Robustness of sample mean and sample median under restrictions on outliers
Robustness of tests based on spacings in the exponential model
Robustness of the best linear unbiased estimator and predictor in linear regression models
If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor.
Robustness of the sample correlation -- the bivariate lognormal case.
Robustness to non-normality of various tests for the one-sample location problem.
Sampling inference, Bayes' inference and robustness in the advancement of learning.
Scientific learning is seen as an iterative process employing Criticism and Estimation. Sampling theory use of predictive distributions for model criticism is examined and also the implications for significance tests and the theory of precise measurement. Normal theory examples and ridge estimates are considered. Predictive checking functions for transformation, serial correlation, and bad values are reviewed as is their relation with Bayesian options. Robustness is seen from a Bayesian view point...
Sensitivity analysis of -estimators of non-linear regression models
An asymptotic formula for the difference of the -estimates of the regression coefficients of the non-linear model for all observations and for observations is presented under conditions covering the twice absolutely continuous -functions. Then the implications for the -estimation of the regression model are discussed.
Sequential confidence intervals based on robust estimators
Several applications of divergence criteria in continuous families
This paper deals with four types of point estimators based on minimization of information-theoretic divergences between hypothetical and empirical distributions. These were introduced (i) by Liese and Vajda [9] and independently Broniatowski and Keziou [3], called here power superdivergence estimators, (ii) by Broniatowski and Keziou [4], called here power subdivergence estimators, (iii) by Basu et al. [2], called here power pseudodistance estimators, and (iv) by Vajda [18] called here Rényi pseudodistance...
Some asymptotic results for robust procedures for testing the constancy of regression models over time
Stability and conditional Γ-minimaxity in Bayesian inference
Two concepts of optimality corresponding to Bayesian robust analysis are considered: conditional Γ-minimaxity and stability. Conditions for coincidence of optimal decisions of both kinds are stated.
Stability, sensitivity and sensitivity of characterizations
Subset selection of the largest location parameter based on -estimates
The problem of selecting a subset of polulations containing the population with the largest location parameter is considered. As a generalization of selection rules based on sample means and on sample medians, a rule based on -estimates of location is proposed. This rule is strongly monotone and minimax, the risk being the expected subset size, provided the underlying density has monotone likelihood ratio. The problem of fulfilling the -condition is solved explicitly only asymptotically, under...
Tests of some hypotheses on characteristic roots of covariance matrices not requiring normality assumptions
Test statistics for testing some hypotheses on characteristic roots of covariance matrices are presented, their asymptotic distribution is derived and a confidence interval for the proportional sum of the characteristic roots is constructed. The resulting procedures are robust against violation of the normality assumptions in the sense that they asymptotically possess chosen significance level provided that the population characteristic roots are distinct and the covariance matrices of certain quadratic...
Tests robustes pour des variables indépendantes et des chaînes de Markov
The application of minimax fit in linear model and its extension in generalised linear models.
The effect of a single point on correlation and slope.
The generalized p-point method of estimation of regression coefficients
The infinitesimal robustness of tests against dependence
The least trimmed squares. Part I: Consistency
The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al. [HamRonRouSta]) is proved under general conditions. The assumptions employed in paper are discussed in details to clarify the consequences for the applications.