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Classical and Bayesian inference robustness in multivariate regression models
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AI
DOI：10.2307/2965413.png)
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摘要
En 中文
We consider a one-to-one correspondence between points z is an element of R(n) - {0} and pairs (y, r), where r > 0 and y lies in some space y, through z = ry. As an immediate consequence, we can represent random variables Z that take values in R(n) - {0} as Z = RY, where R is a positive random variable and Y takes values in y. By fixing the distribution of either R or Y while imposing independence between them, we generate classes of distributions on R(n). Many families of multivariate distributions (e.g., spherical, elliptical, l(q) spherical, upsilon spherical, and anisotropic) can be interpreted in this unifying framework. Some classical inference procedures can be shown to be completely robust in these classes of multivariate distributions. We use these findings in the practically relevant context of regression models. Finally, we present a robust Bayesian analysis and indicate the links between classical and Bayesian results. In particular, for the regression model with lid errors up to a scale, we provide a formal characterization for both classical and Bayesian robustness results concerning inference on the regression parameters.
Keyword:
distribution invariance
independent sampling
likelihood ratio principle
pivotal quantity
posterior inference
scale invariance
