A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data

By M. Olsen, Joseph L. Schafer. 2001

600 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, methodology): “, log-normal) rather than Poisson distribution for the non-zero values (Duan, 1983; Olsen and Schafer, 2001; Tooze et al., 2002).”
Multiple Edit/Multiple Imputation for Multivariate Continuous Data (2003, background): “In contrast, the NHANES III errors are often (essentially) zero and continuouslydistributed otherwise, and thus are inherently semicontinuous(Olsen and Schafer 2001).”
Causal Inference With General Treatment Regimes (2004, methodology): “It is in this setting that the propensity score of Rosenbaum and Rubin (1983b) has found wide applicability in empirical research; in particular, the method has rapidly become popular in the social sciences (e.”
Count Panel Data (2015, background): “Blundell, Gri th and Windmeijer (1997) adapt serial correlation tests proposed by Arellano and Bond (1991) for the linear model.”
Predictive mean matching imputation of semicontinuous variables (2014, methodology): “This model presents the data as a two-part mixture of a normal distribution and a point mass (Schafer and Olsen, 1999; Olsen and Schafer, 2001), thereby decomposing the semicontinuous observations into two variables that can be modeled in succession.”
The joy of pain and the pain of joy: In-group identification predicts schadenfreude and gluckschmerz following rival groups’ fortunes (2015, result): “Fourth, we obtained a large sample for Study 3 (N = 279), enabling us to ‘‘two-part model’’ (Olsen and Schafer 2001) schadenfreude and gluckschmerz by first modeling the presence or absence of schadenfreude and gluckschmerz (i.”
Multilevel modelling of medical data (2002, background): “See also Olsen and Schafer [37] for an alternative approach.”
Analyzing Skewed Continuous Outcomes With Many Zeros: A Tutorial for Social Work and Youth Prevention Science Researchers (2018, background): “…interpretation of the regression coefficients and associated inferences—regardless of which approach is chosen to handle potential skewness—applies only to individuals drawn from a population in which nonzero levels are expected (Eisenberg et al., 2015; Neelon et al., 2016; Olsen & Schafer, 2001).”