Titlebook: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Hu Helmut Rieder Book 1996 Springer-Verlag New York, In

貪心

Robust Regression with a Categorical Covariable,projections, the number of subsets may be drastically reduced. Simulations and examples show that the overall computation time is substantially lower than that of the straightforward algorithm. The method is illustrated with a real data set.

排他

Robust Estimation in the Logistic Regression Model,hen the covariates follow a multivariate normal distribution. We illustrate the behavior of these estimates with two data sets. Finally, we mention some possible extensions of these M-estimates for a multinomial response.

Morose

The m out of n Bootstrap and Goodness of Fit Tests with Double Censored Data,stribution of the test statistic. We show that if the . out of . bootstrap with . → ∞, . = o(.) is used to set the critical value of the test, the proposed testing procedure is asymptotically level ., has the correct asymptotic power function for . alternatives and is asymptotically consistent.

MURKY

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,Bootstrap Variable—Selection and Confidence Sets, of the data. A naive use of the bootstrap in this problem produces risk estimators for candidate variable-selections that have a strong upward bias. Resampling from a less overfitted model removes the bias and leads to bootstrap variable-selections that minimize risk asymptotically. A related boots

泰然自若

Robust Estimation in the Logistic Regression Model,and asymptotically normal. Their robustness is studied through the computation of asymptotic bias curves under point-mass contamination for the case when the covariates follow a multivariate normal distribution. We illustrate the behavior of these estimates with two data sets. Finally, we mention so

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