Nonparametric bootstrapping for multiple logistic regression model using R

Hossain, Ahmed and Khan, Hafiz (2004) Nonparametric bootstrapping for multiple logistic regression model using R. BRAC University Journal, 1 (2). pp. 109-113.

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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 regressions. 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.

Item Type: Article
Uncontrolled Keywords: Nonparametric; Bootstrapping; Sampling; Logistic regression; Covariates
Subjects: Computing
Social sciences
Depositing User: Hafiz Khan
Date Deposited: 02 Feb 2010 12:44
Last Modified: 17 Aug 2017 14:55
URI: http://repository.uwl.ac.uk/id/eprint/3783

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