The aim of bootstrapping is to approximate the sampling distribution of some estimator. An algorithm for combining method is given in SAS, along with applications and visualizations
Problem statement: Bootstrap approach had introduced new advancement in modeling and model evaluatio...
University of Minnesota Ph.D. dissertation. November 2010. Major: Statistics. Advisor: Dr. Yuhong Ya...
We present SAS code to implement the method proposed by Brunner et al. (1997) for performing two-way...
The aim of this study is to compare different robust regression methods in three main models of mult...
Research on modeling is becoming popular nowadays, there are several of analyses used in research f...
Response surface methodology (RSM) can be used when the response variable, y, is influenced by sever...
Multiple linear regressions (MLR) model is an important tool for investigating relationships between...
Multiple logistic regression is a methodology of handling dependent variables with a binary outcome....
(MLR) is the most common type of linear regression analysis. Current technology advancement and incr...
Four types of estimation approaches for prognostic survival oral cancer model building are considere...
This paper introduces a semi-parametric bootstrapping approach to Bayesian analysis of structural pa...
The bootstrap method of resampling can be useful in estimating the replicability of study results. T...
This paper provided an alternative method for exponential growth modeling as a regression analysis t...
We investigate bootstrap inference methods for nonlinear time series models obtained using Multivari...
The using of OLS method to estimate the regression coefficients in multiple linear regression model ...
Problem statement: Bootstrap approach had introduced new advancement in modeling and model evaluatio...
University of Minnesota Ph.D. dissertation. November 2010. Major: Statistics. Advisor: Dr. Yuhong Ya...
We present SAS code to implement the method proposed by Brunner et al. (1997) for performing two-way...
The aim of this study is to compare different robust regression methods in three main models of mult...
Research on modeling is becoming popular nowadays, there are several of analyses used in research f...
Response surface methodology (RSM) can be used when the response variable, y, is influenced by sever...
Multiple linear regressions (MLR) model is an important tool for investigating relationships between...
Multiple logistic regression is a methodology of handling dependent variables with a binary outcome....
(MLR) is the most common type of linear regression analysis. Current technology advancement and incr...
Four types of estimation approaches for prognostic survival oral cancer model building are considere...
This paper introduces a semi-parametric bootstrapping approach to Bayesian analysis of structural pa...
The bootstrap method of resampling can be useful in estimating the replicability of study results. T...
This paper provided an alternative method for exponential growth modeling as a regression analysis t...
We investigate bootstrap inference methods for nonlinear time series models obtained using Multivari...
The using of OLS method to estimate the regression coefficients in multiple linear regression model ...
Problem statement: Bootstrap approach had introduced new advancement in modeling and model evaluatio...
University of Minnesota Ph.D. dissertation. November 2010. Major: Statistics. Advisor: Dr. Yuhong Ya...
We present SAS code to implement the method proposed by Brunner et al. (1997) for performing two-way...