Salīdzināt metodes

	Beta regresija	Logistiskā regresija	Kvantīļu regresija
Nozare	Statistika	Pētniecības statistika	Ekonometrija
Saime	Regression model	Process / pipeline	Regression model
Izcelsmes gads	2004	1958	1978
Autors	Ferrari & Cribari-Neto	David Roxbee Cox	Koenker & Bassett
Tips	Generalized linear model (beta distribution)	Method	Conditional quantile regression
Pirmavots	Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Citi nosaukumi	beta regression model, proportion regression, Beta Regresyonu	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Saistītās	4	3	5
Kopsavilkums	Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.