{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:32:13Z","timestamp":1787841133263,"version":"build-2784847793"},"reference-count":12,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9]]},"abstract":"\n Background:<\/jats:title>\n In this article, we attempt to demonstrate the superiority of the Bayesian approach over the frequentist approaches of the multiple linear regression model in identifying the influencing factors for the response variable.<\/jats:p>\n <\/jats:sec>\n \n Methods and Material:<\/jats:title>\n A survey was conducted among the 310 respondents from the Kathirkamam area in Puducherry. We have considered the response variable, body mass index (BMI), and the predictors such as age, weight, gender, nature of the job, and marital status of individuals were collected with the personal interview method. Jeffreys\u2019s Amazing Statistics Program (JASP) software was used to analyze the dataset. In the conventional multiple linear regression model, the single value of regression coefficients is determined, while in the Bayesian linear regression model, the regression coefficient of each predictor follows a specific posterior distribution. Furthermore, it would be most useful to identify the best models from the list of possible models with posterior probability values. An inclusion probability for all the predictors will give a superior idea of whether the predictors are included in the model with probability.<\/jats:p>\n <\/jats:sec>\n \n Results and Conclusions:<\/jats:title>\n The Bayesian framework offers a wide range of results for the regression coefficients instead of the single value of regression coefficients in the frequentist test. Such advantages of the Bayesian approach will catapult the quality of investigation outputs by giving more reliability to solutions of scientific problems.<\/jats:p>\n <\/jats:sec>","DOI":"10.4103\/ijcm.ijcm_119_22","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T03:10:40Z","timestamp":1694142640000},"page":"659-665","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study"],"prefix":"10.4103","volume":"48","author":[{"given":"R.","family":"Vijayaragunathan","sequence":"first","affiliation":[{"name":"Department of Statistics, Indira Gandhi College of Arts and Science, Puducherry, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kishore K.","family":"John","sequence":"additional","affiliation":[{"name":"Department of Foreign Trade, Indira Gandhi College of Arts and Science, Puducherry, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. R.","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Adjunct Professor, Chennai Mathematical Institute, Siruseri, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"R1-20260330","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1198\/016214507000001337","article-title":"Mixtures of g priors for Bayesian variable selection","volume":"103","author":"Liang","year":"2008","journal-title":"J Am Stat Assoc"},{"key":"R2-20260330","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1080\/00273171.2012.734737","article-title":"Default Bayes factors for model selection in regression","volume":"47","author":"Rouder","year":"2012","journal-title":"Multivariate Behav Res"},{"key":"R3-20260330","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1111\/biom.13309","article-title":"Quantile regression for survival data with covariates subject to detection limits","volume":"77","author":"Yu","year":"2021","journal-title":"Biometrics"},{"key":"R4-20260330","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13104-020-05291-z","article-title":"Simulation data for the analysis of Bayesian posterior significance and effect size indices for the two-sample t-test to support reproducible medical research","volume":"13","author":"Kelter","year":"2020","journal-title":"BMC Res Notes"},{"key":"R5-20260330","doi-asserted-by":"crossref","first-page":"540","DOI":"10.2991\/jsta.d.201230.001","article-title":"Bayes factors for comparison of two-way ANOVA models","volume":"19","author":"Vijayaragunathan","year":"2020","journal-title":"J Stat Theory Appl"},{"key":"R6-20260330","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1177\/1740774518755122","article-title":"A Bayesian basket trial design using a calibrated Bayesian hierarchical model","volume":"15","author":"Chu","year":"2018","journal-title":"Clin Trials"},{"key":"R7-20260330","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1038\/s41586-019-1171-x","article-title":"Rising rural body-mass index is the main driver of the global obesity epidemic in adults","volume":"569","year":"2019","journal-title":"Nature"},{"key":"R8-20260330","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1002\/oby.23048","article-title":"BMI, weight discrimination, and the trajectory of distress and well-being across the coronavirus pandemic","volume":"29","author":"Sutin","year":"2021","journal-title":"Obesity (Silver Spring)"},{"key":"R9-20260330","first-page":"37","article-title":"Reliability and validity of the Iranian version of the human immunodeficiency virus specific World Health Organization quality of life BREF questionnaire","volume":"1","author":"Nikooseresht","year":"2015","journal-title":"J Biostat Epidemiol"},{"key":"R10-20260330","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ejor.2013.10.046","article-title":"Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk","volume":"234","author":"Rockafellar","year":"2014","journal-title":"Eur J Oper Res"},{"key":"R11-20260330","doi-asserted-by":"crossref","first-page":"160","DOI":"10.4103\/0971-5916.180203","article-title":"Epidemiology of childhood overweight and obesity in India:A systematic review","volume":"143","author":"Ranjani","year":"2016","journal-title":"Indian J Med Res"},{"key":"R12-20260330","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1080\/02664763.2018.1450363","article-title":"Bayesian CV@R\/super-quantile regression","volume":"45","author":"Tsionas","year":"2018","journal-title":"J Appl Stat"}],"container-title":["Indian Journal of Community Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/ijcm.ijcm_119_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:43:27Z","timestamp":1787838207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/ijcm.ijcm_119_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":12,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.4103\/ijcm.ijcm_119_22","relation":{},"ISSN":["0970-0218","1998-3581"],"issn-type":[{"value":"0970-0218","type":"print"},{"value":"1998-3581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"2023-02-03","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-04","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}