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By Werner Stahel, Sanford Weisberg

This IMA quantity in arithmetic and its purposes instructions IN strong facts AND DIAGNOSTICS is predicated at the complaints of the 1st 4 weeks of the six week IMA 1989 summer season application "Robustness, Diagnostics, Computing and pics in Statistics". a major aim of the organizers was once to attract a vast set of statisticians operating in robustness or diagnostics into collaboration at the demanding difficulties in those parts, quite at the interface among them. We thank the organizers of the robustness and diagnostics software Noel Cressie, Thomas P. Hettmansperger, Peter J. Huber, R. Douglas Martin, and particularly Werner Stahel and Sanford Weisberg who edited the court cases. A vner Friedman Willard Miller, Jr. PREFACE significant topics of all statistics are estimation, prediction, and making judgements lower than uncertainty. a typical method of those targets is thru parametric mod­ elling. Parametric types can provide an issue enough constitution to permit general, good understood paradigms to be utilized to make the necessary inferences. If, how­ ever, the parametric version isn't really thoroughly right, then the normal inferential equipment won't provide average solutions. within the final zone century, fairly with the arrival of on hand computing, extra recognition has been paid to the matter of inference whilst the parametric version used isn't thoroughly specified.

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Directions in Robust Statistics and Diagnostics: Part II

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D. I\IARTIN AND D. J. n,41 (1979),1'1'. i1. L. MOLINAR! AND G. lIJOt spectral analysis of the EEG, Neuropsychobiology,15 (1986),1'1'. 208-218. P. RAPPELSBERGER AND II. ral allillysis of the EEG by means of autoregressioll. Ill: G. DOLCf; AND II. 1,[iN"'·;!. i,pp. 7-,10. P. OJ'egression , and the approximate callollical factorizatioll of a sficctml dellsity wiltrix, Biometrika,50 (1963),pp. 129134. CONFIGURAL POLYSAMPLING STEPHAN MORGENTHALERt Abstract. Configural polysampling refers to the estimation and optimization of (small sample) mean-squared-errors in a conditional manner and under a variety of sampling distributions.

For many purposes it may be sufficient to fit univariate autoregressive models to the individual components of a d-dinwnsional process, rather than a fully multivariate model which has a much larger number of parameters. 4. Examples A simulated example In this example we simulate data from a bivariate autoregressive AR( 1) process XI consisting of two separate AR( 1) processes coupled via strongly positively correlated innovations. The process is contaminated by a few moderate, isolated, simultaneous artifacts from a similar process with negatively correlated innovations.

H is X(X'X)-I X'. In this form X and d;* are orthogonal and McKean (1975) has shown that this helps eliminate bias in the estimates. This is the model Cook and Wiesberg (1982) used in obtaining the least squares external-t diagnostic. Note that the first part of the model Xb* is a vector in the column space of X. uals from the fit of this reduced model are still eR. 2) A;*(8;) = L d;/a(R(e R,i - 8;d i /». j=1 This problem is just one of finding n simple regressions. hermore these regressions are easily obtained.

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