পদ্ধতির তুলনা করুন

	Tobit Censored Regression Model	লজিস্টিক রিগ্রেশন	কোয়ান্টাইল রিগ্রেশন
ক্ষেত্র	অর্থমিতি	গবেষণা পরিসংখ্যান	অর্থমিতি
পরিবার	Regression model	Process / pipeline	Regression model
উদ্ভবের বছর	1958	1958	1978
প্রবর্তক	James Tobin	David Roxbee Cox	Koenker & Bassett
ধরন	Censored regression (limited dependent variable)	Method	Conditional quantile regression
মৌলিক উৎস	Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.	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.
অপর নাম	censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
সম্পর্কিত	4	3	5
সারসংক্ষেপ	The Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in data such as spending, wages, or duration.	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.