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    • Volume 01, Number 02, 2004
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    Nonparametric bootstrapping for multiple logistic regression model using R

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    Vol 1 No 2.11.pdf (80.56Kb)
    Date
    2004
    Publisher
    BRAC University
    Author
    Hossain, Ahmed
    Khan, H.T. Abdullah
    Metadata
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    URI
    http://hdl.handle.net/10361/520
    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.
    Keywords
    Nonparametric; Bootstrapping; Sampling; Logistic regression; Covariates
     
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