Longitudinal data analysis using generalized linear models

By K. Liang, Scott Zeger. 1986

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, methodology): “We can do so using the robust clustering standard errors proposed by Liang and Zeger (1986).”
Inhibiting natural knowledge in fourth graders: towards a comprehensive test instrument (2015, methodology): “This was done using the generalized estimation of equations (GEE) module in SPSS 22, in order to correct for repeated (and therefore probably correlated) measures within subjects (Liang and Zeger 1986).”
Exact and approximate REML for heteroscedastic regression (2001, background): “…variance of the estimator ~c defined by (9) is given by the robust sandwich estimator var ~c E @u @cT 1 var u E @uT @c 1 ð10Þ This expression is based on a first order Taylor series expansion of the estimating equation (9) at ~c about the true value of c (Huber, 1967; Liang and Zeger, 1986).”
A Meta-analysis of the Spacing Effect in Verbal Learning: Implications for Research on Advertising Repetition and Consumer Memory (2003, methodology): “The GEE procedure is recommended when observations are correlated because they are repeated or because they have a common characteristic (Liang and Zeger 1986).”
Wealth, Health, and Child Development: Evidence from Administrative Data on Swedish Lottery Players* (2016, background): “We therefore reported clustered standard errors (Liang and Zeger 1986) throughout the manuscript.”
The Calculus of M-Estimation (2002, unknown): “Instead, Liang and Zeger (1986) noticed that the M-estimator approach could be used here without (cid:28) and with only the mean correctly speci(cid:12)ed: : Liang and Zeger (1986) actually proposed a generalized set of estimating equations that accommodates independent clusters of correlated data.”
The effects of aquaculture on bottlenose dolphin (Tursiops sp.) ranging in Shark Bay, Western Australia (2005, methodology): “A regression using general estimating equations was employed to compare the average distance from the oyster farm (reported use area and the extension area) between time periods while accounting for variation between individuals, year, month, and time of day ( Liang and Zeger, 1986; SAS v8).”
Effects of Search Timing on Innovation: The Value of Not Being in Sync with Rivals (2008, methodology): “To control for repeated observations for the same firm, we employed the Generalized Estimating Equations (GEE) regression method, which accounts for autocorrelation that may arise because each firm is measured repeatedly across multiple years (Liang and Zeger, 1986).”