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Abraham, B. & Ledolter, J. (1983). Statistical Methods for Forecasting. John Wiley and Sons, Inc.

Aksu, C. & Gunter, S. I. (1992). An Empirical Analysis of the Accuracy of SA, OLS, ERLS and NRLS. Combination Forecasting, International Journal of Forecasting, 8, 28-43.

Bates, J. M. & Granger, C.W.J. (1969). The Combination of Forecasts. Operations Research Quarterly, 20, 451-469.

Bordley, R.F. (1982). The Combination of Forecasts: A Bayesian Approach. J. Operational Research Society, 33, 171-174.

Bunn, D. W. (1985). Statistical Efficiency in the Linear Combination of Forecasts. International Journal of Forecasting, 1, 151-163.

Clemen, R.T. (1989). Combining Forecasts: A Review and Annotated Bibliography. International Journal of Forecasting, 5, 559-583.

Clemen, R.T. (1993, Fall). Combining Forecasts: Progress Since 1989. The Forum. The Joint Newsletter of the International Association of Business Forecasting and The International Institute of Forecasters, 6(3), 11.

DeGroot, M. H. & Feinberg, S. E. (1983). The Comparison and Evaluation of Forecasters. The Statistician, 32, 12-22.

Devroye, L. (1987). A Course in Density Estimation. Boston: Birkhauser.

Gamerman, D. (1997). Markov chain Monte Carlo: Stochastic Simulation for Bayesian Inference. London: Chapman and Hall.

Gelfand, A. E. & Smith, A. F. M. (1990). Sampling Based Approach to Calculating Marginal Densities. J. Am. Statist. Ass., 85, 398-409.

Geman, S. & Geman, D. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattn. Anal. Math. Intell., 6, 721-741.

Gilks, W. R. & Wild, P. (1992). Adaptive Rejection Sampling for Gibbs Sampling. Appl. Statist. 41(2), 337-348.

Gilks, W. R.,Richardson, S., & Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London, Chapman and Hall.

Granger, C. W. J. & Newbold, P. (1986). Forecasting Economic Time Series. Orlando, FL: Academic Press.

Gupta, S. & Wilton, P.C. (1987, March). Combination of Forecasts: An Extension. Management Science, 33(3), 356-372.

Hastings, W. K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57, 97-109.

Kendall, M.,Stuart A. & Ord, K. (1977). The Advance Theory of Statistics. London: Charles Griffin Co. Ltd.

Kotz, S. & Johnson, N. L. (1988). Encyclopedia of Statistical Science. New York: Wiley.

Metropolis, N.,Rosenbluth, A. W.,Rosenbluth, M.N.,Teller, A.H., & Teller, E. (1953). Equations of state calculations by fast computing machines. J. Chem. Phys., 21, 1087-1091.

Öiler, L. E. (1978). A Method for Polling Forecasts. Journal of the Operational Research Society, 29(1), 55-63.

Shun, Z. & McCullagh, P. (1995). Laplace Approximation of High Dimensional Integrals. J. R. Statist. Soc., B 57, 749-760.

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.

Tierney, L. & Kadane, J. B. (1986). Accurate Approximations for Posterior Moments and Marginal Densities. J. Am. Statist. Ass., 81, 82-90.

Troutt, M. D. (1993). Vertical Density Representation and a Further Remark on the Box-Müller Method. Statistics, 24, 81-83.

Winkler, R. L. & Makridakis, S. (1983). The Combination of Forecasts. J.R. Statist. Soc. A., 146, 150-157.

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Managing Data Mining Technologies in Organizations(c) Techniques and Applications
Managing Data Mining Technologies in Organizations: Techniques and Applications
ISBN: 1591400570
EAN: 2147483647
Year: 2003
Pages: 174 © 2008-2017.
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