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Weighted Model Discrimination When the Errors are Multivariate Normal

Borowiak, Dale S.

Abstract Details

1980, Doctor of Philosophy (Ph.D.), Bowling Green State University, Mathematics/Mathematical Statistics.
A problem of model discrimination arises when we select out of a set of proposed models the one which fits some observed data the best. The model's error terms are assumed to be distributed multivariate normal and one of the proposed models is assumed to be the correct one. We propose a procedure to select the true model based on a pairwise weighted least squares error criterion.Upper bounds on the conditional probability of incorrect selections are derived for both the fixed and, under certain conditions, the estimated models case. Using these bounds tests of the selected model being the true model are constructed. The special case of discrimination between regression models is treated in depth, giving a general selection procedure. Also, the gain resulting when using a sequential sample design procedure in conjunction with weighted discrimination is examined in a simulation study.
Arjun K. Gupta (Advisor)

Recommended Citations

Citations

  • Borowiak, D. S. (1980). Weighted Model Discrimination When the Errors are Multivariate Normal [Doctoral dissertation, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1570783750513246

    APA Style (7th edition)

  • Borowiak, Dale. Weighted Model Discrimination When the Errors are Multivariate Normal. 1980. Bowling Green State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1570783750513246.

    MLA Style (8th edition)

  • Borowiak, Dale. "Weighted Model Discrimination When the Errors are Multivariate Normal." Doctoral dissertation, Bowling Green State University, 1980. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1570783750513246

    Chicago Manual of Style (17th edition)