Vertaile menetelmiä

	Bayesilainen regressio	Coxin hazard-suhteiden regressiomalli	Weibull Parametrinen Selviytymisregressio
Tieteenala	Bayesilainen tilastotiede	Elinaika-analyysi	Elinaika-analyysi
Menetelmäperhe	Bayesian methods	Survival analysis	Survival analysis
Syntyvuosi	—	1972	1951
Kehittäjä	—	Cox, D. R.	Waloddi Weibull
Tyyppi	Bayesian linear model	Semi-parametric hazard regression model	Fully parametric survival regression model
Alkuperäislähde	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	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Rinnakkaisnimet	bayesian linear regression, probabilistic regression, bayesian regresyon	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Liittyvät	2	3	4
Tiivistelmä	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.