Modeling of self-report behavior data using the generalized covariates in a uniform and shifted binomial mixture model: An empirical example and Monte Carlo simulation.

2020 ◽  
Vol 25 (1) ◽  
pp. 113-127
Author(s):  
W. Holmes Finch ◽  
Maria E. Hernández Finch
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Mixture Model ◽  
Self Report ◽  
Binomial Mixture Model ◽  
Binomial Mixture

Using a Binomial Mixture Model and Aerial Counts for an Accurate Estimate of Nile Crocodile Abundance and Population Size in the Kunene River, Namibia

2016 ◽  
Vol 46 (2) ◽  
pp. 71 ◽  
Author(s):  
Arnaud Lyet ◽  
Ruhan Slabbert ◽  
William F. Versfeld ◽  
Alison J. Leslie ◽  
Piet C. Beytell ◽  
...  
Keyword(s):  
Population Size ◽  
Mixture Model ◽  
Accurate Estimate ◽  
Binomial Mixture Model ◽  
Binomial Mixture ◽  
Nile Crocodile

On the Chao and Zelterman estimators in a binomial mixture model

2015 ◽  
Vol 22 ◽  
pp. 17-22
Author(s):  
Chang Xuan Mao ◽  
Nan Yang ◽  
Jinhua Zhong
Keyword(s):  
Mixture Model ◽  
Binomial Mixture Model ◽  
Binomial Mixture

Evaluating Wine-Tasting Results and Randomness with a Mixture of Rank Preference Models

2015 ◽  
Vol 10 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Jeffrey C. Bodington
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Mixture Distribution ◽  
Type I ◽  
Type I Errors ◽  
Random Expectation ◽  
Probability Mass ◽  
Mass Functions

AbstractEvaluating observed wine-tasting results as a mixture distribution, using linear regression on a transformation of observed results, has been described in the wine-tasting literature. This article advances the use of mixture models by considering that existing work, examining five analyses of ranking and mixture model applications to non-wine food tastings and then deriving a mixture model with specific application to observed wine-tasting results. The mixture model is specified with Plackett-Luce probability mass functions, solved with the expectation maximization algorithm that is standard in the literature, tested on a hypothetical set of wine ranks, tested with a random-ranking Monte Carlo simulation, and then employed to evaluate the results of a blind tasting of Pinot Gris by experienced tasters. The test on a hypothetical set of wine ranks shows that a mixture model is an accurate predictor of observed rank densities. The Monte Carlo simulation yields confirmatory results and an estimate of potential Type I errors (the probability that tasters appear to agree although ranks are actually random). Application of the mixture model to the tasting of Pinot Gris, with over a 95% level of confidence based on the likelihood ratio and t statistics, shows that agreement among tasters exceeds the random expectation of illusory agreement. (JEL Classifications: A10, C10, C00, C12, D12)

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