Titlebook: Robustness of Statistical Methods and Nonparametric Statistics; Dieter Rasch,Moti Lal Tiku Book 1984 Academy of Agricultural Sciences of t

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Optimal Designs for Contaminated Linear Regression, which may be modelled by including additive contamination functions. These may be caused for example in polynomial regression by higher order terms, which have not been included in the ideal model. The paper shows how the concepts of optimal design theory for linear regression can be extended to co

拍翅

綁架

Analogy of the Linear Regression and the Two-Sample Problem in Robustness Investigations,confidence interval for the slope may be investigated, among others. When doing this for the simplest case of only two different values of x, the t-test becomes equivalent to the t-test for comparing the two means of y at those two positions of x. And the confidence interval of the slope is simply t

Hallowed

Rates of Consistency of Classical One-Side Tests, testing location of a long-tailed distribution and thus are highly non-robust from this point of view. On the other hand, the test based on the sample mean is equally good for every distribution with the exponential tails as for the normal distribution. The situation is similar with the t-test.

美麗的寫

Asymptotic Robustness of Bayesian Decision Rules in Statistical Decision Theory,sion rules are derived for Tukey’s model with contaminating distributions and for the model with additive distortions of observations. The guaranteed risk values for robust and Bayesian decision rules are found and compared by the method of asymptotic expansions. The results are illustrated for the

Maximizer

Power Simulation with the Same Random Sequences Under the Null Hypothesis and the Alternative,mpliest way, called crude Monte Carlo, consists of getting the fraction realized test statistics in the critical region at the alternative hypothesis (K). In that situation simulated power (π) at the null hypothesis (H) is an unbiased estimator of the size (α) of the test with a positive variance ba

Sinus-Node

Pseudoephedrine

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Robustness of Multiple Comparisons Against Variance Heterogeneity,nces and the sample size differ greatly. The adaptations for unequal variances of (4) and (5) seem better than (1) for cases with many samples. Test (2) is rather robust in itself if the variances are not too much different. Modifications exist that allow slight unequalities in the sample sizes.

PANG

Analogy of the Linear Regression and the Two-Sample Problem in Robustness Investigations,he interval for the difference of these two means divided by the distance between the two x-values. Thus robustness statements for the 2-sample problem may be transferred to regression..For the inverse problem of confidence estimation of some x-value corresponding to a given y-value similar considerations are possible.