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...
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| Язык: | English |
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BRAC University
2010
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| Online-ссылка: | http://hdl.handle.net/10361/520 |
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10361-520 |
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10361-5202019-09-29T05:45:59Z Nonparametric bootstrapping for multiple logistic regression model using R Hossain, Ahmed Khan, H.T. Abdullah Nonparametric Bootstrapping Sampling Logistic regression Covariates 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. 2010-10-14T10:20:23Z 2010-10-14T10:20:23Z 2004 http://hdl.handle.net/10361/520 en BRAC University Journal, BRAC University; application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Nonparametric Bootstrapping Sampling Logistic regression Covariates |
| spellingShingle |
Nonparametric Bootstrapping Sampling Logistic regression Covariates Hossain, Ahmed Khan, H.T. Abdullah Nonparametric bootstrapping for multiple logistic regression model using R |
| description |
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. |
| author |
Hossain, Ahmed Khan, H.T. Abdullah |
| author_facet |
Hossain, Ahmed Khan, H.T. Abdullah |
| author_sort |
Hossain, Ahmed |
| title |
Nonparametric bootstrapping for multiple logistic regression model using R |
| title_short |
Nonparametric bootstrapping for multiple logistic regression model using R |
| title_full |
Nonparametric bootstrapping for multiple logistic regression model using R |
| title_fullStr |
Nonparametric bootstrapping for multiple logistic regression model using R |
| title_full_unstemmed |
Nonparametric bootstrapping for multiple logistic regression model using R |
| title_sort |
nonparametric bootstrapping for multiple logistic regression model using r |
| publisher |
BRAC University |
| publishDate |
2010 |
| url |
http://hdl.handle.net/10361/520 |
| work_keys_str_mv |
AT hossainahmed nonparametricbootstrappingformultiplelogisticregressionmodelusingr AT khanhtabdullah nonparametricbootstrappingformultiplelogisticregressionmodelusingr |
| _version_ |
1814306901328920576 |