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Found 10 papers, 5 papers with code
Monte Carlo inference for semiparametric Bayesian regression
Data transformations are essential for broad applicability of parametric regression models.
Bayesian adaptive and interpretable functional regression for exposure profiles
Leveraging the proposed modeling, computational, and decision analysis framework, we conclude that prenatal $\mbox{PM}_{2. 5}$ exposure during early and late pregnancy is most adverse for 4th end-of-grade reading scores.
Warped Dynamic Linear Models for Time Series of Counts
However, the options for count time series are limited: Gaussian DLMs require continuous data, while Poisson-based alternatives often lack sufficient modeling flexibility.
Semiparametric discrete data regression with Monte Carlo inference and prediction
These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models inadequate.
Subset selection for linear mixed models
We introduce a Bayesian decision analysis for subset selection with LMMs.
Semiparametric count data regression for self-reported mental health
STAR is deployed to study the factors associated with self-reported mental health and demonstrates substantial improvements in goodness-of-fit compared to existing count data regression models.
Bayesian subset selection and variable importance for interpretable prediction and classification
Given any Bayesian predictive model $\mathcal{M}$, we extract a family of near-optimal subsets of variables for linear prediction or classification.
Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis
Instead, we design a class of parametrized actions for Bayesian decision analysis that produce optimal, scalable, and simple targeted predictions.
Simultaneous Transformation and Rounding (STAR) Models for Integer-Valued Data
We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data.
Dynamic Function-on-Scalars Regression
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.
Methodology
