SYMMETRICAL BERNOULLI DISTRIBUTIONS AND GENERALIZED BINOMIAL DISTRIBUTIONS

被引:11
|
作者
QU, Y [1 ]
GREENE, T [1 ]
PIEDMONTE, MR [1 ]
机构
[1] CLEVELAND CLIN EDUC FDN,DEPT BIOSTAT & EPIDEMIOL,CLEVELAND,OH 44106
关键词
BETA-BINOMIAL DISTRIBUTION; CORRELATED BINOMIAL DISTRIBUTION; MULTI-VARIATE NORMAL DISTRIBUTION; LATENT VARIABLE MODEL; OCHI-PRENTICE PROBIT REGRESSION MODEL; POLYA-EGGENBERGER DISTRIBUTION; TETRACHORIC CORRELATION;
D O I
10.1002/bimj.4710350503
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The generalized binomial distribution is defined as the distribution of a sum of symmetrically distributed Bernoulli random variates. Several two-parameter families of generalized binomial distributions have received attention in the literature, including the Polya urn model, the correlated binomial model and the latent variable model. Some properties and limitations of the three distributions are described. An algorithm for maximum likelihood estimation for two-parameter generalized binomial distributions is proposed. The Polya urn model and the latent variable model were found to provide good fits to sub-binomial data given by Parkes. An extension of the latent variable model to incorporate heterogeneous response probabilities is discussed.
引用
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页码:523 / 533
页数:11
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