Salīdzināt metodes

	Neiguatnā Binomiālā Regresija (Bayesian Negative Binomial Regression)	Modelis ar pārmērīgu nulles vērtību skaitu
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2000s	1992
Autors	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi	Diane Lambert
Tips	Bayesian GLM for overdispersed counts	Count regression with excess zeros
Pirmavots	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.
Citi nosaukumi	Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial
Saistītās	6	6
Kopsavilkums	Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates.	A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts.