# Semiparametric discrete data regression with Monte Carlo inference and prediction

arXiv: https://arxiv.org/abs/2110.12316 · HTML: https://mlsanity.com/paper/2110.12316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 23 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Conjugate Bayes for probit regression via unified skew-normal distributions](https://mlsanity.com/paper/1802.09565/) (2018)
- [A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One](https://mlsanity.com/paper/2007.06944/) (2020)
- [Bayesian Inference in Nonparanormal Graphical Models](https://mlsanity.com/paper/1806.04334/) (2018)
- [Latent Gaussian Count Time Series](https://mlsanity.com/paper/1811.00203/) (2018)
- [Simultaneous Transformation and Rounding (STAR) Models for Integer-Valued Data](https://mlsanity.com/paper/1906.11653/) (2019)
- [Nonparametric Bayes modeling of count processes](https://mlsanity.com/paper/1307.2822/) (2013)
- [Semiparametric count data regression for self-reported mental health](https://mlsanity.com/paper/2106.09114/) (2021)
- [Discrete Weibull generalised additive model: an application to count fertility data](https://mlsanity.com/paper/1801.07905/) (2018)

## Built on by (most cited first, 2 of 2)

- [Warped Dynamic Linear Models for Time Series of Counts](https://mlsanity.com/paper/2110.14790/) (2021)
- [Monte Carlo inference for semiparametric Bayesian regression](https://mlsanity.com/paper/2306.05498/) (2023)

## Abstract (arXiv)

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, boundedness, and heaping, which render many parametric models inadequate. Yet even for parametric regression models, approximations such as MCMC typically are needed for posterior inference. This paper introduces a Bayesian modeling and algorithmic framework that enables semiparametric regression analysis for discrete data with Monte Carlo (not MCMC) sampling. The proposed approach pairs a nonparametric marginal model with a latent linear regression model to encourage both flexibility and interpretability, and delivers posterior consistency even under model misspecification. For a parametric or large-sample approximation of this model, we identify a class of conjugate priors with (pseudo) closed-form posteriors. All posterior and predictive distributions are available analytically or via direct Monte Carlo sampling. These tools are broadly useful for linear regression, nonlinear models via basis expansions, and variable selection with discrete data. Simulation studies demonstrate significant advantages in computing, prediction, estimation, and selection relative to existing alternatives. This novel approach is applied successfully to self-reported mental health data that exhibit zero-inflation, overdispersion, boundedness, and heaping.