12 citations · 55 across the 7 of their papers we have counts for
8 papers
A Diagnostic for Bias in Linear Mixed Model Estimators Induced by Dependence Between the Random Effects and the Corresponding Model Matrix
Andrew T. Karl, Dale L. Zimmerman
We explore how violations of the often-overlooked standard assumption that the random effects model matrix in a linear mixed model is fixed (and thus independent of the random effe…
Avoiding Bias Due to Nonrandom Scheduling When Modeling Trends in Home-Field Advantage
Andrew T. Karl
Existing approaches for estimating home-field advantage (HFA) include modeling the difference between home and away scores as a function of the difference between home and away tea…
Multivariate Generalized Linear Mixed Models for Joint Estimation of Sporting Outcomes
Jennifer E. Broatch, Andrew T. Karl
This paper explores improvements in prediction accuracy and inference capability when allowing for potential correlation in team-level random effects across multiple game-level res…
A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects
Andrew T. Karl, Yan Yang, Sharon L. Lohr
Value-added models have been widely used to assess the contributions of individual teachers and schools to students' academic growth based on longitudinal student achievement outco…
Computation of Maximum Likelihood Estimates for Multiresponse Generalized Linear Mixed Models with Non-nested, Correlated Random Effects
Andrew T. Karl, Yan Yang, Sharon L. Lohr
Estimation of generalized linear mixed models (GLMMs) with non-nested random effects structures requires approximation of high-dimensional integrals. Many existing methods are tail…
Using RngStreams for Parallel Random Number Generation in C++ and R
Andrew T. Karl, Randy Eubank, Jelena Milovanovic +2
The RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techn…
Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments
Andrew T. Karl, Yan Yang, Sharon L. Lohr
The generalized persistence (GP) model, developed in the context of estimating ``value added'' by individual teachers to their students' current and future test scores, is one of t…
The Sensitivity of College Football Rankings to Several Modeling Choices
Andrew T. Karl
This paper proposes a multiple-membership generalized linear mixed model for ranking college football teams using only their win/loss records. The model results in an intractable,…