Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Узагальнені адитивні моделі для розташування, масштабу та форми (GAMLSS)× | Квантильна регресія× | |
|---|---|---|
| Галузь≠ | Статистика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2005 | 1978 |
| Автор методу≠ | Robert Rigby & Mikis Stasinopoulos | Koenker & Bassett |
| Тип≠ | Semi-parametric distributional regression model | Conditional quantile regression |
| Основоположне джерело≠ | Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C, 54(3), 507–554. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Інші назви≠ | Distributional Regression, Flexible Regression and Smoothing, GAMLSS Framework, Konum, Ölçek ve Şekil için Genelleştirilmiş Toplamlı Modeller | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Пов'язані≠ | 2 | 5 |
| Підсумок≠ | GAMLSS is a broad class of semi-parametric regression models introduced by Robert Rigby and Mikis Stasinopoulos in 2005. Unlike classical regression, which models only the mean of a response, GAMLSS allows each parameter of a chosen parametric distribution — location (e.g., mean), scale (e.g., variance), and shape (e.g., skewness, kurtosis) — to be modeled as an additive function of covariates. This makes it possible to capture heteroscedasticity, skewness, and heavy tails simultaneously within a single unified framework. | 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. |
| ScholarGateНабір даних ↗ |
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