Bootstrapping with R to Make Generalized Inference for Regression Model Article Swipe
Jutatip Sillabutra
,
Prasong Kitidamrongsuk
,
Chukiat Viwatwongkasem
,
Chareena Ujeh
,
Siam Sae-tang
,
Khanokporn Donjdee
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2016.05.103
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2016.05.103
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.
Related Topics
Concepts
Statistics
Confidence interval
Bootstrapping (finance)
Logistic regression
Computer science
Regression diagnostic
Resampling
Percentile
Inference
Poisson regression
Regression analysis
Sampling distribution
Mathematics
Econometrics
Polynomial regression
Artificial intelligence
Population
Sociology
Demography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2016.05.103
- OA Status
- diamond
- Cited By
- 6
- References
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2404296827
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2404296827Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2016.05.103Digital Object Identifier
- Title
-
Bootstrapping with R to Make Generalized Inference for Regression ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Jutatip Sillabutra, Prasong Kitidamrongsuk, Chukiat Viwatwongkasem, Chareena Ujeh, Siam Sae-tang, Khanokporn DonjdeeList of authors in order
- Landing page
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https://doi.org/10.1016/j.procs.2016.05.103Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2016.05.103Direct OA link when available
- Concepts
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Statistics, Confidence interval, Bootstrapping (finance), Logistic regression, Computer science, Regression diagnostic, Resampling, Percentile, Inference, Poisson regression, Regression analysis, Sampling distribution, Mathematics, Econometrics, Polynomial regression, Artificial intelligence, Population, Sociology, DemographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2022: 4Per-year citation counts (last 5 years)
- References (count)
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3Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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