{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T06:43:09Z","timestamp":1704782589854},"reference-count":17,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2004,3,11]],"date-time":"2004-03-11T00:00:00Z","timestamp":1078963200000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2004,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A study into the geographical variability of timing of initial child breastfeeding after birth was carried out with the data set from the 1999 Nigeria Demographic and Health Survey. The effect of the metrical covariate of the mother's age at birth was assumed to be nonlinear and estimated nonparametrically. Other categorical covariates are estimated in the usual parametric form. Within a Bayesian context, appropriate priors are assigned for the geographical location, vector of the unknown (nonlinear) smooth functions and a further vector of the fixed effect parameters. For instance, a Markov random field prior is assumed on the spatial effects. Inferences are based on Markov chain Monte Carlo techniques while Bayesian model diagnostics are based on the deviance information criteria. Copyright \u00a9 2004 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.732","type":"journal-article","created":{"date-parts":[[2004,4,2]],"date-time":"2004-04-02T08:02:27Z","timestamp":1080892947000},"page":"267-281","source":"Crossref","is-referenced-by-count":11,"title":["Bayesian geoadditive modelling of breastfeeding initiation in Nigeria"],"prefix":"10.1002","volume":"19","author":[{"given":"Samson B.","family":"Adebayo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2004,3,11]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116466"},{"key":"e_1_2_7_3_1","unstructured":"BrezgerA KneibT LangS.2002.Bayes X\u2014Software for Bayesian inference based on Markov Chain Monte Carlo simulation techniques. 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