{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T21:48:29Z","timestamp":1771710509127,"version":"3.50.1"},"reference-count":60,"publisher":"Ubiquity Press, Ltd.","issue":"0","license":[{"start":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T00:00:00Z","timestamp":1501200000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CODATA"],"DOI":"10.5334\/dsj-2017-037","type":"journal-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T16:00:05Z","timestamp":1501257605000},"page":"37","source":"Crossref","is-referenced-by-count":47,"title":["Statistical Inference in Missing Data by MCMC and Non-MCMC Multiple Imputation Algorithms: Assessing the Effects of Between-Imputation Iterations"],"prefix":"10.5334","volume":"16","author":[{"given":"Masayoshi","family":"Takahashi","sequence":"first","affiliation":[]}],"member":"3285","published-online":{"date-parts":[[2017,7,28]]},"reference":[{"issue":"1","key":"key20170728120000_B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5183\/jjscs1988.20.1","article-title":"Evaluation of statistical methods for analysis of small-sample longitudinal clinical trials with dropouts","volume":"20","author":"Abe","year":"2007","journal-title":"Journal of the Japanese Society of Computational Statistics"},{"key":"key20170728120000_B2","volume-title":"Handbook of Economic Growth","author":"Acemoglu","year":"2005"},{"key":"key20170728120000_B3","doi-asserted-by":"crossref","DOI":"10.4135\/9781412985079","volume-title":"Missing Data","author":"Allison","year":"2002"},{"issue":"1","key":"key20170728120000_B4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.jsp.2009.10.001","article-title":"An introduction to modern missing data analyses","volume":"48","author":"Baraldi","year":"2010","journal-title":"Journal of School Psychology"},{"issue":"4","key":"key20170728120000_B5","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1093\/biomet\/86.4.948","article-title":"Small-sample degrees of freedom with multiple imputation","volume":"86","author":"Barnard","year":"1999","journal-title":"Biometrika"},{"key":"key20170728120000_B6","volume-title":"Determinants of Economic Growth: A Cross-Country Empirical Study","author":"Barro","year":"1997"},{"key":"key20170728120000_B7","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1080\/10705510802339072","article-title":"What improves with increased missing data imputations?","volume":"15","author":"Bodner","year":"2008","journal-title":"Structural Equation Modeling"},{"key":"key20170728120000_B8","doi-asserted-by":"crossref","DOI":"10.1002\/9781119942283","volume-title":"Multiple Imputation and its Application","author":"Carpenter","year":"2013"},{"key":"key20170728120000_B9","doi-asserted-by":"crossref","DOI":"10.4135\/9781483319605","volume-title":"Monte Carlo Simulation and Resampling Methods for Social Science","author":"Carsey","year":"2014"},{"key":"key20170728120000_B10","unstructured":"Central Intelligence Agency The World Factbook2016Available at: https:\/\/www.cia.gov\/library\/publications\/the-world-factbook\/index.html [Last accessed November 27, 2016]"},{"issue":"2","key":"key20170728120000_B11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.22237\/jmasm\/1414814520","article-title":"Some general guidelines for choosing missing data handling methods in educational research","volume":"13","author":"Cheema","year":"2014","journal-title":"Journal of Modern Applied Statistical Methods"},{"issue":"2","key":"key20170728120000_B12","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1017\/S0007123412000312","article-title":"We have to be discrete about this: A non-parametric imputation technique for missing categorical data","volume":"43","author":"Cranmer","year":"2013","journal-title":"British Journal of Political Science"},{"issue":"21689","key":"key20170728120000_B13","first-page":"1","article-title":"Multiple imputation for general missing data patterns in the presence of high-dimensional data","volume":"6","author":"Deng","year":"2016","journal-title":"Scientific Reports"},{"key":"key20170728120000_B14","doi-asserted-by":"crossref","DOI":"10.1002\/9780470904848","volume-title":"Handbook of Statistical Data Editing and Imputation","author":"de Waal","year":"2011"},{"issue":"8","key":"key20170728120000_B15","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1038\/nbt1406","article-title":"What is the expectation maximization algorithm?","volume":"26","author":"Do","year":"2008","journal-title":"Nature Biotechnology"},{"key":"key20170728120000_B16","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","article-title":"Review: A gentle introduction to imputation of missing values","volume":"59","author":"Donders","year":"2006","journal-title":"Journal of Clinical Epidemiology"},{"key":"key20170728120000_B17","volume-title":"Applied Missing Data Analysis","author":"Enders","year":"2010"},{"key":"key20170728120000_B18","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/2329.001.0001","volume-title":"Democracy, Governance, and Economic Performance: Theory and Evidence","author":"Feng","year":"2003"},{"key":"key20170728120000_B19","unstructured":"Freedom House Freedom in the World 20162016Available at: https:\/\/freedomhouse.org\/report\/freedom-world\/freedom-world-2016 [Last accessed November 30, 2016]"},{"key":"key20170728120000_B20","volume-title":"Bayesian Methods: A Social and Behavioral Sciences Approach","author":"Gill","year":"2008","edition":"Second Edition"},{"key":"key20170728120000_B21","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1146\/annurev.psych.58.110405.085530","article-title":"Missing data analysis: Making it work in the real world","volume":"60","author":"Graham","year":"2009","journal-title":"Annual Review of Psychology"},{"issue":"3","key":"key20170728120000_B22","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s11121-007-0070-9","article-title":"How many imputations are really needed? Some practical clarifications of multiple imputation theory","volume":"8","author":"Graham","year":"2007","journal-title":"Prevention Science"},{"key":"key20170728120000_B23","volume-title":"Basic Econometrics","author":"Gujarati","year":"2003","edition":"Fourth Edition"},{"issue":"184","key":"key20170728120000_B24","first-page":"1","article-title":"Auxiliary variables in multiple imputation in regression with missing X: A warning against including too many in small sample research","volume":"12","author":"Hardt","year":"2012","journal-title":"BMC Medical Research Methodology"},{"issue":"2","key":"key20170728120000_B25","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1540-5907.2010.00447.x","article-title":"What to do about missing values in time series cross-section data","volume":"54","author":"Honaker","year":"2010","journal-title":"American Journal of Political Science"},{"issue":"7","key":"key20170728120000_B26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v045.i07","article-title":"Amelia II: A program for missing data","volume":"45","author":"Honaker","year":"2011","journal-title":"Journal of Statistical Software"},{"key":"key20170728120000_B27","unstructured":"HonakerJ KingG BlackwellM Package \u2018Amelia\u20192016Available at: http:\/\/cran.r-project.org\/web\/packages\/Amelia\/Amelia.pdf [Last accessed November 30, 2016]"},{"key":"key20170728120000_B28","volume-title":"Handbook of Econometrics","author":"Horowitz","year":"2001"},{"issue":"1","key":"key20170728120000_B29","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1198\/000313007X172556","article-title":"Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models","volume":"61","author":"Horton","year":"2007","journal-title":"The American Statistician"},{"issue":"3","key":"key20170728120000_B30","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1198\/000313001317098266","article-title":"Multiple imputation in practice: Comparison of software packages for regression models with missing variables","volume":"55","author":"Horton","year":"2001","journal-title":"The American Statistician"},{"issue":"6","key":"key20170728120000_B31","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1177\/0962280214526216","article-title":"Comparison of imputation variance estimators","volume":"25","author":"Hughes","year":"2016","journal-title":"Statistical Methods in Medical Research"},{"issue":"1","key":"key20170728120000_B32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1017\/S0003055401000235","article-title":"Analyzing incomplete political science data: An alternative algorithm for multiple imputation","volume":"95","author":"King","year":"2001","journal-title":"American Political Science Review"},{"issue":"4","key":"key20170728120000_B33","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1093\/pan\/mpu007","article-title":"Multiple imputation for continuous and categorical data: Comparing joint multivariate normal and conditional approaches","volume":"22","author":"Kropko","year":"2014","journal-title":"Political Analysis"},{"issue":"5","key":"key20170728120000_B34","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1093\/aje\/kwp425","article-title":"Multiple imputation for missing data: Fully conditional specification versus multivariate normal imputation","volume":"171","author":"Lee","year":"2010","journal-title":"American Journal of Epidemiology"},{"issue":"3","key":"key20170728120000_B35","first-page":"1","article-title":"Recovery of information from multiple imputation: A simulation study","volume":"9","author":"Lee","year":"2012","journal-title":"Emerging Themes in Epidemiology"},{"issue":"1","key":"key20170728120000_B36","doi-asserted-by":"crossref","first-page":"64","DOI":"10.22237\/jmasm\/1272686820","article-title":"The performance of multiple imputation for Likert-type items with missing data","volume":"9","author":"Leite","year":"2010","journal-title":"Journal of Modern Applied Statistical Methods"},{"issue":"14","key":"key20170728120000_B37","first-page":"1","article-title":"Imputing missing data by fully conditional models: Some cautionary examples and guidelines","volume":"11","author":"Li","year":"2012","journal-title":"Duke University Department of Statistical Science Discussion Paper"},{"key":"key20170728120000_B38","doi-asserted-by":"crossref","DOI":"10.1002\/9781119013563","volume-title":"Statistical Analysis with Missing Data","author":"Little","year":"2002","edition":"Second Edition"},{"issue":"1","key":"key20170728120000_B39","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1080\/02664763.2016.1158246","article-title":"Missing data methods for arbitrary missingness with small samples","volume":"44","author":"McNeish","year":"2017","journal-title":"Journal of Applied Statistics"},{"key":"key20170728120000_B40","doi-asserted-by":"crossref","DOI":"10.4135\/9781412985116","volume-title":"Monte Carlo Simulation","author":"Mooney","year":"1997"},{"key":"key20170728120000_B41","volume-title":"Missing Data Analysis in Practice","author":"Raghunathan","year":"2016"},{"key":"key20170728120000_B42","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316696","volume-title":"Multiple Imputation for Nonresponse in Surveys","author":"Rubin","year":"1987"},{"key":"key20170728120000_B43","doi-asserted-by":"crossref","DOI":"10.1201\/9781439821862","volume-title":"Analysis of Incomplete Multivariate Data","author":"Schafer","year":"1997"},{"key":"key20170728120000_B44","unstructured":"SchaferJ L Package \u2018norm2\u20192016Available at: https:\/\/cran.r-project.org\/web\/packages\/norm2\/norm2.pdf [Last accessed November 30, 2016]"},{"issue":"2","key":"key20170728120000_B45","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1037\/1082-989X.7.2.147","article-title":"Missing data: Our view of the state of the art","volume":"7","author":"Schafer","year":"2002","journal-title":"Psychological Methods"},{"key":"key20170728120000_B46","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1207\/s15327906mbr3304_5","article-title":"Multiple imputation for multivariate missing-data problems: A data analyst\u2019s perspective","volume":"33","author":"Schafer","year":"1998","journal-title":"Multivariate Behavioral Research"},{"issue":"475","key":"key20170728120000_B47","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1198\/016214505000001375","article-title":"Multiple imputation of missing income data in the national health interview survey","volume":"101","author":"Schenker","year":"2006","journal-title":"Journal of the American Statistical Association"},{"issue":"4","key":"key20170728120000_B48","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1198\/000313005X74016","article-title":"Multiple imputation: How it began and continues","volume":"59","author":"Scheuren","year":"2005","journal-title":"The American Statistician"},{"issue":"9","key":"key20170728120000_B49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0138923","article-title":"Randomly and non-randomly missing renal function data in the strong heart study: A comparison of imputation methods","volume":"10","author":"Shara","year":"2015","journal-title":"PLOS ONE"},{"issue":"9","key":"key20170728120000_B50","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1093\/aje\/kwp026","article-title":"Multiple imputation with large data sets: A case study of the children\u2019s mental health initiative","volume":"169","author":"Stuart","year":"2009","journal-title":"American Journal of Epidemiology"},{"issue":"1","key":"key20170728120000_B51","doi-asserted-by":"crossref","first-page":"630","DOI":"10.22237\/jmasm\/1493598840","article-title":"Multiple ratio imputation by the EMB algorithm: Theory and simulation","volume":"16","author":"Takahashi","year":"2017","journal-title":"Journal of Modern Applied Statistical Methods"},{"issue":"1","key":"key20170728120000_B52","doi-asserted-by":"crossref","first-page":"657","DOI":"10.22237\/jmasm\/1493598900","article-title":"Implementing multiple ratio imputation by the EMB algorithm (R)","volume":"16","author":"Takahashi","year":"2017","journal-title":"Journal of Modern Applied Statistical Methods"},{"key":"key20170728120000_B53","first-page":"3240","article-title":"\u201cMultiple imputation of missing values in economic surveys: Comparison of competing algorithms,\u201d","author":"Takahashi","year":"2013"},{"issue":"71","key":"key20170728120000_B54","first-page":"39","article-title":"Comparison of competing algorithms of multiple imputation: Analysis using large-scale economic data","author":"Takahashi","year":"2014","journal-title":"Research Memoir of Official Statistics"},{"issue":"3","key":"key20170728120000_B55","doi-asserted-by":"crossref","DOI":"10.3233\/SJI-160306","article-title":"Imputing the mean of a heteroskedastic log-normal missing variable: A unified approach to ratio imputation","volume":"33","author":"Takahashi","year":"2017","journal-title":"Statistical Journal of the IAOS"},{"key":"key20170728120000_B56","doi-asserted-by":"crossref","DOI":"10.1201\/b11826","volume-title":"Flexible Imputation of Missing Data","author":"van Buuren","year":"2012"},{"issue":"3","key":"key20170728120000_B57","first-page":"1","article-title":"mice: multivariate imputation by chained equations in R","volume":"45","author":"van Buuren","year":"2011","journal-title":"Journal of Statistical Software"},{"key":"key20170728120000_B58","unstructured":"van BuurenS Groothuis-OudshoornK RoxitzschA VinkG DooveL JolaniS Package \u2018mice\u20192015Available at: https:\/\/cran.r-project.org\/web\/packages\/mice\/mice.pdf [Last accessed November 30, 2016]"},{"issue":"3","key":"key20170728120000_B59","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1080\/10705511.2015.1047931","article-title":"New confidence intervals and bias comparisons show that maximum likelihood can beat multiple imputation in small samples","volume":"23","author":"von Hippel","year":"2016","journal-title":"Structural Equation Modeling"},{"issue":"511","key":"key20170728120000_B60","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1080\/01621459.2014.948117","article-title":"Convergence properties of a sequential regression multiple imputation algorithm","volume":"110","author":"Zhu","year":"2015","journal-title":"Journal of the American Statistical Association"}],"container-title":["Data Science Journal"],"original-title":[],"link":[{"URL":"https:\/\/doi.org\/10.5334\/dsj-2017-037","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T09:19:22Z","timestamp":1657531162000},"score":1,"resource":{"primary":{"URL":"http:\/\/datascience.codata.org\/article\/10.5334\/dsj-2017-037\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,28]]},"references-count":60,"journal-issue":{"issue":"0","published-online":{"date-parts":[[2017,1,12]]}},"URL":"https:\/\/doi.org\/10.5334\/dsj-2017-037","relation":{},"ISSN":["1683-1470"],"issn-type":[{"value":"1683-1470","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,28]]}}}