Estimation by combining MinxEnt principle and time series analysis

WSEAS Transactions on Mathematics

Volumn 5, Issue 6, 2006, Pages 677-683

  Shamilov, Aladdin ^a   Giriftinoglu, Cigdem ^a  

^a ANADOLU UNIVERSITY   (Turkey)

Author keywords

Estimation of probability distribution; Kullback's minimum cross entropy principle; Kullback Leibler entropy measure

Indexed keywords

CONSTRAINT THEORY; FORECASTING; LAGRANGE MULTIPLIERS; PROBABILITY DISTRIBUTIONS; TIME SERIES ANALYSIS;

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA); KULLBACK'S MINIMUM CROSS-ENTROPY (MINXENT) PRINCIPLE; KULLBACK-LEIBLER ENTROPY MEASURE; NEWTON METHOD; VISUAL BASIC PROGRAM;

ESTIMATION;

EID: 33745495001     PISSN: 11092769     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Papoulis, A., Probability, Random variables, and stochastic processes, McGraw-Hill, Singapore, USE, 1991.
2. Cover, T.M. and Thomas, J.A., Elements of Information Theory, A Wiley-Interscience Publication, New York, USA, 1992.
3. Golan, A., Judge, G. and Miller, D., Maximum Entropy Econometrics: Robust Estimation with Limited Data, John Wiley & Sons, New York, USA, 1996.
4. Karmeshu and Pal N.R., Entropy Measures, Maximum Entropy Principle and Emerging Applications, Springer, New York, USA, 2003.
5. Kullback, S., Information Theory and Statistics, Dover Publications, New York, USA, 1968.
6. Kapur, J.N. and Kesavan, H.K., Entropy Optimization Principles with Applications, Academic Press, New York, USA, 1992.
7. Wu, N., The Maximum Entropy Method, Springer, Berlin, Germany 1997.
8. Jaynes, E.T. "Information Theory and Statistical Mechanics." Physical Reviews, 106: 620-630, 1957.
9. Kullback, S. and Leibler, R.A. "On Information and Sufficiency." Ann. Math. Stat., 22: 79-86, 1951.
10. Kapur, J.N. and Kesavan, H.K., "Inverse MaxEnt and MinxEnt Principles and their Applications." In PaulF. Fougere, editor, Maximum Entropy and Bayesian Methods, pp. 433-450, Kluwer Academic Publishers, New York, 1990.
11. Kesavan, H.K. and Kapur, J.N., "Maximum Entropy and Minimum Cross-Entropy Principles: Need for a Broader Perspective." In Paul F. Fougere, editor, Maximum Entropy and Bayesian Methods, pp. 419-432, Kluwer Academic Publishers, New York, 1990.
12. Shannon, C.E., "A Mathematical Theory of Communication." Bell System Tech. J, 27: 379-423, 623-659, 1948.
13. http://www.die.gov.tr/TURKIS/SONIST/ENERJI/enerji.html
14. Shamilov A., Comparision of Classical Estimate Methods Based On MaximumEntropy Distribution, Ordered statistical data: Approximation, Bounds and characterizations, Izmir, Turkey, 2005.
15. http://www.statsoft.com/textbook/sttimser.html#arima
16. Box, G. E. P., and Jenkins, G., Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
17. J.C. Hoff, A Practical Guide to Box-Jenkins Forecasting; and W. Vandaele, Applied Time Series and Box-Jenkins Models, Journal of American Statistical Association, 79, 1984.
18. A. Pankratz, Forecasting with Univariate Box-Jenkins Models, Concepts and Cases. John Wiley and Sons, 1983.
19. Bails, Dale G. and Larry C. Peppers, Business Fluctuations: Forecasting Techniques and Application,Englewood Cliffs NJ: Prentice-Hall Inc., 1982.
20. Aslanargun A., Mammadov M., Yazici B., Yolacan S., "Comparison of ARIMA, Neural Networks and Hybrid Models in Time Series Tourist Arrival Forecasting" Journal of Statistical Computation and Simulation (in press), 2006