Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Ushawishi (Umbali wa Cook, DFFITS, Leveraji)× | Regression Imara (Robust Regression)× | |
|---|---|---|
| Nyanja | Takwimu | Takwimu |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1977 | 1964 |
| Mwanzilishi≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Aina≠ | Regression diagnostic | Regression with outlier resistance |
| Chanzo asilia≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Majina mbadala≠ | Cook's distance, DFFITS, leverage, influential observation detection | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | 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. | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. |
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