An algorithm for generating positively correlated Beta-distributed random variables with known marginal distributions and a specified correlation

Computational Statistics and Data Analysis

Volumn 46, Issue 2, 2004, Pages 397-406

  Magnussen, Steen ^a  

^a PACIFIC FORESTRY CENTRE   (Canada)

Author keywords

Expected covariance; Expected variance; Standard Gamma distribution

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CORRELATION METHODS; STATISTICAL TESTS;

GAMMA DISTRIBUTIONS; MARGINAL DISTRIBUTIONS; RANDOM VARIABLES; TAYLOR-SERIES APPROXIMATION;

PROBABILITY DISTRIBUTIONS;

ALGORITHM;

EID: 16544383690     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(03)00169-5     Document Type: Article

References (26)

1. Anderson A.B. Detecting changes in natural resources using land condition trend analysis data Env. Mgmt. 29 3 2002 428-436
2. Agresti A. Caffo B. Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures Am. Stat. 54 4 2000 280-288
3. Casella, G., Berger, R.L., 2002. Statistical Inference. Duxbury, London, pp. 1-660.
4. Cochran, W.G., 1977. Sampling Techniques. Wiley, New York, pp. 1-380.
5. Collett, D., 1991. Modelling Binary Data. Chapman - Hall, London, pp. 1-369.
6. Corona P. Chirici G. Marchetti M. Forest ecosystem inventory and monitoring as a framework for terrestrial natural renewable resource survey programmes Plant Biosyst. 136 1 2002 69-82
7. Crowder M. Gaussian estimation for correlated binomial data J. R. Stat. Soc. B 47 2 1985 229-237
8. Devroye, L., 1986. Non-Uniform Random Variate Generation. Springer, New York, pp. 1-356.
9. Efron, B., Tibshirani, R.J., 1993. An Introduction to the Bootstrap. Chapman - Hall, Boca Raton, pp. 1-436.
10. Johnson M.E. Multivariate Statistical Simulation 1987 Wiley New York
11. Johnson, N.L., Kotz, S., 1970. Continuous Univariate Distributions. Houghton Mifflin, Boston, p. 719.
12. Johnson N.L. Kotz S. Distributions In Statistics: Continuous Multivariate Distributions 1976 Wiley New York
13. Johnson, N.L., Kotz, S., Balakrishnan, N., 1986. Discrete Multivariate Distributions. Wiley, New York, pp. 1-330.
14. Lloyd C.J. Confidence intervals from the difference between two correlated proportions J. Am. Stat. Assoc. 85 412 1999 1154-1158
15. Loukas S. Simple methods for computer generation of bivariate beta random variables J. Statist. Comput. Simul. 20 1984 145-152
16. Magnussen S. Fast pre-survey computation of the mean spatial autocorrelation in large plots composed of a regular array of secondary sampling units Math. Modelling Sci. Comput. 13 3-4 2001 204-217
17. Massmann W.J. Foliage distribution in old-growth coniferous tree canopies Can. J. For. Res. 12 1 1981 10-17
18. Michael J.R. Schucany W.R. The mixture approach for simulating bivariate distributions with specified correlations Am. Stat. 56 1 2002 48-54
19. Mosimann J.E. On the compound multinomial distribution, the multivariate β-distribution, and correlations among proportions Biometrika 49 1/2 1962 65-82
20. Magnussen, S., Boudewyn, P., Gillis, M.D., 1998. Towards a plot size for Canada's national forest inventory. In: M. Hansen, T.E. Burk (Eds.), Integrated Tools for Natural Resources Inventories in the 21st Century, USDA Forest Service, Boise Idaho, 116-128.
21. Park C.G. Park T. Shin D.W. A simple method for generating correlated binary variates Am. Stat. 50 4 1996 306-310
22. Plackett R.L. A class of bivariate distributions J. Am. Stat. Assoc. 60 1965 516-522
23. Pitkanen S. The use of diversity indices to assess the diversity of vegetation in managed boreal forests For. Ecol. Mgmt. 112 1-2 1998 121-137
24. Robert, C.P., Casella, G., 1999. Monte Carlo Statistical Methods. Springer, New York, pp. 1-507.
25. Schreuder H.T. Czaplewski R. Bailey R.G. Combining mapped and statistical data in forest ecological inventory and monitoring-Supplementing an existing system Env. Monitor. Assess. 56 3 1999 269-291
26. Yaglom, A.M., 1986. Correlation Theory of Stationary and Related Random Functions. Vol. I: Basic results. Springer, New York.