{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:22:53Z","timestamp":1787505773382,"version":"build-2736575974"},"reference-count":78,"publisher":"Wiley","issue":"18-19","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>We develop a new method for simultaneously selecting fixed and random effects in a multilevel functional regression model. The proposed method is motivated by accelerometer\u2010derived physical activity data from the 2011 to 2012 cohort of the National Health and Nutrition Examination Survey (NHANES), with the aim of identifying age and race\u2010specific heterogeneity in covariate effects on the diurnal pattern of physical activity across the lifespan. Existing methods for variable selection in function\u2010on\u2010scalar regression have primarily been designed for fixed effect selection and for single\u2010level functional data. In high\u2010dimensional multilevel functional regression, the presence of cluster\u2010specific heterogeneity in covariate effects could be detected through sparsity in fixed and random effects, and for this purpose, we propose a multilevel functional mixed effects selection (MuFuMES) method. The fixed and random functional effects are modelled using splines, with spike\u2010and\u2010slab group lasso (SSGL) priors on the unknown parameters of interest, and a computationally efficient MAP estimation approach is employed for mixed effect selection through an Expectation Conditional Maximization (ECM) algorithm. Numerical analysis using simulation study illustrates the satisfactory selection accuracy of the variable selection method in having a negligible false\u2010positive and false\u2010negative rate. The proposed method is applied to the NHANES 2011\u201012 accelerometer data, where it effectively identifies age and race\u2010specific heterogeneity in covariate effects on the diurnal pattern of physical activity, recovering biologically meaningful insights.<\/jats:p>","DOI":"10.1002\/sim.70694","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T03:13:48Z","timestamp":1786331628000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0939-4731","authenticated-orcid":false,"given":"Rahul","family":"Ghosal","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics University of South Carolina  Columbia 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