29 citations · 59 across the 4 of their papers we have counts for
4 papers
Self-normalized score-based tests to detect parameter heterogeneity for mixed models
Ting Wang, Edgar Merkle
Score-based tests have been used to study parameter heterogeneity across many types of statistical models. This chapter describes a new self-normalization approach for score-based…
Computation and application of generalized linear mixed model derivatives using lme4
Ting Wang, Benjamin Graves, Yves Rosseel +1
Maximum likelihood estimation of generalized linear mixed models(GLMMs) is difficult due to marginalization of the random effects. Computing derivatives of a fitted GLMM's likeliho…
Score-based Tests for Explaining Upper-Level Heterogeneity in Linear Mixed Models
Ting Wang, Edgar C. Merkle, Joaquin A. Anguera +1
Cross-level interactions among fixed effects in linear mixed models (also known as multilevel models) are often complicated by the variances stemming from random effects and residu…
Derivative Computations and Robust Standard Errors for Linear Mixed Effects Models in lme4
Ting Wang, Edgar C. Merkle
While robust standard errors and related facilities are available in R for many types of statistical models, the facilities are notably lacking for models estimated via lme4. This…