Bootstrapping with R to Make Generalized Inference for Regression Model
| Autor: | Siam Sae-tang, Khanokporn Donjdee, Prasong Kitidamrongsuk, Chukiat Viwatwongkasem, Chareena Ujeh, Jutatip Sillabutra |
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| Rok vydání: | 2016 |
| Předmět: |
Generalized linear model
Percentile Statistics::Theory Computer science Bootstrap aggregating Regression dilution information science Cross-sectional regression Logistic regression symbols.namesake Resampling Linear regression Statistics Statistical inference Statistics::Methodology natural sciences Poisson regression Segmented regression Statistic General Environmental Science Polynomial regression Regression analysis Confidence interval health care quality access and evaluation humanities Sampling distribution Generalized Inference symbols Model Validation General Earth and Planetary Sciences Bootstrapping for Regression Regression diagnostic Factor regression model Count data |
| Zdroj: | Procedia Computer Science. 86:228-231 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2016.05.103 |
| Popis: | Bootstrap is a resampling procedure drawn from an original sample data with replacement allocation method to build a sampling distribution of a statistic for statistical inference. This paper focuses to validate the generalized linear regression model by using the bootstrap method in order to make generalization of statistical inference to the different settings outside the original. The first application involved the bootstrap regression coefficients of predictors in the classical regression model while the others emphasized the bootstrap responses for binary outcomes in the logistic regression and for count data in the Poisson regression. The results on the bootstrap regression coefficients perform well even if the original data were restricted with small sample sizes and/or non-normal errors. The confidence intervals based upon the normal theory is quite narrower than the percentile interval and the bootstrap t interval. For the results of the bootstrap responses along a single predictor, both percentile confidence intervals of logistic and Poisson regression models perform well with a nice bandwidth of bootstrap responses for generalization. |
| Databáze: | OpenAIRE |
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