{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T10:07:24Z","timestamp":1783591644717,"version":"3.55.0"},"reference-count":59,"publisher":"Informa UK Limited","issue":"543","funder":[{"DOI":"10.13039\/100007718","name":"CDAR, Department of Economics, the University of California, Berkeley","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CIF-2102227"],"award-info":[{"award-number":["CIF-2102227"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH","award":["R01AG061303"],"award-info":[{"award-number":["R01AG061303"]}]},{"name":"NIH","award":["R01AG062542"],"award-info":[{"award-number":["R01AG062542"]}]},{"name":"NIH","award":["R01AG034570"],"award-info":[{"award-number":["R01AG034570"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American Statistical Association"],"published-print":{"date-parts":[[2023,7,3]]},"DOI":"10.1080\/01621459.2022.2039671","type":"journal-article","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T18:33:22Z","timestamp":1647282802000},"page":"2158-2170","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":14,"title":["Kernel Knockoffs Selection for Nonparametric Additive Models"],"prefix":"10.1080","volume":"118","author":[{"given":"Xiaowu","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Economics, University of California, Berkeley, CA"},{"name":"Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology, University of California, Berkeley, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lexin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology, University of California, Berkeley, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1663"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.12068"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9947-1950-0051437-7"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390161"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.02.059"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1337"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1755"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOS1852"},{"key":"e_1_3_3_10_1","volume-title":"Fourier Kernel Learning","author":"B\u0103z\u0103van E. G.","year":"2012","unstructured":"B\u0103z\u0103van, E. G., Li, F., and Sminchisescu, C. (2012), Fourier Kernel Learning, New York: Springer."},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9904-1934-05843-9"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-economics-063016-104355"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12265"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0197-4580(99)00107-4"},{"key":"e_1_3_3_16_1","volume-title":"in International Conference on Machine Learning. PMLR","author":"Dai R.","year":"2016","unstructured":"Dai, R., and Barber, R. (2016), \u201cThe Knockoff Filter for FDR Control in Group-Sparse and Multitask Regression,\u201d in International Conference on Machine Learning. PMLR."},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2021.1882466"},{"key":"e_1_3_3_18_1","first-page":"961","article-title":"\u201cThe Preclinical Alzheimer Cognitive Composite: Measuring Amyloid-Related Decline,\u201d","volume":"71","author":"Donohue M. C.","year":"2014","unstructured":"Donohue, M. C., Sperling, R. A., Salmon, D. P., Rentz, D. M., Raman, R., Thomas, R. G., Weiner, M., Aisen, P. S. Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer\u2019s Disease Neuroimaging Initiative; and Alzheimer\u2019s Disease Cooperative Study (2014), \u201cThe Preclinical Alzheimer Cognitive Composite: Measuring Amyloid-Related Decline,\u201d Journal of the American Medical Association: Neurology, 71, 961\u2013970.","journal-title":"Journal of the American Medical Association: Neurology"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awm016"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-IMSCOLL907"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382129"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1546589"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.04.087"},{"key":"e_1_3_3_24_1","volume-title":"Generalized Additive Models","author":"Hastie T. J.","year":"1990","unstructured":"Hastie, T. J., and Tibshirani, R. J. (1990), Generalized Additive Models (Vol. 43), London: Chapman and Hall."},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv517"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS781"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.06.042"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS825"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00502.x"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOAS589"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000000722"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOS1018"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS692"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2010.00740.x"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1909.0016"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420035933"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2016.01.002"},{"key":"e_1_3_3_38_1","first-page":"1177","article-title":"\u201cRandom Features for Large-Scale Kernel Machines,\u201d","volume":"20","author":"Rahimi A.","year":"2007","unstructured":"Rahimi, A., and Recht, B. (2007), \u201cRandom Features for Large-Scale Kernel Machines,\u201d Advances in Neural Information Processing Systems, 20, 1177\u20131184.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_39_1","first-page":"389","article-title":"\u201cMinimax-Optimal Rates for Sparse Additive Models Over Kernel Classes via Convex Programming","volume":"13","author":"Raskutti G.","year":"2012","unstructured":"Raskutti, G., Wainwright, M. J., and Yu, B. (2012), \u201cMinimax-Optimal Rates for Sparse Additive Models Over Kernel Classes via Convex Programming,\u201d Journal of Machine Learning Research, 13, 389\u2013427.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2165799"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2009.00718.x"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS691"},{"key":"e_1_3_3_43_1","article-title":"\u201cDerandomizing Knockoffs,\u201d","author":"Ren Z.","year":"2020","unstructured":"Ren, Z., Wei, Y., and Cand\u00e8s, E. (2020), \u201cDerandomizing Knockoffs,\u201d arXiv preprint arXiv:2012.02717.","journal-title":"arXiv preprint arXiv:2012.02717"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1660174"},{"key":"e_1_3_3_45_1","volume-title":"NIPS","author":"Rudi A.","year":"2017","unstructured":"Rudi, A., and Rosasco, L. (2017), \u201cGeneralization Properties of Learning with Random Features,\u201d in NIPS."},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176349548"},{"key":"e_1_3_3_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.005592.x"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1521"},{"key":"e_1_3_3_49_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000545"},{"key":"e_1_3_3_50_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00694.x"},{"key":"e_1_3_3_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0099-4_4"},{"key":"e_1_3_3_52_1","doi-asserted-by":"publisher","DOI":"10.1002\/hipo.450010102"},{"key":"e_1_3_3_53_1","first-page":"491","article-title":"\u201cMedial Temporal Lobe Atrophy Predicts Alzheimer\u2019s Disease in Patients with Minor Cognitive Impairment","volume":"72","author":"Visser P. J.","year":"2002","unstructured":"Visser, P. J., Verhey, F. R. J., Hofman, P. A. M., Scheltens, P., and Jolles, J. (2002), \u201cMedial Temporal Lobe Atrophy Predicts Alzheimer\u2019s Disease in Patients with Minor Cognitive Impairment,\u201d Journal of Neurology, Neurosurgery & Psychiatry, 72, 491\u2013497.","journal-title":"Journal of Neurology, Neurosurgery & Psychiatry"},{"key":"e_1_3_3_54_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970128"},{"key":"e_1_3_3_55_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1034713638"},{"key":"e_1_3_3_56_1","article-title":"\u201cA Power Analysis for Knockoffs with the Lasso Coefficient-Difference Statistic,\u201d","author":"Weinstein A.","year":"2020","unstructured":"Weinstein, A., Su, W. J., Bogdan, M., Barber, R. F., and Cand\u00e8s, E. J. (2020), \u201cA Power Analysis for Knockoffs with the Lasso Coefficient-Difference Statistic,\u201d arXiv preprint arXiv:2007.15346.","journal-title":"arXiv preprint arXiv:2007.15346"},{"key":"e_1_3_3_57_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315370279"},{"key":"e_1_3_3_58_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000843"},{"key":"e_1_3_3_59_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1422"},{"key":"e_1_3_3_60_1","first-page":"2541","article-title":"\u201cOn Model Selection Consistency of Lasso","volume":"7","author":"Zhao P.","year":"2006","unstructured":"Zhao, P., and Yu, B. (2006), \u201cOn Model Selection Consistency of Lasso,\u201d Journal of Machine Learning Research, 7, 2541\u20132563.","journal-title":"Journal of Machine Learning Research"}],"container-title":["Journal of the American Statistical Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/01621459.2022.2039671","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:19:38Z","timestamp":1697156378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/01621459.2022.2039671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,14]]},"references-count":59,"journal-issue":{"issue":"543","published-print":{"date-parts":[[2023,7,3]]}},"alternative-id":["10.1080\/01621459.2022.2039671"],"URL":"https:\/\/doi.org\/10.1080\/01621459.2022.2039671","relation":{},"ISSN":["0162-1459","1537-274X"],"issn-type":[{"value":"0162-1459","type":"print"},{"value":"1537-274X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,14]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uasa20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uasa20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2021-06-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-03-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}