방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 베타 회귀분석× | 감마 회귀 (GLM)× | 조건부 분위수 회귀× | |
|---|---|---|---|
| 분야≠ | 통계학 | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model | Regression model |
| 기원 연도≠ | 2004 | 1989 | 1978 |
| 창시자≠ | Ferrari & Cribari-Neto | McCullagh & Nelder (GLM framework) | Koenker & Bassett |
| 유형≠ | Generalized linear model (beta distribution) | Generalized linear model | Conditional quantile regression |
| 원전≠ | 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 ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 별칭 | beta regression model, proportion regression, Beta Regresyonu | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 관련≠ | 4 | 4 | 5 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
|
|
|