78 citations · 210 across the 23 of their papers we have counts for
7 papers · 1 filter
Warped Dynamic Linear Models for Time Series of Counts
Brian King, Daniel R. Kowal
Dynamic Linear Models (DLMs) are commonly employed for time series analysis due to their versatile structure, simple recursive updating, ability to handle missing data, and probabi…
Semiparametric discrete data regression with Monte Carlo inference and prediction
Daniel R. Kowal, Bohan Wu
Discrete data are abundant and often arise as counts or rounded data. These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, bo…
Semiparametric Functional Factor Models with Bayesian Rank Selection
Daniel R. Kowal, Antonio Canale
Functional data are frequently accompanied by a parametric template that describes the typical shapes of the functions. However, these parametric templates can incur significant bi…
Subset selection for linear mixed models
Daniel R. Kowal
Linear mixed models (LMMs) are instrumental for regression analysis with structured dependence, such as grouped, clustered, or multilevel data. However, selection among the covaria…
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…
Bayesian subset selection and variable importance for interpretable prediction and classification
Daniel R. Kowal
Subset selection is a valuable tool for interpretable learning, scientific discovery, and data compression. However, classical subset selection is often avoided due to selection in…
Bayesian Data Synthesis and the Utility-Risk Trade-Off for Mixed Epidemiological Data
Joseph Feldman, Daniel Kowal
Much of the micro data used for epidemiological studies contain sensitive measurements on real individuals. As a result, such micro data cannot be published out of privacy concerns…