caa a22 4500 477076645 CHVBK 20180405111440.0 cr unu---uuuuu 170330e19960401xx s 000 0 eng 10.1007/BF00046997 doi (NATIONALLICENCE)springer-10.1007/BF00046997 Minimax least squares and quasiminimax estimation in linear models with missing values [Elektronische Daten] [Michaela Jänner, Peter Stahlecker] We consider the problem of estimating the parameter vector in the linear model when observations on the independent variables are partially missing or incorrect. New estimators are developed, which systematically combine prior information with the incomplete data. We compare these methods with the alternative strategy of deleting incomplete observations. Kluwer Academic Publishers, 1996 linear regression model nationallicence missing values nationallicence prior information nationallicence minimax estimation nationallicence error in variables nationallicence Jänner Michaela Department of Economics, University of Oldenburg, Germany aut Stahlecker Peter Department of Economics, University of Hamburg, Germany aut Acta Applicandae Mathematica Kluwer Academic Publishers 43/1(1996-04-01), 159-167 0167-8019 43:1<159 1996 43 10440 https://doi.org/10.1007/BF00046997 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00046997 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jänner Michaela Department of Economics, University of Oldenburg, Germany aut NATIONALLICENCE 700 1- Stahlecker Peter Department of Economics, University of Hamburg, Germany aut NATIONALLICENCE 773 0- Acta Applicandae Mathematica Kluwer Academic Publishers 43/1(1996-04-01), 159-167 0167-8019 43:1<159 1996 43 10440 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer