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Solving implicit equations in psychometric data analysis

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Abstract

Many data analysis problems in psychology may be posed conveniently in terms which place the parameters to be estimated on one side of an equation and an expression in these parameters on the other side. A rule for improving the rate of convergence of the iterative solution of such equations is developed and applied to four problems: the principal axis communality problem, individual differences multidimensional scaling,L P norm multiple regression, andL P norm factor analysis of a data matrix. The rule results in substantially faster solutions or in solutions where none would be possible without the rule.

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Authors and Affiliations

  1. McGill University, Canada

    J. O. Ramsay

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  1. J. O. Ramsay
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This work was supported by National Research Council of Canada grant APA 320 to the author.

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Ramsay, J.O. Solving implicit equations in psychometric data analysis. Psychometrika 40, 337–360 (1975). https://doi.org/10.1007/BF02291762

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  • DOI: https://doi.org/10.1007/BF02291762

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