کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
486921 703534 2016 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bootstrapping with R to Make Generalized Inference for Regression Model
ترجمه فارسی عنوان
بوت استرپینگ با R به منظور استنتاج کلی برای مدل رگرسیون
کلمات کلیدی
بوت استرپینگ برای رگرسیون؛ استنتاج عمومی؛ اعتبارسنجی مدل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 86, 2016, Pages 228–231
نویسندگان
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