Academic Commons Search Results
http://academiccommons.columbia.edu/catalog.rss?f%5Bauthor_facet%5D%5B%5D=Yajima%2C+Masanao&f%5Btype_of_resource_facet%5D%5B%5D=Text&q=&rows=500&sort=record_creation_date+desc
Academic Commons Search Resultsen-usMultiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box
http://academiccommons.columbia.edu/catalog/ac:154731
Su, Yu-Sung; Yajima, Masanao; Gelman, Andrew E.; Hill, Jenniferhttp://hdl.handle.net/10022/AC:P:15342Tue, 20 Nov 2012 00:00:00 +0000Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates state-of-the-art diagnostics that can be applied more generally and can be incorporated into the software of others.Statisticsag389 Political Science, StatisticsArticlesWhy we (usually) don't have to worry about multiple comparison
http://academiccommons.columbia.edu/catalog/ac:129500
Gelman, Andrew E.; Hill, Jennifer; Yajima, Masanaohttp://hdl.handle.net/10022/AC:P:9795Wed, 12 Jan 2011 00:00:00 +0000Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p-values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern.Statisticsag389Political Science, Statistics, Columbia Population Research CenterWorking papersWhy we (usually) don't have to worry about multiple comparisons
http://academiccommons.columbia.edu/catalog/ac:125255
Gelman, Andrew E.; Hill, Jennifer; Yajima, Masanaohttp://hdl.handle.net/10022/AC:P:8560Fri, 12 Mar 2010 00:00:00 +0000Statisticsag389Political Science, StatisticsPresentationsWhy we (usually) don't have to worry about multiple comparisons
http://academiccommons.columbia.edu/catalog/ac:125258
Gelman, Andrew E.; Hill, Jennifer; Yajima, Masanaohttp://hdl.handle.net/10022/AC:P:8561Fri, 12 Mar 2010 00:00:00 +0000Statisticsag389Political Science, StatisticsPresentationsWhy we (usually) don't have to worry about multiple comparisons
http://academiccommons.columbia.edu/catalog/ac:125225
Gelman, Andrew E.; Hill, Jennifer; Yajima, Masanaohttp://hdl.handle.net/10022/AC:P:8550Fri, 12 Mar 2010 00:00:00 +0000Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p-values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern.Statisticsag389Political Science, StatisticsArticles