APPLICATION OF SUPERSATURATED DESIGN TO STUDY THE ACTUAL CAUSES OF THE SPREAD OF SMOKING

2021 ◽  
Vol 68 (2) ◽  
pp. 135-149
Author(s):  
Alanazi Talal Abdulrahman
Keyword(s):  
Supersaturated Design

SUPERSATURATED DESIGN WITH MORE THAN TWO LEVELS

2001 ◽  
Vol 22 (02) ◽  
pp. 183-194 ◽  
Author(s):  
XUAN LU ◽  
YIZHOU SUN
Keyword(s):  
Supersaturated Design

On the identifiability of a supersaturated design

1998 ◽  
Vol 72 (1-2) ◽  
pp. 99-107 ◽  
Author(s):  
Jiahua Chen ◽  
Dennis K.J. Lin
Keyword(s):  
Supersaturated Design

Mixed-level supersaturated design application to a robustness study on an organic synthesis

2012 ◽  
Vol 113 ◽  
pp. 19-25 ◽  
Author(s):  
Alice Croguennoc ◽  
Marc Frédéric ◽  
Magalie Claeys-Bruno ◽  
Roger Phan-Tan-Luu ◽  
Michelle Sergent
Keyword(s):  
Organic Synthesis ◽  
Supersaturated Design

Construction of three-level supersaturated design

1999 ◽  
Vol 81 (1) ◽  
pp. 183-193 ◽  
Author(s):  
Shu Yamada ◽  
Yoshiko T. Ikebe ◽  
Hiroki Hashiguchi ◽  
Naoto Niki
Keyword(s):  
Supersaturated Design

scholarly journals Analysis Methods for Supersaturated Design: Some Comparisons

2021 ◽  
Vol 1 (3) ◽  
pp. 249-260
Author(s):  
Runze Li ◽  
Dennis K. J. Lin
Keyword(s):  
Supersaturated Design ◽  
Analysis Methods

Seleksi Peubah menggunakan Algoritme Genetika pada Data Rancangan Faktorial Pecahan Lewat Jenuh Dua Taraf

2020 ◽  
Vol 10 (1) ◽  
pp. 55-69
Author(s):  
Ani Safitri ◽  
Rahma Anisa ◽  
Bagus Sartono
Keyword(s):  
Genetic Algorithm ◽  
Variable Selection ◽  
Stepwise Regression ◽  
Computation Time ◽  
Penalized Regression ◽  
Fractional Factorial ◽  
Supersaturated Design ◽  
Forward Selection ◽  
Main Factors ◽  
Genetic Algorithm Model

In certain fields, experiments involve many factors and are constrained by costs. Reducing runs is one of the solutions to reduce experiment costs. But that can cause the number of runs to become less than the number of factors. This case of experimental design also is known as a supersaturated design. The important factors in this design are generally estimated by involving variable selection such as forward selection, stepwise regression, and penalized regression. Genetic algorithm is one of the methods that can be used for variable selection, especially for high dimensional data or supersaturated design. This study aims to use a genetic algorithm for variable selection in the supersaturated design and compare the genetic algorithm results with a stepwise regression which is generally used for a simple design. This study also involved fractional factorial design principles. The result showed that the main factors and interactions of the genetic algorithm and stepwise regression were quite different. But the principle was the same because the variables correlated. The genetic algorithm model had a smaller AIC and BIC and all of the main factors and interactions which had chosen were significant on the 0.1%. Therefore genetic algorithm model was chosen although computation time was much longer than stepwise regression.

Construction of Super Saturated Design Using Hadamard Matrix and Its E(S2) Optimality

2020 ◽  
Vol 11 (10) ◽  
pp. 829-834
Author(s):  
Salawu Saheed
Keyword(s):  
Factorial Design ◽  
Fractional Factorial Design ◽  
Hadamard Matrix ◽  
Fractional Factorial ◽  
Supersaturated Design ◽  
Saturated Design

Supersaturated design is essentially a fractional factorial design in which the number of potential effects is greater than the number of runs. In this paper, a super-saturated design is constructed using half fraction of Hadamard matrix of order N. A Hadamard matrix of order N, can investigate up to N 2 factors in N/2 runs. Result is shown in N = 16. The extension to larger N is adaptable.

Factor assignment based on linear-by-linear interaction for a three-level supersaturated design

2012 ◽  
Vol 7 (2/3) ◽  
pp. 245
Author(s):  
Hironobu Kawamura ◽  
Kohei Takada ◽  
Masayuki Furukawa ◽  
Tomomichi Suzuki
Keyword(s):  
Supersaturated Design ◽  
Linear Interaction
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