78 citations · 210 across the 23 of their papers we have counts for
24 papers
Graph-dependent shrinkage priors for Bayesian trend filtering
Andrea Mascaretti, Daniel R. Kowal
Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), images appear as pixels (lattice graph), areal data are defined by neig…
Efficient Bayesian inference for two-stage models in environmental epidemiology
Konstantin Larin, Daniel R. Kowal
Statistical models often require inputs that are not completely known. This can occur when inputs are measured with error, indirectly, or when they are predicted using another mode…
A dynamic copula model for probabilistic forecasting of non-Gaussian multivariate time series
John Zito, Daniel R. Kowal
Multivariate time series (MTS) data often include a heterogeneous mix of non-Gaussian distributional features (asymmetry, multimodality, heavy tails) and data types (continuous and…
Bayesian Functional Graphical Models with Change-Point Detection
Chunshan Liu, Daniel R. Kowal, James Doss-Gollin +1
Functional data analysis, which models data as realizations of random functions over a continuum, has emerged as a useful tool for time series data. Often, the goal is to infer the…
The projected dynamic linear model for time series on the sphere
John Zito, Daniel Kowal
Time series on the unit n-sphere arise in directional statistics, compositional data analysis, and many scientific fields. There are few models for such data, and the ones that exi…
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…
Monte Carlo inference for semiparametric Bayesian regression
Daniel R. Kowal, Bohan Wu
Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model paramete…
Nonparametric Copula Models for Multivariate, Mixed, and Missing Data
Joseph Feldman, Daniel R. Kowal
Modern datasets commonly feature both substantial missingness and many variables of mixed data types, which present significant challenges for estimation and inference. Complete ca…
Bayesian adaptive and interpretable functional regression for exposure profiles
Yunan Gao, Daniel R. Kowal
Pollutant exposure during gestation is a known and adverse factor for birth and health outcomes. However, the links between prenatal air pollution exposures and educational outcome…
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…
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…
Dynamic Function-on-Scalars Regression
Daniel R. Kowal
We develop a modeling framework for dynamic function-on-scalars regression, in which a time series of functional data is regressed on a time series of scalar predictors. The regres…
Dynamic Shrinkage Processes
Daniel R. Kowal, David S. Matteson, David Ruppert
We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building upon a global-local framework of prior construction, in which con…
Functional Autoregression for Sparsely Sampled Data
Daniel R. Kowal, David S. Matteson, David Ruppert
We develop a hierarchical Gaussian process model for forecasting and inference of functional time series data. Unlike existing methods, our approach is especially suited for sparse…
A Bayesian Multivariate Functional Dynamic Linear Model
Daniel R. Kowal, David S. Matteson, David Ruppert
We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent,…