A new resampling method is introduced to generate virtual data through a smoothing technique for replenishing small samples. The replenished analyzable sample retains the statistical properties of the original small sample, has small standard errors and possesses adequate statistical power. © 2010 JMASM, Inc
Resampling methods are a common measure to estimate the variance of a statistic of interest when dat...
Comparing time series is an important and common problem. Most results are asymptotic because it is ...
Computer- intensive resampling/bootstrap methods are feasible when calculating reference intervals f...
A new resampling method is introduced to generate virtual data through a smoothing technique for rep...
A new resampling method is introduced to generate virtual data through a smoothing technique for rep...
A new resampling method is introduced to generate virtual data through a smoothing technique for rep...
First, concepts of different types of resampling will be introduced with simple examples. Next,..sof...
Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjo...
Resampling (typically, but not necessarily, bootstrapping) is a well-known stochastic technique for ...
This article concentrates on one of the newer techniques, namely, resampling, and attempts to addres...
With the advent of high-speedpersonal computers, resamplingprocedures are now a realistic alternativ...
Resampling methods [1–4], also called Out-of-Sample methods, are favoured by practitioners beca...
This article discusses some resampling techniques that have found widespread application in survey ...
The determination of sample size before collecting experimental data is a key issue to obtain reliab...
The issues of estimation accuracy and statistical power in multiple regression with small samples ha...
Resampling methods are a common measure to estimate the variance of a statistic of interest when dat...
Comparing time series is an important and common problem. Most results are asymptotic because it is ...
Computer- intensive resampling/bootstrap methods are feasible when calculating reference intervals f...
A new resampling method is introduced to generate virtual data through a smoothing technique for rep...
A new resampling method is introduced to generate virtual data through a smoothing technique for rep...
A new resampling method is introduced to generate virtual data through a smoothing technique for rep...
First, concepts of different types of resampling will be introduced with simple examples. Next,..sof...
Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjo...
Resampling (typically, but not necessarily, bootstrapping) is a well-known stochastic technique for ...
This article concentrates on one of the newer techniques, namely, resampling, and attempts to addres...
With the advent of high-speedpersonal computers, resamplingprocedures are now a realistic alternativ...
Resampling methods [1–4], also called Out-of-Sample methods, are favoured by practitioners beca...
This article discusses some resampling techniques that have found widespread application in survey ...
The determination of sample size before collecting experimental data is a key issue to obtain reliab...
The issues of estimation accuracy and statistical power in multiple regression with small samples ha...
Resampling methods are a common measure to estimate the variance of a statistic of interest when dat...
Comparing time series is an important and common problem. Most results are asymptotic because it is ...
Computer- intensive resampling/bootstrap methods are feasible when calculating reference intervals f...
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