Sandwich Variance Estimation in Scale Mixture of Skew-Normal Linear Mixed Models.
- 1Departamento de Estatística, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil.
- 2Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, USA.
Abstract
Linear mixed models are often used to analyze studies with repeated measurements over time, where the data present a correlation between observations of the same individual. However, one of the main challenges in using these models is correctly defining the covariance structure associated with error and random effects. To overcome this challenge, we obtain the sandwich variance estimator as a robust alternative to estimate the standard error in the scale mixture of skew-normal linear mixed models (SMSN-LMMs). By definition, SMSN-LMMs can accommodate skewness and heavy tails, and by considering our proposed variance estimator, we increase the model's flexibility by allowing for misspecification of the covariance components. We present the definition of the estimator, its derivation for this model, and its practical implementation. In addition, through simulation studies, we evaluate its performance in different scenarios, demonstrating its ability to provide reliable inferences even when the model or the covariance structure is misspecified.
Related
- Mixture of Censored Partially Linear Experts Models for Symmetric or Asymmetric Data with or without Outlying Observations: A novel approach with two-piece scale-mixture of normal distributions
- Variance estimation for generalized linear regression with binary outcome when using case-cohort data
- Robust estimation in generalized linear models based on the normal quantiles of the probability integral transformation
- Exact finite-sample bias, variance, and MSE of normalized scale-invariant inequality indices under Gamma mixture models
- An efficient EM algorithm for both element-wise and structural missingness in matrix-variate normal mixture models
BibTeX
@article{travassos2026sandwich,
title = {{Sandwich Variance Estimation in Scale Mixture of Skew-Normal Linear Mixed Models.}},
author = {Keyliane Travassos and Larissa A Matos and Fernanda L Schumacher},
journal = {Statistics in medicine},
year = {2026},
month = oct,
doi = {10.1002/sim.70753},
url = {https://europepmc.org/article/MED/42853020},
note = {CC BY},
}
RIS
TY - JOUR
TI - Sandwich Variance Estimation in Scale Mixture of Skew-Normal Linear Mixed Models.
AU - Keyliane Travassos
AU - Larissa A Matos
AU - Fernanda L Schumacher
PY - 2026
DA - 2026/10/01
JO - Statistics in medicine
DO - 10.1002/sim.70753
UR - https://europepmc.org/article/MED/42853020
AB - Linear mixed models are often used to analyze studies with repeated measurements over time, where the data present a correlation between observations of the same individual. However, one of the main challenges in using these models is correctly defining the covariance structure associated with error and random effects. To overcome this challenge, we obtain the sandwich variance estimator as a robust alternative to estimate the standard error in the scale mixture of skew-normal linear mixed models (SMSN-LMMs). By definition, SMSN-LMMs can accommodate skewness and heavy tails, and by considering our proposed variance estimator, we increase the model's flexibility by allowing for misspecification of the covariance components. We present the definition of the estimator, its derivation for this model, and its practical implementation. In addition, through simulation studies, we evaluate its performance in different scenarios, demonstrating its ability to provide reliable inferences even when the model or the covariance structure is misspecified.
SN - 0277-6715
SN - 1097-0258
ER -