{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T01:10:10Z","timestamp":1744333810157,"version":"3.40.4"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["P2C-HD041022"],"award-info":[{"award-number":["P2C-HD041022"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either observed or estimated, to effectively estimate the function at near-parametric convergence rates. This novel approach and computational algorithm could lead to methods useful to practitioners in many areas of science and engineering. Our theoretical results reveal behaviour universal to this class of nonparametric estimation problems. We explore a general setting involving tensor product spaces and build upon the smoothing spline analysis of variance framework. For d-dimensional models under full interaction, the optimal rates with gradient information on p covariates are identical to those for the (d\u2212p)-interaction models without gradients and, therefore, the models are immune to the curse of interaction. For additive models, the optimal rates using gradient information are n, thus achieving the parametric rate. We demonstrate aspects of the theoretical results through synthetic and real data applications.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkae093","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T14:23:14Z","timestamp":1724682194000},"page":"319-336","source":"Crossref","is-referenced-by-count":0,"title":["Nonparametric estimation via partial derivatives"],"prefix":"10.1093","volume":"87","author":[{"given":"Xiaowu","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, and Biostatistics, University of California , Los Angeles, CA 90095 ,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"issue":"2","key":"2025041100295564000_qkae093-B1","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1287\/opre.1090.0754","article-title":"Stochastic kriging for simulation 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