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  • Search: subject:"Ordinary ridge regression estimator"
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multicollinearity 2 ordinary ridge regression estimator 2 r-k class estimator 2 Concomitant variables 1 Mean square error matrix Multicollinearity Ordinary ridge regression estimator Principal components regression estimator r 1 Misspecification 1 Multicollinearity 1 Ordinary ridge regression estimator 1 Pitman closeness criterion 1 Principal components regression estimator 1 k class estimator 1 ordinary least squares estimator 1 principal component regression estimator 1 principal components regression estimator 1 r − k class estimator 1
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Sarkar, Nityananda 2 Inagaki, Nobuo 1 Jimichi, Masayuki 1 Kaçıranlar, Selahattin 1 Özkale, M. 1
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Annals of the Institute of Statistical Mathematics 2 Statistical Papers / Springer 1 Statistics & Probability Letters 1
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RePEc 4
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Comparisons of the r − k class estimator to the ordinary least squares estimator under the Pitman’s closeness criterion
Özkale, M.; Kaçıranlar, Selahattin - In: Statistical Papers 49 (2008) 3, pp. 503-512
Persistent link: https://www.econbiz.de/10008486818
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Mean square error matrix comparison of some estimators in linear regressions with multicollinearity
Sarkar, Nityananda - In: Statistics & Probability Letters 30 (1996) 2, pp. 133-138
The ordinary least squares, the principal components regression and the ordinary ridge regression estimators are special cases of the r - k class estimator proposed by Baye and Parker (1984) for regression models with multicollinearity. We obtain necessary and sufficient conditions for the...
Persistent link: https://www.econbiz.de/10005074540
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r-k Class estimation in regression model with concomitant variables
Jimichi, Masayuki; Inagaki, Nobuo - In: Annals of the Institute of Statistical Mathematics 48 (1996) 1, pp. 89-95
Persistent link: https://www.econbiz.de/10005616172
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Comparisons among some estimators in misspecified linear models with multicollinearity
Sarkar, Nityananda - In: Annals of the Institute of Statistical Mathematics 41 (1989) 4, pp. 717-724
Persistent link: https://www.econbiz.de/10005395519
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