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Continuous Parameter Estimation Model: Expanding the Standard Statistical Paradigm
Paper Type  |
Contributed Paper |
Title |
Continuous Parameter Estimation Model: Expanding the Standard Statistical Paradigm |
Author |
Richard L. Gorsuch |
Email |
rgorsuch@fuller.edu |
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Abstract: Our classical paradigm of statistics considers parameters such as means, standard deviations, correlations, and standard errors as discrete parameters. This paper shows we can expand the ìDiscrete Parameter Estimation Modelî (DPEM) to consider most parameters as continuous, both in the sense of (a) the parameter varying continuously as a function of other variables and of (b) each case having a separate score on a continuum related to each parameter. The ìContinuous Parameter Estimation Modelî (CPEM) is a broader paradigm which includes DPEM and includes analyzing parameters such as correlations and standard errors as conditional, that is, to vary as a function of other variables, through the use of standard statistics. (As a paradigm paper, there are no unique derivations nor new statistical formulas, but there is an expanded perspective for how variables are conceptualized and how analyzes may proceed.)
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Start & End Page |
11 - 21 |
Received Date |
2004-02-05 |
Revised Date |
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Accepted Date |
2004-11-29 |
Full Text |
Download |
Keyword |
CPEM, DPEM |
Volume |
Vol.32 No.1 (JANUARY 2005) |
DOI |
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Citation |
Gorsuch R.L., Continuous Parameter Estimation Model: Expanding the Standard Statistical Paradigm, Chiang Mai Journal of Science, 2005; 32(1): 11-21. |
SDGs |
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