# Scale mixture of skew-normal linear mixed models with within-subject serial dependence

arXiv: https://arxiv.org/abs/2002.01040 · HTML: https://mlsanity.com/paper/2002.01040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [A Monotone Single-Index Modal Regression Powered by Deep Neural Networks for Non-Gaussian Periodontal Data](https://mlsanity.com/paper/2505.02153/) (2025)

## Abstract (arXiv)

In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. In this paper we consider an extension of skew-normal/independent linear mixed models introduced by Lachos et al. (2010), where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. For this robust model, we present an efficient EM-type algorithm for computation of maximum likelihood estimation of parameters and the observed information matrix is derived analytically to account for standard errors. The methodology is illustrated through an application to schizophrenia data and several simulation studies. The proposed algorithm and methods are implemented in the new R package skewlmm.