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| 影響診断(Cook距離、DFFITS、レバレッジ)× | 中央絶対偏差 (MAD) 推定× | 分位点回帰× | |
|---|---|---|---|
| 分野≠ | 統計学 | 統計学 | 計量経済学 |
| 系統 | Regression model | Regression model | Regression model |
| 提唱年≠ | 1977 | 1974 | 1978 |
| 提唱者≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Hampel (influence-curve treatment); classical robust statistics | Koenker & Bassett |
| 種類≠ | Regression diagnostic | Robust scale estimator | Conditional quantile regression |
| 原典≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 別名≠ | Cook's distance, DFFITS, leverage, influential observation detection | median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 関連 | 5 | 5 | 5 |
| 概要≠ | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. | Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result. | 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. |
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