0000000000999150

AUTHOR

Michael Lavine

showing 2 related works from this author

An overview of robust Bayesian analysis

1994

Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the subject, one that is accessible to statisticians outside the field. Recent developments in the area are also reviewed, though with very uneven emphasis. © 1994 SEIO.

Statistics and ProbabilityComputer scienceBayesian probabilitycomputer.software_genreData scienceField (computer science)Bayesian robustnessN/ARobust Bayesian analysisPrior probabilityData miningSensitivity (control systems)Statistics Probability and Uncertaintycomputer
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Exploring regression structure with graphics

1993

We investigate the extent to which it may be possible to carry out a regression analysis using graphics alone, an idea that we refer to asgraphical regression. The limitations of this idea are explored. It is shown that graphical regression is theoretically possible with essentially no constraints on the conditional distribution of the response given the predictors, but with some conditions on marginal distribution of the predictors. Dimension reduction subspaces and added variable plots play a central role in the development. The possibility of useful methodology is explored through two examples.

Statistics and ProbabilityPolynomial regressionEconometricsSufficient dimension reductionPartial regression plotRegression analysisCross-sectional regressionConditional probability distributionStatistics Probability and UncertaintyMarginal distributionSegmented regressionMathematicsTest
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