Heteroscedastic nonlinear regression models. (English) Zbl 1183.62045

Summary: We present a generalization of the Bayesian methodology introduced by E. Cepeda and D. Gamerman [Braz. J. Probab. Stat. 14, No. 2, 207–221 (2000; Zbl 0983.62013)] for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.


62F15 Bayesian inference
62J02 General nonlinear regression
62H12 Estimation in multivariate analysis
62J12 Generalized linear models (logistic models)
65C40 Numerical analysis or methods applied to Markov chains


Zbl 0983.62013
Full Text: DOI