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Some Applications of Canonical Moments in Fourier Regression Models
Lecture Notes-Monograph Series
Vol. 34, New Developments and Applications in Experimental Design (1998), pp. 175-185
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/4356072
Page Count: 11
You can always find the topics here!Topics: Product design, Regression analysis, Polynomials, Additivity, Statism, Cubes, Mathematical moments, Design efficiency, Fourier series, Sine function
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This paper applies recent results on canonical moments for the determination of optimal designs for multivariate Fourier regression models. Optimal designs for discriminating between different Fourier regression models can be found explicitly. It is also demonstrated that these designs may be useful in orthogonal series estimation and for testing additivity in nonparametric regression. In contrast to many other optimality criteria for the trigonometric regression model, the discrimination designs are not necessarily uniformly distributed on equidistant points.
Lecture Notes-Monograph Series © 1998 Institute of Mathematical Statistics