# Partial aliased effect number pattern and selection of optimal compromise designs

Authors
• 1 Southwest Forestry University, Faculty of Mathematics and Physics, Kunming, China , Kunming (China)
• 2 Jilin University of Finance and Economics, School of Statistics, Changchun, China , Changchun (China)
• 3 Nankai University, LPMC and School of Mathematical Sciences, Tianjin, China , Tianjin (China)
• 4 University of British Columbia, Department of Statistics, Vancouver, BC, V6T 1Z4, Canada , Vancouver (Canada)
• 5 Northeast Normal University, CLAS and School of Mathematics and Statistics, Changchun, China , Changchun (China)
Type
Published Article
Journal
Metrika
Publisher
Springer Berlin Heidelberg
Publication Date
Feb 22, 2019
Volume
82
Issue
3
Pages
269–293
Identifiers
DOI: 10.1007/s00184-018-00705-2
Source
Springer Nature
Keywords
Often, experimenters are only interested in estimating a few factor specified effects. In this paper, we broadly call a design which can reach this target a compromise design. First, for assessing and selecting this kind of designs we introduce a partial aliased effect number pattern (P-AENP), then we use this pattern to study class one two-level compromise designs. Some theoretical results are obtained and a number of class one clear, strongly clear and general optimal 2n-m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2^{n-m}$$\end{document} compromise designs with 8, 16, 32 and 64 runs are tabulated.