Two-level Isomorphic Foldovers Designs

Two-level orthogonal arrays ensure the independent estimations of main effects when linear models are considered, and thus are popularly used experimental designs.Such arrays can be classified into regular and nonregular designs (Wu and Hamada, 2021).Regular designs entertain specific algebraic structures and thus have been well studied in the literature.Their run sizes, however, are limited to powers of 2. Nonregular designs have a more complicated structure, but they are more flexible in the run sizes and allow the estimation of more effects.The construction of nonregular designs remains a challenge.This paper introduces a new class of nonregular designs called isomorphic foldovers design (IFD).Specifically, it is composed of several foldovers of an initial design.The goal of our study is to investigate the general theory of IFDs.We propose a method for obtaining all nonequivalent IFDs with f foldovers for any initial design.Two algorithms are provided to construct optimal f -IFD in terms of G-aberration (or G2-aberration) criterion.The IFD structure provides an efficient way to find good designs in the sense that constructing good IFDs based on a nonregular initial design is often more successful than doing so with a more granular single flat.Meanwhile, the IFDs have a parallel flats structure and thus are much easier to understand and analyze than many other nonregular designs.Moreover, we show that some existing designs can be viewed as special cases of IFDs.

Authors

Publication Details

Journal
Statistica Sinica
Published
2024-08-23
DOI
https://doi.org/10.5705/ss.202023.0401
Primary Topic
Design Education and Practice
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Two-level Isomorphic Foldovers Designs

Chunyan Wang, Dennis K. J. Lin
Statistica Sinica
Design Education and Practice
article

Two-level Isomorphic Foldovers Designs

Chunyan Wang, Dennis K. J. Lin
article en

Abstract

Two-level orthogonal arrays ensure the independent estimations of main effects when linear models are considered, and thus are popularly used experimental designs.Such arrays can be classified into regular and nonregular designs (Wu and Hamada, 2021).Regular designs entertain specific algebraic structures and thus have been well studied in the literature.Their run sizes, however, are limited to powers of 2. Nonregular designs have a more complicated structure, but they are more flexible in the run sizes and allow the estimation of more effects.The construction of nonregular designs remains a challenge.This paper introduces a new class of nonregular designs called isomorphic foldovers design (IFD).Specifically, it is composed of several foldovers of an initial design.The goal of our study is to investigate the general theory of IFDs.We propose a method for obtaining all nonequivalent IFDs with f foldovers for any initial design.Two algorithms are provided to construct optimal f -IFD in terms of G-aberration (or G2-aberration) criterion.The IFD structure provides an efficient way to find good designs in the sense that constructing good IFDs based on a nonregular initial design is often more successful than doing so with a more granular single flat.Meanwhile, the IFDs have a parallel flats structure and thus are much easier to understand and analyze than many other nonregular designs.Moreover, we show that some existing designs can be viewed as special cases of IFDs.

Statistica Sinica
National Natural Science Foundation of China, Ministry of Education, India
Openalex Percentile: Top 87%
Design Education and Practice
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.