Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

Linear mixed effects models are highly flexible in handling a broad range of data types and
are therefore widely used in applications. A key part in the analysis of data is model …

When comparing multilevel models (MLMs) differing in fixed and/or random effects,
researchers have had continuing interest in using R-squared differences to communicate …

Linear mixed-effects models are a class of models widely used for analyzing different types
of data: longitudinal, clustered and panel data. Many fields, in which a statistical …

A penalized likelihood (PL) method for structural equation modeling (SEM) was proposed as
a methodology for exploring the underlying relations among both observed and latent …

Mixed effects models have become increasingly prominent in sensory and consumer
science. Still applying such models may be challenging for a sensory practitioner due the …

Objective To describe decline in muscle strength and physical function in patients with
sporadic inclusion body myositis (IBM). Methods Manual muscle testing (MMT), quantitative …

Large, high-severity wildfires in many regions across the globe have increased concerns
about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes …

The application of generalized linear mixed models presents some major challenges for
both estimation, due to the intractable marginal likelihood, and model selection, as we …

Large sample techniques are fundamental to all fields of statistics. Mixed effects models,
including linear mixed models, generalized linear mixed models, non-linear mixed effects …