A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores
dc.contributor.author | Neelon, Brian | en_US |
dc.contributor.author | Gelfand, Alan E. | en_US |
dc.contributor.author | Miranda, Marie Lynn | en_US |
dc.date.accessioned | 2014-10-07T16:09:50Z | |
dc.date.available | WITHHELD_14_MONTHS | en_US |
dc.date.available | 2014-10-07T16:09:50Z | |
dc.date.issued | 2014-11 | en_US |
dc.identifier.citation | Neelon, 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.issn | 0035-9254 | en_US |
dc.identifier.issn | 1467-9876 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/108684 | |
dc.publisher | Chapman and Hall–CRC | en_US |
dc.publisher | Wiley Periodicals, Inc. | en_US |
dc.subject.other | Bayesian Analysis | en_US |
dc.subject.other | Conditional Auto‐Regressive Prior | en_US |
dc.subject.other | Education Data | en_US |
dc.subject.other | Finite Mixture Model | en_US |
dc.subject.other | Multivariate Spatial Analysis | en_US |
dc.subject.other | Areal Data | en_US |
dc.title | A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/108684/1/rssc12061.pdf | |
dc.identifier.doi | 10.1111/rssc.12061 | en_US |
dc.identifier.source | Journal of the Royal Statistical Society: Series C (Applied Statistics) | en_US |
dc.identifier.citedreference | North 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.citedreference | Ji, 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.citedreference | Jin, X., Carlin, B. P. and Banerjee, S. ( 2005 ) Generalized hierarchical multivariate car models for areal data. Biometrics, 61, 950 – 961. | en_US |
dc.identifier.citedreference | Kottas, 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.citedreference | Kottas, 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.citedreference | Lawson, A. B. and Clark, A. ( 2002 ) Spatial mixture relative risk models applied to disease mapping. Statist. Med., 21, 359 – 370. | en_US |
dc.identifier.citedreference | Mardia, K. ( 1988 ) Multi‐dimensional multivariate Gaussian Markov random fields with application to image processing. J. Multiv. Anal., 24, 265 – 284. | en_US |
dc.identifier.citedreference | McLachlan, G. and Peel, D. ( 2000 ) Finite Mixture Models. New York: Wiley. | en_US |
dc.identifier.citedreference | Nathoo, 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.citedreference | National 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.citedreference | North 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.citedreference | North 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.citedreference | Pope, 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.citedreference | R Development Core Team ( 2011 ) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. | en_US |
dc.identifier.citedreference | Reich, 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.citedreference | Richardson, 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.citedreference | Sethuraman, J. ( 1994 ) A constructive definition of Dirichlet priors. Statist. Sin., 4, 639 – 650. | en_US |
dc.identifier.citedreference | Spiegelhalter, 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.citedreference | Stephens, M. ( 2000 ) Dealing with label switching in mixture models. J. R. Statist. Soc. B, 62, 795 – 809. | en_US |
dc.identifier.citedreference | US 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.citedreference | Wall, 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.citedreference | Zareifard, 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.citedreference | Zhang, 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.citedreference | Banerjee, 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.citedreference | Besag, 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.citedreference | Besag, 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.citedreference | Carlin, 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.citedreference | Celeux, 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.citedreference | Congdon, 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.citedreference | Ferguson, R. ( 2008 ) Toward Excellence with Equity: an Emerging Vision for Closing the Achievement Gap. Cambridge: Harvard Education. | en_US |
dc.identifier.citedreference | Frühwirth‐Schnatter, S. ( 2006 ) Finite Mixture and Markov Switching Models. Berlin: Springer. | en_US |
dc.identifier.citedreference | Gelfand, 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.citedreference | Gelfand, A. E. and Vounatsou, P. ( 2003 ) Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics, 4, 11 – 15. | en_US |
dc.identifier.citedreference | Gelman, 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.citedreference | Genton, M. and Zhang, H. ( 2012 ) Identifiability problems in some non‐Gaussian spatial random fields. Chil. J. Statist., 3, 171 – 179. | en_US |
dc.identifier.citedreference | Green, P. J. ( 1995 ) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711 – 732. | en_US |
dc.identifier.citedreference | Green, P. J. and Richardson, S. ( 2002 ) Hidden Markov models and disease mapping. J. Am. Statist. Ass., 97, 1055 – 1070. | en_US |
dc.identifier.citedreference | Haario, H., Saksman, E. and Tamminen, J. ( 2005 ) Componentwise adaptation for high dimensional MCMC. Computnl Statist., 20, 265 – 273. | en_US |
dc.identifier.citedreference | Ismail, 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.owningcollname | Interdisciplinary and Peer-Reviewed |
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