{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:13:47Z","timestamp":1760058827213,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aerospace Science and Technology Innovation Project of Hainan Province","award":["ATIC2023010001","11871294"],"award-info":[{"award-number":["ATIC2023010001","11871294"]}]},{"name":"National Natural Science Foundation of China","award":["ATIC2023010001","11871294"],"award-info":[{"award-number":["ATIC2023010001","11871294"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>A kriging regression model is a popular and effective type of surrogate model in computer experiments. A significant challenge arises when the mean function of the model includes polynomial terms with unknown coefficients, leading to identifiability problems and potentially unreliable results. To overcome this problem, Plumlee and Joseph (2018) introduced an orthogonal kriging model. Variable selection for kriging models has been widely considered by researchers in computer experiments. In this paper, we introduce a new method for combining orthogonal kriging with penalized variable selection. Furthermore, an efficient algorithm is given to select the correct mean function. The simulation results and an example study with real data show that the proposed method is superior to others in variable recognition rate and prediction accuracy.<\/jats:p>","DOI":"10.3390\/axioms14050339","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T09:39:47Z","timestamp":1745833187000},"page":"339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Penalized Orthogonal Kriging Method for Selecting a Global Trend"],"prefix":"10.3390","volume":"14","author":[{"given":"Xituo","family":"Zhang","sequence":"first","affiliation":[{"name":"Naval Submarine Academy, Qingdao 266199, China"}]},{"given":"Guoxing","family":"Gao","sequence":"additional","affiliation":[{"name":"Naval Submarine Academy, Qingdao 266199, China"}]},{"given":"Jianxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Naval Submarine Academy, Qingdao 266199, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2573-5236","authenticated-orcid":false,"given":"Xinmin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qingdao University, Qingdao 266071, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/00401706.1989.10488474","article-title":"Designs for computer experiments","volume":"31","author":"Sacks","year":"1989","journal-title":"Technometrics"},{"key":"ref_2","first-page":"409","article-title":"Design and analysis of computer experiments","volume":"4","author":"Sacks","year":"1989","journal-title":"Stat. 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