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Journal of Inverse and Ill-Posed Problems

2021; De Gruyter; Linguagem: Inglês

10.1515/jiip

1569-3945

Sergey Kabanikhin, Maxim Shishlenin, Novosibirsk Advisory, Avner Friedman, Columbus Kress, Göttingen Lax, New York, Zuhair Nashed, Vladimir Orlando, Pierre Romanov, Vladimir Sabatier, Giovanni Alessandrini, Trieste Ammari, Zurich Thomas Banks, Raleigh Gang, Alexander Bukhgeim, Jin Cheng, Shanghai Clason, Alexander Denisov, Heinz W. Engl, Vienna Dinh, Nho Hào, Hanoi Hasanoglu, Bernd Hofmann, Thorsten Hohage, Göttingen Maarten, V De Hoop, Houston Masaru Ikehata, Higashihiroshima Isakov, Wichita Mikhail, Yu Kokurin, Yoshkar-Ola Lesnic, Jijun Leeds, Alfred Liu, Vyacheslav Louis, Victor Maksimov, St Mikhaylov, Gen Petersburg, Andreas Nakamura, Roman Neubauer, Paris Novikov, Valery Moscow, Novosibirsk Pickalov, Todd Eric, Paul Quinto, Ames Sacks, Vienna Scherzer, Plamen Shkalikov, West Stefanov, Gunther Lafayette, Yanfei Uhlmann, A. G. Yagola, Masahiro Yamamoto, Tokyo Vyacheslav, A Yurko, Jun Saratov, Hong Zou, Responsible Editor, Markus Kügel, Franz Stückle, Druck Und Verlag,

Multi-Criteria Decision Making

In this paper, we propose the r - d class predictors which are general predictors of the best linear unbiased predictor (BLUP), the principal components regression (PCR) and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictors to each of these predictors are done in the sense of the mean square error matrix criterion. Finally, numerical examples and a simulation study are done to illustrate the findings.