STATISTICAL METHOD FOR EMPIRICAL TESTING OF COMPETING THEORY IN ANAMBRA STATE POLYTECHNIC, MGBAKWU..
DOI:
https://doi.org/10.53555/nncse.v9i5.1696Keywords:
Empirical testing, Competing theories, statistical method, finite Mixture-Models, probability model.Abstract
Empirical testing of competing theories lies at the heart of social science and applied Science research. We demonstrate that a well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated either from statistical models implied by one of the competing theories or more generally from a weighted combination of multiple statistical models under consideration. Researchers can then estimate the probability that a specific observation is consistent with each rival theory. By modeling this probability with covariates, one can also explore the condition under which a particular theory applies. We discuss a principles way to identify a list of observations that are statistically consistent with each theory and propose measure of the overall performance of each competing theory. We illustrate the relatives’ advantages of our method over existing methods through empirical and stimulation studies.
References
Achen, C. H.( 2005.) “Let’s Put Garbage-Can Regressions and Garbage-Can Probits Where They
a. Belong.”Conflict Man¬agement and Peace Science (p): 327–339.
Beck, N., and J. N. Katz. (2007). “Random Coefficient Models for Time-Series-Cross-Section
a. Data:Monte Carlo Experi-ments.” Political Analysis 15(p): 182–195.
Benjamini, Y., and Y. Hochberg. (1995). “Controlling the False Discovery Rate: A Practical and Powerful
Approach to Mul¬tiple Testing.” Journal of the Royal Statistical Society, Series B 57(p): 289–300
Braumoeller,B.(2003.) “Causal Complexity and the Study of Politics.” Political Analysis 11(p): 209–333.
Clarke, K. A.(2000.) “Testing Non-nested Models of Interna¬tional Relations: Reevaluating
a. Realism.”American Journal of Political Science 45(3): 724–44.
Davidson, R., and J. G. MacKinnon .(1981). “Several Tests for Model Specification in the Presence of
a. Alternative Hypothe-ses.” Econometrica 49(3): 781–93.
Dempster, A. P., N. M. Laird, and D. B. Rubin.( 2017) “Maximum Likelihood from Incomplete Data via
a. the EM Algorithm (with Discussion).” Journal of the Royal Statistical Society, Series B, Methodological p. 1–37.
Friedman, M. (1966.) “The Methodology of Positive Eco-nomics.“ In Essays in Positive Economics.
a. Chicago: University of Chicago Press,P. 3–160.
Fru¨hwirth-Schnatter, S.( 2007). Finite Mixture and Markov Switching Models. New York: Springer.
.
Harrison, G. W., and E. E. Rutstro¨m. (2009.) “Expected Utility Theory and Prospect Theory: One
Wedding and a Decent Funeral.” Experimental Economics (P): 133–158
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
