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Nonparametric bootstrapping for multiple logistic regression model using R

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dc.contributor.author Hossain, Ahmed
dc.contributor.author Khan, H.T. Abdullah
dc.date.accessioned 2010-10-14T10:20:23Z
dc.date.available 2010-10-14T10:20:23Z
dc.date.issued 2004
dc.identifier.uri http://hdl.handle.net/10361/520
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.relation.ispartofseries BRAC University Journal, BRAC University;
dc.subject Nonparametric en_US
dc.subject Bootstrapping en_US
dc.subject Sampling en_US
dc.subject Logistic regression en_US
dc.subject Covariates en_US
dc.title Nonparametric bootstrapping for multiple logistic regression model using R en_US


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