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Double generalized linear model for tissue culture proportion data: a Bayesian perspective. (English) Zbl 1218.62072

Summary: Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. We propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

MSC:

62J12 Generalized linear models (logistic models)
62F15 Bayesian inference
65C40 Numerical analysis or methods applied to Markov chains
62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

OpenBUGS; CODA; GLIM; R; BUGS; BayesDA
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Full Text: DOI Link

References:

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