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Orthogonalization-Triangularization Methods in Statistical Computations

In: Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface

Author

Listed:
  • Del T. Scott

    (Brigham Young University)

  • G. Rex Bryce

    (Brigham Young University)

  • David M. Allen

    (University of Kentucky)

Abstract

Procedures for reducing a data matrix to triangular form using orthogonal transformations are presented, e.g., Householder, Givens, and examples these procedures are compard to procedure operating on normal equations. We show how an analysis of variance can be constructed from the triangular reduction of the data matrix. Procedures for calculating sums of squares, degrees of freedom, and expected mean squares are presented. These procedures apply even with mixed models and missing data. It is demonstrated that all statistics needed for inference on linear combinations of parameters of a linear model may be calculated from the triangular reduction of the data matrix. Also included is a test for estimability. We also demonstrate that if the computations are done properly some inference is warranted even when the X matrix is ill-conditioned.

Suggested Citation

  • Del T. Scott & G. Rex Bryce & David M. Allen, 1981. "Orthogonalization-Triangularization Methods in Statistical Computations," Springer Books, in: William F. Eddy (ed.), Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, pages 223-244, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-9464-8_33
    DOI: 10.1007/978-1-4613-9464-8_33
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