Pages that link to "Item:Q2643271"

The following pages link to Nonparametric curve estimation with missing data: a general empirical process approach (Q2643271):

Displaying 11 items.

• Estimating the density of a possibly missing response variable in nonlinear regression (Q413378) (← links)
• Kernel regression estimation for incomplete data with applications (Q513697) (← links)
• On the \(L_p\) norms of kernel regression estimators for incomplete data with applications to classification (Q518885) (← links)
• The Glivenko-Cantelli theorem based on data with randomly imputed missing values (Q1612946) (← links)
• Smoothed empirical likelihood analysis of partially linear quantile regression models with missing response variables (Q1621250) (← links)
• Dealing with missing data based on data envelopment analysis and halo effect (Q1788728) (← links)
• Nonparametric regression estimation with missing data (Q1907648) (← links)
• Regression imputation in the functional linear model with missing values in the response (Q2317298) (← links)
• Nonparametric estimation of the mean function of a stochastic process with missing observa\-tions (Q2385623) (← links)
• Nonparametric Imputation by Data Depth (Q3304851) (← links)
• Nonparametric Regression With Predictors Missing at Random (Q5256422) (← links)