Weighted likelihood bootstrap in CML.

Monte Carlo evidence suggests that weighted likelihood bootstrap produces confidence limits with the correct size.

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A description of CML, and discussion of statistical inference in models with constrained parameters.

The paper presents a Monte Carlo study of the effects of constrained nuisance parameters on the size of confidence regions for parameters of interest, both constrained and unconstrained.

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Describes the new CO applications module written in GAUSS.

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Describes the application of the Markowitz portfolio optimization method to the optimal distribution of resources in a portfolio of stocks and securities. It uses a specially designed procedure written in GAUSS.

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rons@u.washington.edu (206) 727-6521