We investigate their breakdown point in mixture of linear regression models. It is expected that the robust S-estimators can achieve the high breakdown point in the contaminated data from the heterogenous populations. This model presents a unified, robust framework and parameter estimation is achieved...
Robust linear regression is less sensitive to outliers than standard linear regression. Standard linear regression uses ordinary least-squares fitting to compute the model parameters that relate the response data to the predictor data with one or more coefficients. (SeeEstimation of Multivariate Regressio...
SVD与主成分的关系:特征值越大,方差越大。 三、Robust regression鲁棒线性回归(Laplace/Student似然+均匀先验) 因为先验服从均匀分布,所以求鲁棒线性回归即求Laplace/Student最大似然。在heavy tail(奇异点较多)情况下用鲁棒线性回归,因为Laplace/Student分布比高斯分布更鲁棒。 似然函数为: 由于零点不可微,所以求解析解...
fromsklearn.linear_model import LinearRegression from scipy import stats from scipy.stats import median_abs_deviation def create_model_add_point(x_base, y_base, point=None): """扩展基础数据,添加一个可选点,并拟合线性模型。""" x, y = (np.append(x_base, point[0]), np.append(y_base,...
Müller, S., Welsh, A.: Outlier robust model selection in linear regression. Journal of the American Statistical Association 100(472), 1297–1310 (2005) MathSciNet MATHMüller, S., Welsh, A. H. (2005) Outlier robust model selection in linear regression. Journal of the American ...
"Linear Regression Model Selection Based On Robust Bootstrapping Technique". American Journal of Applied Sciences, 2009, 6(6), pp.1191-1198.Hassan S. U., Habshah M., Bashar A. T. and Jabar H. Y., 2009, " Linear Regression Model Selection Based on Robust Bootstraping Technique" ...
Estimates of Regression Parameters Based on Rank Tests In the linear regression model $Y_j = \\\alpha + \\\beta x_j + Z_j$, it is usual to estimate $\\\alpha$ and $\\\beta$ by the method of least squares. This method has, among other things, the nice property of providing...
(1967) was the first to describe this variance estimate; White’s estimator or HCCM (heteroskedasticity consistent covariance matrix) estimator because White (1980) independently showed that this variance estimate is consistent under a linear regression model including heteroskedasticity; or the sandwich ...
Quantile Regression is more powerful... D Ramdani,AV Witteloostuijn - University of Antwerp, Faculty of Applied Economics 被引量: 107发表: 2009年 On the Dual Formulation of Regularized Linear Systems with Convex Risks convex dualitylinear modellogistic regressionregulationsupport vector machineIn this...
import numpy as np import pandas as pd from sklearn.linear_model import LogisticRegression, LinearRegression def doubly_robust(df, X, T, Y): ps = LogisticRegression(C=1e6, max_iter=10000).fit(df[X], df[T]).predict_proba(df[X])[:, 1] mu0 = LinearRegression().fit(df.query(f"...