{"title":"Optimal s-level fractional factorial designs under baseline parameterization","authors":"Zhaohui Yan, Shengli Zhao","doi":"10.1016/j.jspi.2024.106242","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, we explore the minimum aberration criterion for <span><math><mi>s</mi></math></span>-level designs under baseline parameterization, called BP-MA. We give a complete search method and an incomplete search method to obtain the BP-MA (or nearly BP-MA) designs. The methodology has no restriction on <span><math><mi>s</mi></math></span>, the levels of the factors. The catalogues of (nearly) BP-MA designs with <span><math><mrow><mi>s</mi><mo>=</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>,</mo><mn>4</mn><mo>,</mo><mn>5</mn></mrow></math></span> levels are provided.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"236 ","pages":"Article 106242"},"PeriodicalIF":0.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Planning and Inference","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378375824000995","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we explore the minimum aberration criterion for -level designs under baseline parameterization, called BP-MA. We give a complete search method and an incomplete search method to obtain the BP-MA (or nearly BP-MA) designs. The methodology has no restriction on , the levels of the factors. The catalogues of (nearly) BP-MA designs with levels are provided.
期刊介绍：
The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.
We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
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