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Bayesian Estimation of the Nakagami-m Fading Parameter
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 Title & Authors
Bayesian Estimation of the Nakagami-m Fading Parameter
Son, Young-Sook; Oh, Mi-Ra;
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A Bayesian estimation of the Nakagami-m fading parameter is developed. Bayesian estimation is performed by Gibbs sampling, including adaptive rejection sampling. A Monte Carlo study shows that the Bayesian estimators proposed outperform any other estimators reported elsewhere in the sense of bias, variance, and root mean squared error.
Nakagami-m fading parameter;Bayesian estimation;Gibbs sampling;adaptive rejection sampling;
 Cited by
Abramowitz, M. and Stegun, I. A. (1972). Polygamma Functions. Handbook of Mathematical Functions with Formulas, Graph, and Mathematical Tables, 9th printing, Dover, New York

Adbi, A. and Kaveh, M. (2000). Performance comparison of three different estimators for the Nakagami m parameter using Monte Carlo simulation. IEEE Communications Letters, 4, 119-121 crossref(new window)

Bowman, K. O. and Shenton, L. R. (1988). Properties of Estimators for the Gamma Distribution. Marcel Dekker, New York

Cheng, J. and Beaulieu, N. C. (2001). Maximum-likelihood based estimation of the Nakagami m parameter. IEEE Communications Letters, 5, 101-103 crossref(new window)

Cheng, J. and Beaulieu, N. C. (2002). Generalized moment estimatiors for the Nakagami fading parameter. IEEE Communications Letters, 6, 144-146 crossref(new window)

Damsleth, E. (1975). Conjugate classes for gamma distributions. Scandinavian Journal of Statistics, 2, 80-84

Gelfand, A. E. and Smith, A. F. M. (1990). Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409 crossref(new window)

Gilks, W. R. and Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41, 337-348 crossref(new window)

Greenwood, J. A. and Durand, D. (1960). Aids for fitting the gamma distribution by maximum likelihood. Technometrics, 2, 55-65 crossref(new window)

Miller, R. B. (1980). Bayesian analysis of the two-parameter gamma distribution. Technometrics, 22, 65-69 crossref(new window)

Nakagami, M. (1960). The m-distribution: a general formula of intensity distribution of rapid fading. Statistical Methods in Radio Wave Propagation, Oxford, U.K.; Pergamon Press, 3-36

Ripley, B. D. (1987). Stochastic Simulation. John Wiley & Sons, New York

Son, Y. S. and Oh, M. (2006). Bayesian estimation of the two-parameter gamma Distribution. Communications in Statistics-Simulation and Computation, 35, 285-293 crossref(new window)

Suzuki, H. (1977) A statistical model for urban radio propogation, IEEE transactions on communications, COM-25, 673-680

The MathWorks Inc. (2002). MATLAB/Statistics Toolbox. Version 6.5, Natick, MA

Thom, H. C. (1958). A note on the gamma distribution. Monthly Weather Review, 86, 117-122 crossref(new window)

Wiens, D. P., Cheng, J. and Beaulieu, N. C. (2003). A Class of method of moments estimators for the two-parameter gamma family. Parkistan Journal of Statistics, 29, 29-141

Zhang, Q. T. (2002). A note on the estimation of Nakagami-m fading parameter. IEEE Communications Letters, 6, 237-238 crossref(new window)