J. X. WU ET AL.

Copyright © 2012 SciRes. OJS

280

tical Analysis of Finite Mixture Distributions,” John Wiley

and Sons, New York, 1985.

[2] J. Lawless, “Negative Binomial and Mixed Poisson Re-

gression,” Canadian Journal of Statistics, Vol. 15, No. 3,

1987, pp. 209-225. doi:10.2307/3314912

[3] D. C. Heibron, “Generalized Linear Models for Altered

Zero Probability and Overdispersion in Count Data,” SIMS

Technical Report No. 9, University of California, San

Francisco, 1989.

[4] R. Schall, “Estimation in Generalized Linear Models with

Random Effects,” Biometrika, Vol. 78, No. 4, 1991, pp.

719-727. doi:10.1093/biomet/78.4.719

[5] C. E. McCulloch, “Maximum Likelihood Algorithms for

Generalized Linear Mixed Models,” Journal of American

Statistical Association, Vol. 92, No. 437, 1997, pp. 162-

170.

[6] D. B. Hall, “Zero-Inflated Poisson and Binomial Regres-

sion with Random Effects: A Case Study,” Biometrics,

Vol. 56, No. 4, 2000, pp. 1030-1039.

doi:10.1111/j.0006-341X.2000.01030.x

[7] L. Zhang, J. Wu and W. D. Johnson, “Empirical Study of

Six Tests for Equality of Populations with Zero-Inflated

Continuous Distributions,” Communications in Statistics

—Simulation and Computation, Vol. 39, No. 6, 2010, pp.

1196-1211. doi:10.1080/03610918.2010.489169

[8] G. Casella and R. L. Berger, “Statistical Inference,” Dux-

bury Inc., San Francisco, 2002.

[9] A. Wald, “Tests of Statistical Hypotheses Concerning

Several Parameters When the Number of Observations Is

Large,” Transactions in American Mathematical Society,

Vol. 54, No. 3, 1943, pp. 426-482.

[10] E. S. Edgington, “Statistical Inference and Nonrandom

Samples,” Psychological Series A, Vol. 66, No. 6, 1966,

pp. 485-487. doi:10.1037/h0023916

[11] B. E. Wampold and N. L. Worsham, “Randomization

Tests for Multiple Baseline Designs,” Behavioral As-

sessment, Vol. 8, 1986, pp. 135-143.

[12] R. C. Blair and W. Karniski, “An Alternative Method for

Significance Testing of Waveform Difference Potentials,”

Psychophysiology, Vol. 30, No. 5, 1993, pp. 518-524.

doi:10.1111/j.1469-8986.1993.tb02075.x

[13] D. C. Adams and C. D. Anthony, “Using Randomization

Techniques to Analyze Behavioural Data,” Animal Be-

haviour, Vol. 61, No. 1, 1996, pp. 733-738.

doi:10.1006/anbe.2000.1576

[14] J. Ludbrook and H. Dudley, “Why Permutation Tests Are

Superior to t and F Tests in Biomedical Research,”

American Statistician Association, Vol. 52, No. 2, 1998,

pp. 127-132.

[15] A. F. Hayes, “Randomization Tests and Equality of Vari-

ance Assumption When Comparing Group Means,” Ani-

mal Behaviour, Vol. 59, No. 3, 2000, pp. 653-656.

doi:10.1006/anbe.1999.1366

[16] L. H. Koopman, “Introduction of Contemporary Statisti-

cal Methods,” 2nd Edition, Duxbury Press, Boston, 1981.

[17] J. Aitchison, “On the Distribution of a Positive Random

Variable Having a Discrete Probability Mass at the Ori-

gin,” Journal of American Statistical Association, Vol. 50,

No. 271, 1995, pp. 901-908.

[18] S. C. Wang, “Analysis of Zero-Heavy Data Using a Mix-

ture Model Approach,” Ph.D. Thesis, Virginia Polytech-

nic Institute and State University, Blacksburg, 1998.