Subscribe

Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

BMSA > BMSA Volume 16 > Fit Adequacy of Dichotomous Logit Response Models...
< Back to Volume

Fit Adequacy of Dichotomous Logit Response Models of the Regressor Bernoulli and Binomial Probability Distributions

Full Text PDF

Abstract:

Logit models belong to the class of probability models that determine discrete probabilities over a limited number of possible outcomes. They are often called ‘Quantal Variables’ or ‘Stimulus and Response Models’ in Biological Literature. The conventional R2 measure of goodness-of-fit is problematic in logit models. This has therefore led to the proposal of several alternative goodness-of-fit measures. But researchers in this area have identified the base rate problem in using these several alternative goodness-of-fit measures. This research is an extension of work done by people in this area. Specifically, this research is aimed at investigating the goodness-of-fit performances of eight statistics using the Bernoulli and Binomial distributions as explanatory variables under various scenarios. The study will draw conclusions on the “best” fit. The data for the study was generated through simulation and analysed using the multiple correlation analysis. The findings clearly show that for the Bernoulli Distribution, the goodness-of-fit statistics to use are: RO2, RC2, RM2 and λp; and for the Binomial Distribution, the goodness-of-fit statistics to use are: and RN2 and λp. RO2 stood out as the “best” goodness-of-fit statistics.

Info:

Periodical:
Bulletin of Mathematical Sciences and Applications (Volume 16)
Pages:
38-48
Citation:
G. U. Ugwuanyim and C. J. Ogbonna, "Fit Adequacy of Dichotomous Logit Response Models of the Regressor Bernoulli and Binomial Probability Distributions", Bulletin of Mathematical Sciences and Applications, Vol. 16, pp. 38-48, 2016
Online since:
August 2016
Export:
Distribution:
References:

[1] Cramer, J. S., Logit Models from Economics and Other Fields, Cambridge: Cambridge University Press, (2003).

[2] Gujarati, D. N., Basic Econometrics, 4th ed., New York: McGraw-Hill/Irwin, (2003).

[3] Huck, S. W., Reading Statistics and Research, 6th ed., USA: Pearson Education Inc., (2012).

[4] Pongsapukdee, V. &Kumsiri, T., Goodness-of-fit Tests for Logit Models Based on Probability Levels of Response Categories, Thailand Statistician, 4 (2006), 43 – 61.

[5] Cramer, J.S., The Origins and Development of the Logit Model, (2003b), mars. cram@worldonline. nl.

[6] Train, Discrete Choice Analysis, (2002), http: /elsa. berkeley. edu/~train.

[7] Green, W. H., Econometric Analysis, 5th ed., India: Pearson Education, Inc., (2008).

[8] Menard S., Coefficients of Determination for Multiple Logistic Regression Analysis, The American Statistician, 54 (2000), 1, 17 – 24.

DOI: https://doi.org/10.1080/00031305.2000.10474502

[9] Soderstrom, I.R. &Leitner, D. W., The Effects of Base Rate, Selection Ratio, Sample Size, and Reliability of Predictive Efficiency Indices Associated with Logistic Regression Models, presented at the Annual Meeting of the Mid-Western Educational Research Association, Chicago, October 15 – 18, (1997).

[10] Veall, M. R. & Zimmermann, K. F., Pseudo-R2's in the Ordinal Probit Model, Discussion Paper, (1990a), No. 90 – 15, University of Munich.

[11] Veall, M. R. & Zimmermann, K. F., Pseudo-R2's in the Ordinal Probit Model, Journal of Mathematical Sociology, 16 (1992a), 332 – 342.

[12] Veall, M. R. & Zimmermann, K. F., Evaluating Pseudo-R2's for Binary Probit Models, Quantity and Quality, 28 (1994a), 151 – 164.

DOI: https://doi.org/10.1007/bf01102759

[13] Hagel, T. M. & Mitchell, G. E., Goodness-of-Fit Measures for Probit and Logit, American Journal of Political Science, 36 (1992), 762-784.

[14] Laitila, T., A Pseudo-R2 Measure for Limited and Qualitative Dependent Variable Models, Journal of Econometrics, 56 (1993), 341 – 356.

DOI: https://doi.org/10.1016/0304-4076(93)90125-o

[15] Windmeijer, F.A.G., Goodness-of-fit measures in binary choice models, Econometric Reviews 14(1995), 101-116.

DOI: https://doi.org/10.1080/07474939508800306
Show More Hide
Cited By:
This article has no citations.