11 citations · 15 across the 3 of their papers we have counts for
7 papers
Ultra-efficient MCMC for Bayesian longitudinal functional data analysis
Thomas Y. Sun, Daniel R. Kowal
Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing a…
Semiparametric count data regression for self-reported mental health
Daniel R. Kowal, Bohan Wu
"For how many days during the past 30 days was your mental health not good?" The responses to this question measure self-reported mental health and can be linked to important covar…
Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis
Daniel R. Kowal
Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific…
Simultaneous Transformation and Rounding (STAR) Models for Integer-Valued Data
Daniel R. Kowal, Antonio Canale
We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data. The data-generating process is defined by Simultaneously Tran…
Integer-Valued Functional Data Analysis for Measles Forecasting
Daniel R. Kowal
Measles presents a unique and imminent challenge for epidemiologists and public health officials: the disease is highly contagious, yet vaccination rates are declining precipitousl…
Bayesian Function-on-Scalars Regression for High Dimensional Data
Daniel R. Kowal, Daniel C. Bourgeois
We develop a fully Bayesian framework for function-on-scalars regression with many predictors. The functional data response is modeled nonparametrically using unknown basis functio…