विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| गामा रिग्रेशन (GLM)× | ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|---|
| क्षेत्र≠ | सांख्यिकी | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model | Regression model |
| उद्भव वर्ष≠ | 1989 | 2011 | 2019 |
| प्रवर्तक≠ | McCullagh & Nelder (GLM framework) | Hilbe (textbook treatment); generalized linear model framework | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Generalized linear model | Generalized linear model for count data | Linear regression |
| मौलिक स्रोत≠ | McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| उपनाम≠ | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | NB regression, NB2 regression, negatif binom regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित≠ | 4 | 4 | 5 |
| सारांश≠ | Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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