Geodesic
A note on optimal joint prediction of order statistics
Alexander Zaigraev1
1 Faculty of Mathematics and Computer Science Nicolaus Copernicus University 87-100 Toruń, Poland <a href="https://orcid.org/0000-0002-6261-031X">ORCID: 0000-0002-6261-031X</a>
Applicationes Mathematicae, Tome 50 (2023) no. 2, pp. 97-106 Cet article a éte moissonné depuis la source Institute of Mathematics Polish Academy of Sciences

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The problem of prediction of several future order statistics, based on previous ones, is considered. An optimal predictor is defined as one minimizing the determinant of the covariance matrix of the predictor or of the predictive error vector. It is shown that the Lagrange multipliers method works well in all cases, despite some statements in the papers by Balakrishnan et al. [Metrika 85 (2022), 253–267; J. Multivariate Anal. 188 (2022), art. 104854; Statistics 57 (2023), 1239–1250].
DOI : 10.4064/am2506-2-2024
Keywords: problem prediction several future order statistics based previous considered optimal predictor defined minimizing determinant covariance matrix predictor predictive error vector shown lagrange multipliers method works cases despite statements papers balakrishnan metrika multivariate anal art statistics

Alexander Zaigraev 1

1 Faculty of Mathematics and Computer Science Nicolaus Copernicus University 87-100 Toruń, Poland <a href="https://orcid.org/0000-0002-6261-031X">ORCID: 0000-0002-6261-031X</a> 
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Alexander Zaigraev. A note on optimal joint prediction of order statistics. Applicationes Mathematicae, Tome 50 (2023) no. 2, pp. 97-106. doi: 10.4064/am2506-2-2024

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