Florens J.P., Mouchart M., Raoult J.P., Simar L., Smith A.F.M. (Eds.) Specifying Statistical Models: From Parametric to Non-Parametric, Using Bayesian or Non-Bayesian Approaches

Springer, 1983. — 215 p.During the last decades, the evolution of theoretical statistics has been marked by a considerable expansion of the number of mathematically and computationaly tractable models. Faced with this inflation, applied statisticians feel more and more uncomfortable: they are often hesitant about their traditional (typically parametric) assumptions. such as normal and i.i.d., ARMA forms for time-series. etc ., but are at the same time afraid of venturing into the jungle of less familiar models. The problem of the justification for taking up one model rather than another one is thus a crucial one, and can take different forms.