Daniel R. Kowal | DeepAI

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Featured Co-authors

Antonio Canale
18 publications
Bohan Wu
10 publications
Brian King
8 publications
Joseph Feldman
2 publications
Daniel C. Bourgeois
1 publication
Yunan Gao
1 publication
Thomas Y. Sun
1 publication

∙ 06/12/2023

Ultra-efficient MCMC for Bayesian longitudinal functional data analysis

Functional mixed models are widely useful for regression analysis with d...

0 Thomas Y. Sun, et al. ∙

∙ 06/08/2023

Monte Carlo inference for semiparametric Bayesian regression

Data transformations are essential for broad applicability of parametric...

0 Daniel R. Kowal, et al. ∙

∙ 10/26/2022

Nonparametric Copula Models for Mixed Data with Informative Missingness

Modern datasets commonly feature both substantial missingness and variab...

0 Joseph Feldman, et al. ∙

∙ 03/01/2022

Bayesian adaptive and interpretable functional regression for exposure profiles

Pollutant exposures during gestation are a known and adverse factor for ...

0 Yunan Gao, et al. ∙

∙ 10/27/2021

Warped Dynamic Linear Models for Time Series of Counts

Dynamic Linear Models (DLMs) are commonly employed for time series analy...

0 Brian King, et al. ∙

∙ 10/23/2021

Conjugate priors for count and rounded data regression

Discrete data are abundant and often arise as counts or rounded data. Ye...

0 Daniel R. Kowal, et al. ∙

∙ 08/04/2021

Semiparametric Functional Factor Models with Bayesian Rank Selection

Functional data are frequently accompanied by parametric templates that ...

0 Daniel R. Kowal, et al. ∙

∙ 07/27/2021

Subset selection for linear mixed models

Linear mixed models (LMMs) are instrumental for regression analysis with...

0 Daniel R. Kowal, et al. ∙

∙ 06/16/2021

Semiparametric count data regression for self-reported mental health

"For how many days during the past 30 days was your mental health not go...

0 Daniel R. Kowal, et al. ∙

∙ 04/20/2021

Bayesian subset selection and variable importance for interpretable prediction and classification

Subset selection is a valuable tool for interpretable learning, scientif...

0 Daniel R. Kowal, et al. ∙

∙ 06/23/2020

Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis

Prediction is critical for decision-making under uncertainty and lends v...

0 Daniel R. Kowal, et al. ∙

∙ 06/27/2019

A Simultaneous Transformation and Rounding Approach for Modeling Integer-Valued Data

We propose a simple yet powerful framework for modeling integer-valued d...

1 Daniel R. Kowal, et al. ∙

∙ 06/27/2019

Simultaneous Transformation and Rounding (STAR) Models for Integer-Valued Data

We propose a simple yet powerful framework for modeling integer-valued d...

0 Daniel R. Kowal, et al. ∙

∙ 02/20/2019

Integer-Valued Functional Data Analysis for Measles Forecasting

Measles presents a unique and imminent challenge for epidemiologists and...

0 Daniel R. Kowal, et al. ∙

∙ 08/20/2018

Bayesian Function-on-Scalars Regression for High Dimensional Data

We develop a fully Bayesian framework for function-on-scalars regression...

0 Daniel R. Kowal, et al. ∙

∙ 06/05/2018

Dynamic Function-on-Scalars Regression

We develop a modeling framework for dynamic function-on-scalars regressi...

0 Daniel R. Kowal, et al. ∙

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