Total Nonnegativity of Moment Matrices and its Application to E-Optimal Designs in Polynomial Regression

1996 ◽  
pp. 359-383
Author(s):  
Berthold Heiligers
Keyword(s):  
Polynomial Regression ◽  
Optimal Designs ◽  
Total Nonnegativity

Q-optimal experimental designs and close to them experimental designs for polynomial regression on the interval

2020 ◽  
Vol 86 (5) ◽  
pp. 65-72
Author(s):  
Yu. D. Grigoriev
Keyword(s):  
Optimal Design ◽  
Polynomial Regression ◽  
Direct Consequence ◽  
Experimental Designs ◽  
Optimal Designs ◽  
Software Systems ◽  
General Character ◽  
Support Points ◽  
Optimal Weights ◽  
Universal Expression

The problem of constructing Q-optimal experimental designs for polynomial regression on the interval [–1, 1] is considered. It is shown that well-known Malyutov – Fedorov designs using D-optimal designs (so-called Legendre spectrum) are other than Q-optimal designs. This statement is a direct consequence of Shabados remark which disproved the Erdős hypothesis that the spectrum (support points) of saturated D-optimal designs for polynomial regression on a segment appeared to be support points of saturated Q-optimal designs. We present a saturated exact Q-optimal design for polynomial regression with s = 3 which proves the Shabados notion and then extend this statement to approximate designs. It is shown that when s = 3, 4 the Malyutov – Fedorov theorem on approximate Q-optimal design is also incorrect, though it still stands for s = 1, 2. The Malyutov – Fedorov designs with Legendre spectrum are considered from the standpoint of their proximity to Q-optimal designs. Case studies revealed that they are close enough for small degrees s of polynomial regression. A universal expression for Q-optimal distribution of the weights pi for support points xi for an arbitrary spectrum is derived. The expression is used to tabulate the distribution of weights for Malyutov – Fedorov designs at s = 3, ..., 6. The general character of the obtained expression is noted for Q-optimal weights with A-optimal weight distribution (Pukelsheim distribution) for the same problem statement. In conclusion a brief recommendation on the numerical construction of Q-optimal designs is given. It is noted that in this case in addition to conventional numerical methods some software systems of symbolic computations using methods of resultants and elimination theory can be successfully applied. The examples of Q-optimal designs considered in the paper are constructed using precisely these methods.

scholarly journals A note on optimal designs for estimating the slope of a polynomial regression

2021 ◽  
Vol 170 ◽  
pp. 108992
Author(s):  
Holger Dette ◽  
Viatcheslav B. Melas ◽  
Petr Shpilev
Keyword(s):  
Polynomial Regression ◽  
Optimal Designs

scholarly journals Optimal designs for estimating individual coefficients in polynomial regression with no intercept

2020 ◽  
Vol 158 ◽  
pp. 108636
Author(s):  
Holger Dette ◽  
Viatcheslav B. Melas ◽  
Petr Shpilev
Keyword(s):  
Polynomial Regression ◽  
Optimal Designs

scholarly journals Optimal Designs for Large Degree Polynomial Regression

1976 ◽  
Vol 4 (6) ◽  
pp. 1113-1123 ◽  
Author(s):  
J. Kiefer ◽  
W. J. Studden
Keyword(s):  
Polynomial Regression ◽  
Large Degree ◽  
Optimal Designs ◽  
Degree Polynomial
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