Aug 1, 2022 · Fundamentals

When to Use Poisson vs. Negative Binomial Regression: A Practical Guide

Poisson regression models count data assuming equal mean and variance, while negative binomial regression handles over‑dispersion by adding a gamma‑distributed heterogeneity term; this article explains their assumptions, likelihoods, interpretation of incidence rate ratios, and guidance on choosing the appropriate model with robust standard errors.

MLEPoisson regressioncount data

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When to Use Poisson vs. Negative Binomial Regression: A Practical Guide

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Dec 16, 2021 · Fundamentals

How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression

This article explains how mixing a Poisson process with a discrete k‑state hidden Markov model creates a Poisson HMM that captures autocorrelation in integer‑valued time‑series, detailing the model formulation, prediction via expectation over states, and parameter estimation using MLE or EM.

EMMLEMarkov model

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How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression