Free-coordinate estimation for doubly multivariate data

Arkadiusz Kozioł, Anuradha Roy, Roman Zmyślony, Ricardo Leiva, Miguel Fonseca

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The article addresses the best unbiased estimators of the block compound symmetric covariance structure for m−variate observations with equal mean vector over u sites under the assumption of multivariate quasi-normality. The free-coordinate approach is used to prove that the quadratic estimation of covariance parameters is equivalent to linear estimation with a properly defined inner product in the space of symmetric matrices. Without assumption of normality but quasi-normality, meaning that up to fourth moments are the same as in the normal case, the estimators are best linear and best quadratic for mean and covariance parameters, respectively. Finally, strong consistency is proven. The properties of the estimators in the proposed model are compared against a similar model available in the literature. An application of the proposed approach to a clinical study data is presented.

Original languageEnglish
Pages (from-to)217-239
Number of pages23
JournalLinear Algebra and Its Applications
Volume547
DOIs
Publication statusPublished - 15 Jun 2018

Keywords

  • Best unbiased estimator
  • Block compound symmetric covariance structure
  • Coordinate free approach
  • Doubly multivariate data
  • Structured mean vector

Fingerprint Dive into the research topics of 'Free-coordinate estimation for doubly multivariate data'. Together they form a unique fingerprint.

  • Cite this