Pages that link to "Item:Q4541308"

The following pages link to Identifying Regression Outliers and Mixtures Graphically (Q4541308):

Displaying 36 items.

• Robust estimation of the number of components for mixtures of linear regression models (Q333392) (← links)
• Identifying outlying observations in regression trees (Q338316) (← links)
• Isometric sliced inverse regression for nonlinear manifold learning (Q746295) (← links)
• A bootstrap method for assessing the dimension of a general regression problem (Q871006) (← links)
• Dimension reduction based on constrained canonical correlation and variable filtering (Q939658) (← links)
• Sufficient dimension reduction and variable selection for regression mean function with two types of predictors (Q952880) (← links)
• On the structure of the quadratic subspace in discriminant analysis (Q962217) (← links)
• Asymptotics for sliced average variance estimation (Q997370) (← links)
• On hybrid methods of inverse regression-based algorithms (Q1019889) (← links)
• Sliced mean variance-covariance inverse regression (Q1023521) (← links)
• Clusters, outliers, and regression: Fixed point clusters (Q1400150) (← links)
• The hybrid method of FSIR and FSAVE for functional effective dimension reduction (Q1663193) (← links)
• Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables (Q1728659) (← links)
• The effect of data contamination in sliced inverse regression and finite sample breakdown point (Q1744720) (← links)
• Sufficient dimension reduction in regressions with categorical predictors (Q1848946) (← links)
• Testing predictor contributions in sufficient dimension reduction. (Q1879930) (← links)
• A general theory for nonlinear sufficient dimension reduction: formulation and estimation (Q1952450) (← links)
• An ensemble of inverse moment estimators for sufficient dimension reduction (Q2242021) (← links)
• Using sliced mean variance-covariance inverse regression for classification and dimension reduction (Q2259752) (← links)
• Dimension reduction in functional regression with categorical predictor (Q2358936) (← links)
• On kernel method for sliced average variance estimation (Q2372139) (← links)
• Determining the dimension of iterative Hessian transformation (Q2388334) (← links)
• Dimension reduction via marginal high moments in regression (Q2489887) (← links)
• Dimension Reduction in Regressions through Weighted Variance Estimation (Q3015906) (← links)
• A note On outlier sensitivity of Sliced Inverse Regression (Q3153641) (← links)
• DETECTING INFLUENTIAL OBSERVATIONS IN SLICED INVERSE REGRESSION ANALYSIS (Q3429837) (← links)
• An Adaptive Estimation of Dimension Reduction Space (Q4665890) (← links)
• Dimension Reduction for the Conditional<i>k</i>th Moment in Regression (Q4670763) (← links)
• Dimension reduction via adaptive slicing (Q5037837) (← links)
• A METHOD OF LOCAL INFLUENCE ANALYSIS IN SUFFICIENT DIMENSION REDUCTION (Q5066770) (← links)
• (Q5290311) (← links)
• Sliced Average Variance Estimation for Censored Data (Q5495069) (← links)
• Sliced average variance estimation for multivariate time series (Q5742598) (← links)
• Computational Outlier Detection Methods in Sliced Inverse Regression (Q5870991) (← links)
• Stationary subspace analysis based on second-order statistics (Q6049301) (← links)
• Optimal projections for Gaussian discriminants (Q6106145) (← links)