Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...
A random sample of curves can be usually thought of as noisy realisations of a compound stochastic process X(t) = Z{W(t)}, where Z(t) produces random amplitude variation and W(t) produces random ...
In this paper, we introduce the class of the nonlinear overdispersed models and derive general formulae for the biases of the maximum likelihood estimators of the parameters in these models, thus ...
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