The ENTROPY Procedure |
Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., and York, R. L. (1966), Equality of Educational Opportunity, Washington, DC: U.S. Government Printing Office.
Deaton, A. and Muellbauer, J. (1980), “An Almost Ideal Demand System,” The American Economic Review, 70, 312–326.
Golan, A., Judge, G., and Miller, D. (1996), Maximum Entropy Econometrics: Robust Estimation with Limited Data, Chichester, England: John Wiley & Sons.
Golan, A., Judge, G., and Perloff, J. (1996), “A Generalized Maximum Entropy Approach to Recovering Information from Multinomial Response Data,” Journal of the American Statistical Association, 91, 841–853.
Golan, A., Judge, G., and Perloff, J. (1997), “Estimation and Inference with Censored and Ordered Multinomial Response Data,” Journal of Econometrics, 79, 23–51.
Golan, A., Judge, G., and Perloff, J. (2002), “Comparison of Maximum Entropy and Higher-Order Entropy Estimators,” Journal of Econometrics, 107, 195–211.
Good, I. J. (1963), “Maximum Entropy for Hypothesis Formulation, Especially for Multidimensional Contingency Tables,” Annals of Mathematical Statistics, 34, 911–934.
Harmon, A. M., Preckel, P., and Eales, J. (1998), Maximum Entropy-Based Seemingly Unrelated Regression, Master’s thesis, Purdue University.
Jaynes, E. T. (1957), “Information of Theory and Statistical Mechanics,” Physics Review, 106, 620–630.
Jaynes, E. T. (1963), “Information Theory and Statistical Mechanics,” in K. W. Ford, ed., Brandeis Lectures in Theoretical Physics, volume 3, Statistical Physics, 181–218, New York, Amsterdam: W. A. Benjamin Inc.
Kapur, J. N. and Kesavan, H. K. (1992), Entropy Optimization Principles with Applications, Boston: Academic Press.
Kullback, J. (1959), Information Theory and Statistics, New York: John Wiley & Sons.
Kullback, J. and Leibler, R. A. (1951), “On Information and Sufficiency,” Annals of Mathematical Statistics.
LaMotte, L. R. (1994), “A Note on the Role of Independence in Statistics Constructed from Linear Statistics in Regression Models,” The American Statistician, 48, 238–240.
Miller, D., Eales, J., and Preckel, P. (2003), “Quasi-Maximum Likelihood Estimation with Bounded Symmetric Errors,” in Advances in Econometrics, volume 17, 133–148, Elsevier.
Mittelhammer, R. C. and Cardell, S. (2000), “The Data-Constrained GME Estimator of the GLM: Asymptotic Theory and Inference,” Working paper of the Department of Statistics, Washington State University, Pullman.
Mittelhammer, R. C., Judge, G. G., and Miller, D. J. (2000), Econometric Foundations, Cambridge: Cambridge University Press.
Myers, R. H. and Montgomery, D. C. (1995), Response Surface Methodology: Process and Product Optimization Using Designed Experiments, New York: John Wiley & Sons.
Shannon, C. E. (1948), “A Mathematical Theory of Communication,” Bell System Technical Journal, 27, 379–423 and 623–656.
Note: This procedure is experimental.
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