This paper considers the optimal design problem for multiresponse regression models. The <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$R$$</EquationSource> </InlineEquation>-optimality introduced by Dette (J R Stat Soc B 59:97–110, <CitationRef CitationID="CR5">1997</CitationRef>) for single response experiments is extended to the case of multiresponse parameter estimation. A general equivalence theorem for the <InlineEquation ID="IEq5">...</inlineequation></citationref></equationsource></inlineequation>

This paper considers R-optimal approximate designs for random coefficient regression models. An equivalence theorem for the R-optimality is provided for random coefficient regression models. Explicit expressions for R-optimal designs under linear regression models are presented for illustration.

Consider a linear regression model with both quantitative and qualitative factors and an k-dimensional response variable y whose components are equicorrelated for each observation. The D-optimal design problem is investigated when the levels of the qualitative factor interact with the...

This paper deals with the design problem for recovering a response surface by using a nonparametric Bayesian approach. The criterion for selecting the designs is based on the asymptotic average estimation variance, and three priors for the response are specified. We found the optimal design that...

This article deals with the design problem for recovering a response surface with correlated observations by a Bayesian approach. The performance of several types of quasirandom point sets and random point sets are compared for the designs.

The multiresponse model E(y[alpha]x)=[summation operator]l=1p[alpha] [theta][alpha]lf[alpha]l(x)+h[alpha](x), [alpha]=1,...,r, is considered, where h[alpha](x) is an unknown bias or contamination function from some class with a probability measure. Optimal designs are studied in terms of...

The randomization of a (t,m,s)-net or a (t,s)-sequence proposed by Owen (1995, Monte Carlo and quasi-Monte Carlo Methods in Scientific Computing (Berlin), Lecture Notes in Statistics, vol. 106, pp. 299-317) combines the strengths of equidistribution and Monte Carlo integration. This paper shows...