เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การถดถอยแบบเบตา× | Gamma Regression (GLM)× | การถดถอยโลจิสติก× | |
|---|---|---|---|
| สาขาวิชา≠ | สถิติศาสตร์ | สถิติศาสตร์ | สถิติการวิจัย |
| ตระกูล≠ | Regression model | Regression model | Process / pipeline |
| ปีกำเนิด≠ | 2004 | 1989 | 1958 |
| ผู้ริเริ่ม≠ | Ferrari & Cribari-Neto | McCullagh & Nelder (GLM framework) | David Roxbee Cox |
| ประเภท≠ | Generalized linear model (beta distribution) | Generalized linear model | Method |
| แหล่งต้นตำรับ≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| ชื่อเรียกอื่น | beta regression model, proportion regression, Beta Regresyonu | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | logit model, binomial logistic regression, LR |
| ที่เกี่ยวข้อง≠ | 4 | 4 | 3 |
| สรุป≠ | 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. | 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. | 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. |
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