Below is a list of my main methodological papers. For a complete publication list, se my Google Scholar Profile.
A Hybrid NUTS-Gibbs Sampler with State Space Marginalization for Estimation of Dynamic Structural Equation Models with Binomial Outcomes
This paper presents a hybrid NUTS-Gibbs sampler for dynamic structural equation models with binomial outcomes. The Gibbs step handles Pólya-Gamma latent variables from a logit link, while the NUTS step uses a Kalman filter to marginalize over latent states. arXiv preprint.
A Semiparametric Nonlinear Mixed Effects Model with Penalized Splines Using Automatic Differentiation
We present an estimation procedure for nonlinear mixed-effects models in which the population trajectory is represented by penalized splines and adapted to individuals via subject-specific transformation parameters. Exact derivatives are obtained via automatic differentiation implemented through Template Model Builder. arXiv preprint.
Efficient Bayesian Estimation of Dynamic Structural Equation Models via State Space Marginalization
This paper shows that the within-level part of any dynamic structural equation model can be reformulated as a linear Gaussian state space model, enabling analytical marginalization via a Kalman filter and highly efficient estimation via Hamiltonian Monte Carlo. arXiv preprint.
Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods
This paper evaluates multiple interpretability techniques for machine learning models applied to neuroimaging data, including SHAP and SAGE. We trained XGBoost models to predict age and fluid intelligence using UK Biobank data and found that subcortical mean intensities are associated with brain aging, while fluid intelligence prediction is driven by the hippocampus and cerebellum. Published in Neuroinformatics.
Modeling Cycles, Trends and Time-Varying Effects in Dynamic Structural Equation Models with Regression Splines
This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. arXiv preprint currently under revision.
Sequential Rank and Preference Learning with the Bayesian Mallows Model
This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. Published in Bayesian Analysis.
Multilevel Semiparametric Latent Variable Modeling in R with "galamm"
This paper presents the R package "galamm", which contains open-source implementations for generalized additive latent and mixed models (GALAMMs). Published in Multivariate Behavioral Research.
Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models
This paper was motivated by the need to use flexible spline models to model how cognitive abilities change with age, and investigating how changes in abilities in certain cognitive domains can be explained by changes in the brain. To this end we developed a framework called Generalized Additive Latent and Mixed Models (GALAMM), combining generalized additive mixed models, structural equation modeling, and mxied effect modeling. We also propose algorithms for estimating the models, using sparse matrix methods and automatic differentation. Published in Psychometrika.
A recipe for accurate estimation of lifespan brain trajectories, distinguishing longitudinal and cohort effects
This paper considers estimation of lifespan brain trajectories from longitudinal data, using generalized additive mixed models. It studies how to distinguish longitudinal effects from cohort effects, and shows example code in R which practitioners can use for their own data. Published in NeuroImage.
Meta-analysis of generalized additive models in neuroimaging studies
This paper considers meta analysis of nonlinear functions estimated by generalized additive (mixed) models.
BayesMallows: An R Package for the Bayesian Mallows Model
This paper presents the BayesMallows R package, for analysis of rank and preference data. Published in the R Journal.
From observed laterality to latent hemispheric differences: Revisiting the inference problem
This paper considers predicting whether people are right or left brain dominant for language processing, based on their result in dichotic listening experiments as well as their handedness.
hdme: High-Dimensional Regression with Measurement Error
This paper presents the hdme package, implementing variable selection methods for regression with covariate measurement error.
Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
This paper proposes an extension of the generalized Dantzig selector for cases with measurement error in the predictor variables.
Probabilistic preference learning with the Mallows rank model
This paper consider probabilistic preference learning with Mallows' rank model.
Measurement Error in Lasso: Impact and Likelihood Bias Correction
This paper analyzes the impact of covariate measurement error in the lasso method for penalized regression, and then proposes correction methods both for linear models and generalized linear models.