Add to ORKGThe issues of estimation accuracy and statistical power in multiple regression with small samples have long been a concern. The present study utilizes a new multivariate resampling method, the kernel resampling technique (KRT), to improve the estimation accuracy and statistical power in multiple regression with small samples. KRT is a distribution-free method that employs kernel technique to create multivariate resamples based on a given small sample. The findings from both a simulation study and an empirical example suggest that the statistical performance of multiple regression has been improved through KRT. © 2010 Nova Science Publishers, Inc
The multivariate kernel density estimator (MKDE) for the analysis of data in more than one dimension...
Simulation is widely used to predict the performance of complex systems. The main drawback of simula...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
The issues of estimation accuracy and statistical power in multiple regression with small samples ha...
This study presents a new multivariate resampling method to improve the performance of multiple regr...
The kernel regularized least squares (KRLS) method uses the kernel trick to perform non-linear regre...
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. Th...
When unobserved confounders can be ruled out, investigators often use matching and reweighting techn...
A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regres...
Multistage sampling is a common sampling technique in many studies. A challenge presented by multist...
Multistage sampling is a common sampling technique in many studies. A challenge presented by multist...
In regression analysis, existence of multicollinearity (collinearity) on given data, say X, can seri...
Kernel density estimation (KDE) is the most widely-used practical method for accurate nonparametric ...
Abstract Data modeling requires a sufficient sample size for reproducibility. A small sample size ca...
Simulation is widely used to predict the performance of complex systems. The main drawback of simula...
The multivariate kernel density estimator (MKDE) for the analysis of data in more than one dimension...
Simulation is widely used to predict the performance of complex systems. The main drawback of simula...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
The issues of estimation accuracy and statistical power in multiple regression with small samples ha...
This study presents a new multivariate resampling method to improve the performance of multiple regr...
The kernel regularized least squares (KRLS) method uses the kernel trick to perform non-linear regre...
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. Th...
When unobserved confounders can be ruled out, investigators often use matching and reweighting techn...
A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regres...
Multistage sampling is a common sampling technique in many studies. A challenge presented by multist...
Multistage sampling is a common sampling technique in many studies. A challenge presented by multist...
In regression analysis, existence of multicollinearity (collinearity) on given data, say X, can seri...
Kernel density estimation (KDE) is the most widely-used practical method for accurate nonparametric ...
Abstract Data modeling requires a sufficient sample size for reproducibility. A small sample size ca...
Simulation is widely used to predict the performance of complex systems. The main drawback of simula...
The multivariate kernel density estimator (MKDE) for the analysis of data in more than one dimension...
Simulation is widely used to predict the performance of complex systems. The main drawback of simula...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...