On Equidistant Designs, Symmetries and Their Violations in Multivariate Models

Milan Stehlík (), Mirtha Pari Ruiz (), Silvia Stehlíková () and Ying Lu ()
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Milan Stehlík: Johannes Kepler University Linz, Institute of Statistics, University of Valparaíso, Valparaíso Chile and Department of Applied Statistics and Linz Institute of Technology
Mirtha Pari Ruiz: Universidad de Tarapacá (UTA), Departamento de Matemáticas
Silvia Stehlíková: Johannes Kepler University Linz, Department of Applied Statistics and Linz Institute of Technology
Ying Lu: Stanford University, Department of Biomedical Data Science

Chapter Chapter 14 in Contemporary Experimental Design, Multivariate Analysis and Data Mining, 2020, pp 217-225 from Springer

Abstract: Abstract In this Festschrift to Prof. Kai-Tai Fang 80 birthday we emphasize importance and potential of his results in statistics and general sciences. In particular we concentrate on equidistant designs, symmetric and asymmetric models. We discuss equidistant designs from perspective of optimal designs of experiments with correlated errors. We address symmetry and asymmetry of statistical multivariate models and its recent developments. Several applications are given.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-46161-4_14

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DOI: 10.1007/978-3-030-46161-4_14

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