Joseph L. Schafer
Affiliations: | Pennsylvania State University, State College, PA, United States |
Area:
StatisticsGoogle:
"Joseph Schafer"Children
Sign in to add traineeRecai M. Yucel | grad student | 2000 | Penn State |
Hwan Chung | grad student | 2003 | Penn State |
Hakan Demirtas | grad student | 2003 | Penn State |
Hyekyung Jung | grad student | 2007 | Penn State |
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Publications
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Schafer JL. (2012) Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations Models For Intensive Longitudinal Data |
Chung H, Anthony JC, Schafer JL. (2011) Latent class profile analysis: an application to stage-sequential process in early-onset drinking behaviours. Journal of the Royal Statistical Society. Series a, (Statistics in Society). 174: 689-712 |
Chung H, Anthony JC, Schafer JL. (2011) Latent class profile analysis: An application to stage sequential processes in early onset drinking behaviours Journal of the Royal Statistical Society. Series a: Statistics in Society. 174: 689-712 |
Jung H, Schafer JL, Seo B. (2011) A latent class selection model for nonignorably missing data Computational Statistics and Data Analysis. 55: 802-812 |
Harel O, Schafer JL. (2009) Partial and latent ignorability in missing-data problems Biometrika. 96: 37-50 |
Schafer JL, Kang J. (2008) Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychological Methods. 13: 279-313 |
Lanza ST, Collins LM, Lemmon DR, et al. (2007) PROC LCA: A SAS Procedure for Latent Class Analysis. Structural Equation Modeling : a Multidisciplinary Journal. 14: 671-694 |
Bernaards CA, Belin TR, Schafer JL. (2007) Robustness of a multivariate normal approximation for imputation of incomplete binary data. Statistics in Medicine. 26: 1368-82 |
Chung H, Flaherty BP, Schafer JL. (2006) Latent class logistic regression: Application to marijuana use and attitudes among high school seniors Journal of the Royal Statistical Society. Series a: Statistics in Society. 169: 723-743 |
Lanza ST, Collins LM, Schafer JL, et al. (2005) Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis. Psychological Methods. 10: 84-100 |