Multiplicative-Binomial Distribution: Some Results on Characterization, Inference and Random Data Generation

Elamir, Elsayed A. H.

doi:10.2991/jsta.2013.12.1.8

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…[8] presented an elegant characteristics of the multiplicative binomial distribution including its four central moments. His treatment includes generation of random data from the distribution as well as the likelihood profiles and several examples-some of which are similarly employed in this presentation.…”

Section: The Multiplicative Binomial Model-mbmmentioning

confidence: 99%

“…His treatment includes generation of random data from the distribution as well as the likelihood profiles and several examples-some of which are similarly employed in this presentation. Following [8] the probability π of success for the Bernoulli trial, that is, P(Y = 1) can be computed from the following expression in (4) as:…”

Section: The Multiplicative Binomial Model-mbmmentioning

confidence: 99%

See 1 more Smart Citation

On Modeling the Double and Multiplicative Binomial Models as Log-Linear Models

Lawal¹

2018

Adv Comput Sci

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In this paper we have fitted the double binomial and multiplicative binomial distributions as log-linear models using sufficient statistics. This approach is not new as several authors have employed this approach, most especially in the analysis of the Human sex ratio in [1]. However, obtaining the estimated parameters of the distributions may be problematic, especially for the double binomial where the parameter estimate of π may not be readily available from the Log-Linear (LL) parameter estimates. Other issues associated with the LL approach is its implementation in the generalized linear model with covariates. The LL uses far more parameters than the procedure that employs conditional log-likelihoods functions where the marginal likelihood functions are minimized over the parameter space. This is the procedure employed in SAS PROC NLMIXED. The two procedures are essentially equivalent for frequency data. For models with covariates, the LL uses far more parameters and the marginal likelihood functions approach are employed here on three data set having covariates.

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Section: The Multiplicative Binomial Model-mbmmentioning

confidence: 99%

Section: The Multiplicative Binomial Model-mbmmentioning

confidence: 99%

On Modeling the Double and Multiplicative Binomial Models as Log-Linear Models

Lawal¹

2018

Adv Comput Sci

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show abstract

“…In [11], the author presented some elegant characteristics of the multiplicative binomial distribution, including its four central moments. The author's treatment includes generation of random data from the distribution as well as the likelihood profiles and several examples-some of which are similarly employed in this presentation.…”

Section: The Multiplicative Binomial (Mbm) Modelmentioning

confidence: 99%

“…Following [11], the probability π of success for the Bernoulli trial, that is, P (Y = 1) can be computed from the following expression in (3.2) as:…”

Section: The Multiplicative Binomial (Mbm) Modelmentioning

confidence: 99%

Employing the Double, Multiplicative and The Com-Poisson Binomial Distributions for modeling Over and Under-dispersed Binary Data

Lawal

2017

JAMCS

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In this paper, we compare the performances of several models for fitting over-dispersed binary data. The distribution models considered in this study include the binomial (BN), the betabinomial (BB), the multiplicative binomial (MBM), the Com-Poisson binomial (CPB) and the double binomial (DBM) models. Applications of these models to several well known data sets exhibiting under-dispersion and over-dispersion were considered in this paper. We applied these models to two frequency data sets and two data sets with covariates that have been variously analysed in the literature. The first relates to the Portuguese version of Duke Religiosity Index in a sample of 273 (202 women, 71 Male) postgraduate students of the faculty of Medicine of University of Sao Paulo. The second set that employs the Generalize Linear Model (GLM) is the correlated binary data which studies the cardiotoxic effects of doxorubicin chemoteraphy on the treatment of acute lymphoblastic leukemia in childhood. In the first data set, we have a single covariate, Sex (0,1) and two covariates in the second data set (dose and time). Our results indicate that all the models considered here (excluding the binomial) behave reasonably well in modeling over-dispersed binary data with or without covariates, although both the multiplicative binomial and the double binomial models slightly behave better for these

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Determinants of Effective Telework: An Evidence from Telecommunication Sector in Bahrain

Bawab

Elamir

2021

2021 International Conference on Data Analytics for Business and Industry (ICDABI)

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Multiplicative-Binomial Distribution: Some Results on Characterization, Inference and Random Data Generation

Cited by 5 publications

References 14 publications

On Modeling the Double and Multiplicative Binomial Models as Log-Linear Models

On Modeling the Double and Multiplicative Binomial Models as Log-Linear Models

Employing the Double, Multiplicative and The Com-Poisson Binomial Distributions for modeling Over and Under-dispersed Binary Data

Determinants of Effective Telework: An Evidence from Telecommunication Sector in Bahrain

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