustrate the performance of the proposed estimators.<\/jats:p>","DOI":"10.1155\/2014\/639070","type":"journal-article","created":{"date-parts":[[2014,4,2]],"date-time":"2014-04-02T23:13:10Z","timestamp":1396480390000},"page":"1-6","source":"Crossref","is-referenced-by-count":2,"title":["Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model"],"prefix":"10.1155","volume":"2014","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2395-6174","authenticated-orcid":true,"given":"Yalian","family":"Li","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science, Chongqing University, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, Chongqing University, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"309","key":"1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1080\/01621459.1965.10480787","volume":"60","year":"1965","journal-title":"The Journal of the American Statistical Association"},{"issue":"1","key":"2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1080\/00401706.2000.10485983","volume":"42","year":"2000","journal-title":"Technometrics"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1080\/03610929308831034"},{"issue":"3","key":"4","first-page":"342","volume":"48","year":"1986","journal-title":"Sankhy\u0101. 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