Titlebook: Specifying Statistical Models; From Parametric to N J. P. Florens,M. Mouchart,A. F. M. Smith Conference proceedings 1983 Springer Science+B

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袖章

锡箔纸

0930-0325 and computationaly trac­ table models. Faced with this inflation. applied statisticians feel more and more un­ comfortable: 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

万灵丹

Protecting Against Gross Errors: The Aid of Bayesian Methods,ed by this family..The statistician often needs to protect himself against the consequences of a gross error relative to the basic hypothesis: either a specification error for the functionnal form of p(x|θ), or the treatment of outliers. It will be shown in this paper that the Bayesian approach offe

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The Probability Integral Transformation for Non Necessarily Absolutely Continuous Distribution Funcomposition of Q by Q. is the uniform probability (or Lebesgue measure) on [0,1].; Q. is called the . (p.i.t) of Q. . being a class of probability measures on a space of observations Z, let φ be a . sufficient mapping from Z to a space Y, and ξ a mapping from Z to ℝ.; let, for every z, .(z) be the pr

ARCH

Simulation in the General First Order Autoregressive Process (Unidimensional Normal Case),discretization parameters for the computation of non parametric kernel estimators for these processes; then, we investigate some “bad” cases and some “good” cases; it seems that effective computations generally give better results than those obtained in theory, finally we study the relation between

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