Nonparametric bootstrapping for multiple logistic regression model using R
The use of explanatory variables or covariates in a regression model is an important way to represent heterogeneity in a population. Again bootstrapping is rapidly becoming a popular tool to apply in a broad range of standard applications including multiple regression. The nonparametric bootstrap al...
| Main Authors: | , |
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| שפה: | English |
| יצא לאור: |
BRAC University
2010
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| נושאים: | |
| גישה מקוונת: | http://hdl.handle.net/10361/520 |
| סיכום: | The use of explanatory variables or covariates in a regression model is an important way to represent heterogeneity in a population. Again bootstrapping is rapidly becoming a popular tool to apply in a broad range of standard applications including multiple regression. The nonparametric bootstrap allows us to estimate the sampling distribution of a statistic empirically without making assumptions about the form of the population, and without deriving the sampling distribution explicitly.
The main objective of this study to discuss the nonparametric bootstrapping procedure for
multiple logistic regression model associated with Davidson and Hinkley's (1997) “boot” library in R. |
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