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A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

dc.contributor.authorNeelon, Brianen_US
dc.contributor.authorGelfand, Alan E.en_US
dc.contributor.authorMiranda, Marie Lynnen_US
dc.date.accessioned2014-10-07T16:09:50Z
dc.date.availableWITHHELD_14_MONTHSen_US
dc.date.available2014-10-07T16:09:50Z
dc.date.issued2014-11en_US
dc.identifier.citationNeelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn (2014). "A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores." Journal of the Royal Statistical Society: Series C (Applied Statistics) 63(5): 737-761.en_US
dc.identifier.issn0035-9254en_US
dc.identifier.issn1467-9876en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/108684
dc.publisherChapman and Hall–CRCen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherBayesian Analysisen_US
dc.subject.otherConditional Auto‐Regressive Prioren_US
dc.subject.otherEducation Dataen_US
dc.subject.otherFinite Mixture Modelen_US
dc.subject.otherMultivariate Spatial Analysisen_US
dc.subject.otherAreal Dataen_US
dc.titleA multivariate spatial mixture model for areal data: examining regional differences in standardized test scoresen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/108684/1/rssc12061.pdf
dc.identifier.doi10.1111/rssc.12061en_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series C (Applied Statistics)en_US
dc.identifier.citedreferenceNorth Carolina Department of Public Instruction ( 2007 ) Achievement level ranges for the North Carolina end‐of‐grade tests: mathematics at grades 3–8. North Carolina Department of Public Instruction, Raleigh. (Available from http://www.ncpublicschools.org/docs/accountability/testing/eog/rangeseogmath.pdf.)en_US
dc.identifier.citedreferenceJi, C., Merl, D., Kepler, T. B. and West, M. ( 2009 ) Spatial mixture modelling for unobserved point processes: examples in immunofluorescence histology. Baysn Anal., 4, 297 – 316.en_US
dc.identifier.citedreferenceJin, X., Carlin, B. P. and Banerjee, S. ( 2005 ) Generalized hierarchical multivariate car models for areal data. Biometrics, 61, 950 – 961.en_US
dc.identifier.citedreferenceKottas, A., Duan, J. A. and Gelfand, A. E. ( 2008 ) Modeling disease incidence data with spatial and spatio‐temporal Dirichlet process mixtures. Biometr. J., 50, 29 – 42.en_US
dc.identifier.citedreferenceKottas, A. and Sansó, B. ( 2007 ) Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis. J. Statist. Planng Inf., 137, 3151 – 3163.en_US
dc.identifier.citedreferenceLawson, A. B. and Clark, A. ( 2002 ) Spatial mixture relative risk models applied to disease mapping. Statist. Med., 21, 359 – 370.en_US
dc.identifier.citedreferenceMardia, K. ( 1988 ) Multi‐dimensional multivariate Gaussian Markov random fields with application to image processing. J. Multiv. Anal., 24, 265 – 284.en_US
dc.identifier.citedreferenceMcLachlan, G. and Peel, D. ( 2000 ) Finite Mixture Models. New York: Wiley.en_US
dc.identifier.citedreferenceNathoo, F. S. and Ghosh, P. ( 2013 ) Skew‐elliptical spatial random effect modeling for areal data with application to mapping health utilization rates. Statist. Med., 32, 290 – 306.en_US
dc.identifier.citedreferenceNational Center for Education Statistics ( 2013 ) The nation's report card: trends in academic progress 2012. Report 2013‐456. Institute of Education Sciences, US Department of Education, Washington DC. (Available from http://nces.ed.gov/nationsreportcard/pubs/main2012/2013456.aspx.)en_US
dc.identifier.citedreferenceNorth Carolina Department of Public Instruction ( 2006 ) The North Carolina testing program 2006–2007. North Carolina Department of Public Instruction, Raleigh. (Available from http://www.ncpublicschools.org/docs/accountability/NORTHCgeneralpolicies.pdf.)en_US
dc.identifier.citedreferenceNorth Carolina Department of Public Instruction ( 2008 ) Achievement level ranges for the North Carolina end‐of‐grade tests: reading comprehension at grades 3–8. North Carolina Department of Public Instruction, Raleigh. (Available from http://www.ncpublicschools.org/docs/accountability/testing/achievelevels/alrangesreading.pdf.)en_US
dc.identifier.citedreferencePope, D. G. and Sydnor, J. R. ( 2010 ) Geographic variation in the gender differences in test scores. J. Econ. Perspect., 24, 95 – 108.en_US
dc.identifier.citedreferenceR Development Core Team ( 2011 ) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.en_US
dc.identifier.citedreferenceReich, B. J. and Fuentes, M. ( 2007 ) A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields. Ann. Appl. Statist., 1, 249 – 264.en_US
dc.identifier.citedreferenceRichardson, S. ( 2002 ) Discussion on ‘Bayesian measures of model complexity and fit’ (by D. J. Spiegelhalter, N. G. Best, B. P. Carlin and A. van der Linde). J. R. Statist. Soc. B, 64, 626 – 627.en_US
dc.identifier.citedreferenceSethuraman, J. ( 1994 ) A constructive definition of Dirichlet priors. Statist. Sin., 4, 639 – 650.en_US
dc.identifier.citedreferenceSpiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. ( 2002 ) Bayesian measures of model complexity and fit (with discussion). J. R. Statist. Soc. B, 64, 583 – 639.en_US
dc.identifier.citedreferenceStephens, M. ( 2000 ) Dealing with label switching in mixture models. J. R. Statist. Soc. B, 62, 795 – 809.en_US
dc.identifier.citedreferenceUS Census Bureau ( 2010 ) American Community Survey 2005–2009. US Census Bureau, Washington DC. (Available from http://www.census.gov/acs/www/.)en_US
dc.identifier.citedreferenceWall, M. M. and Liu, X. ( 2009 ) Spatial latent class analysis model for spatially distributed multivariate binary data. Computnl Statist. Data Anal., 53, 3057 – 3069.en_US
dc.identifier.citedreferenceZareifard, H. and Khaledi, M. J. ( 2013 ) Non‐Gaussian modeling of spatial data using scale mixing of a unified skew Gaussian process. J. Multiv. Anal., 114, 16 – 28.en_US
dc.identifier.citedreferenceZhang, Y., Hodges, J. S. and Banerjee, S. ( 2009 ) Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Ann. Appl. Statist., 3, 1805 – 1830.en_US
dc.identifier.citedreferenceBanerjee, S., Carlin, B. P. and Gelfand, A. E. ( 2004 ) Hierarchical Modeling and Analysis for Spatial Data. Boca Raton: Chapman and Hall–CRC.en_US
dc.identifier.citedreferenceBesag, J. ( 1974 ) Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Statist. Soc. B, 36, 192 – 236.en_US
dc.identifier.citedreferenceBesag, J., York, J. and Mollié, A. ( 1991 ) Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Statist. Math., 43, 1 – 20.en_US
dc.identifier.citedreferenceCarlin, B. P. and Banerjee, S. ( 2002 ) Hierarchical multivariate CAR models for spatio‐temporally correlated survival data (with discussion). Baysn Statist., 7, 45 – 63.en_US
dc.identifier.citedreferenceCeleux, G., Forbes, F., Robert, C. P. and Titterington, D. M. ( 2006 ) Deviance information criteria for missing data models. Baysn Anal., 1, 651 – 674.en_US
dc.identifier.citedreferenceCongdon, P. ( 2010 ) Random‐effects models for migration attractivity and retentivity: a Bayesian methodology. J. R. Statist. Soc. A, 173, 755 – 774.en_US
dc.identifier.citedreferenceFerguson, R. ( 2008 ) Toward Excellence with Equity: an Emerging Vision for Closing the Achievement Gap. Cambridge: Harvard Education.en_US
dc.identifier.citedreferenceFrühwirth‐Schnatter, S. ( 2006 ) Finite Mixture and Markov Switching Models. Berlin: Springer.en_US
dc.identifier.citedreferenceGelfand, A. E., Kottas, A. and MacEachern, S. N. ( 2005 ) Bayesian nonparametric spatial modeling with Dirichlet process mixing. J. Am. Statist. Ass., 100, 1021 – 1035.en_US
dc.identifier.citedreferenceGelfand, A. E. and Vounatsou, P. ( 2003 ) Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics, 4, 11 – 15.en_US
dc.identifier.citedreferenceGelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. ( 2004 ) Bayesian Data Analysis, 2nd edn. Boca Raton: Chapman and Hall–CRC.en_US
dc.identifier.citedreferenceGenton, M. and Zhang, H. ( 2012 ) Identifiability problems in some non‐Gaussian spatial random fields. Chil. J. Statist., 3, 171 – 179.en_US
dc.identifier.citedreferenceGreen, P. J. ( 1995 ) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711 – 732.en_US
dc.identifier.citedreferenceGreen, P. J. and Richardson, S. ( 2002 ) Hidden Markov models and disease mapping. J. Am. Statist. Ass., 97, 1055 – 1070.en_US
dc.identifier.citedreferenceHaario, H., Saksman, E. and Tamminen, J. ( 2005 ) Componentwise adaptation for high dimensional MCMC. Computnl Statist., 20, 265 – 273.en_US
dc.identifier.citedreferenceIsmail, S., Sun, W., Nathoo, F. S., Babul, A., Moiseev, A., Beg, M. F. and Virji‐Babul, N. ( 2013 ) A skew‐t space‐varying regression model for the spectral analysis of resting state brain activity. Statist. Meth. Med. Res., 22, 424 – 438.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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