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Bayesian Estimation for Skew Normal Distributions Using Data Augmentation
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 Title & Authors
Bayesian Estimation for Skew Normal Distributions Using Data Augmentation
Kim Hea-Jung;
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In this paper, we develop a MCMC method for estimating the skew normal distributions. The method utilizing the data augmentation technique gives a simple way of inferring the distribution where fully parametric frequentist approaches are not available for small to moderate sample cases. Necessary theories involved in the method and computation are provided. Two numerical examples are given to demonstrate the performance of the method.
Skew normal distribution;Bayesian estimation;MCMC;data augmentation;
 Cited by
Arnold, B. C., Beaver, R. J., Groeneveld, R. A., and Meeker, W. Q.(1993). The nontruncated marginal of a truncated bivariate normal distribution, Psychometrica, 58, 471-478 crossref(new window)

Azzalini, A.(1985). A class of distributions which includes the normal one Scandinavian Journal of Statistics, 12, 171-178 crossref(new window)

Azzalini, A.(1986). Further results on a class of distributions which includes the normal ones, Statistica, 46, 199-208

Azzalini, A. and Capitanio, A.(1999). Statistical applications of the multivariate skew normal distribution, Journal of the Royal Statistical Society, B, 61, 579-602 crossref(new window)

Azzalini, A. and Dalla Valle, A.(1996). The multivariate skew-normal distribution, Biometrika, 83, 715-726 crossref(new window)

Branco, M. D.(2001). A general class of multivariate skew-elliptical distributions, Journal of Multivariate Analysis, 79, 99-113 crossref(new window)

Chen, M. H., Dey, D. K.,and Shao, Q. M.(1999). A new skewed link model for dichotomous quantal response data, Journal of the American Statistical Association, 94, 1172-1185 crossref(new window)

Dennis, J. E., Gay, D. M., and Welsch, R. E. (1981). An adoptive nonlinear least-squares algorithm. ACM Transactions on Mathematical Software, 7, 348-384 crossref(new window)

Devroye, L.(1986). Non-Uniform Random Variate Generation, New York: Springer Verlag

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. parameter crossref(new window)

Gelfand, A. E., Smith, A. F. M., and Lee, T. M. (1992). Bayesian analysis of constrained and truncated data problems using Gibbs sampling, Journal of the American Statistical Association, 87, 523-532 crossref(new window)

Geman, S. and Geman, D.(1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, 721-741 crossref(new window)

Henze, N.(1986). A probabilistic representation of the skew-normal distribution, Scandinavian Journal of Statistics, 13, 271-275

Kim, H. J.(2002). Binary regression with a class of skewed t link models, Communications in Statistics- Theory and Methods, 1863-1886

Lee. P. M. (1997). Bayesian Statistics, 2nd ed. New York: John Wiley

Robert, G. O., Gelman, A., and Gilks, W. R.(1997). Weak convergence and optimal scaling of random walk Metropolis algorithm, Annals of Applied Probability, 7, 110-120 crossref(new window)