Parametric bootstrapping in a generalized extreme value regression model for binary response: Application in health study
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JSMTA-2-2_003
تاریخ نمایه سازی: 19 اردیبهشت 1401
Abstract:
Generalized extreme value regression is often more adapted when we investigate a relationship between a binary response variable that represents a rare event and potential predictors. In particular, we use the quantile function of the generalized extreme value distribution as the link function. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, hypotheses testing) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Bootstrapping estimates the properties of an estimator by measuring those properties when sampling from an approximating distribution. In this paper, we fit the generalized extreme value regression model and perform a parametric bootstrap method for testing hypotheses and confidence interval estimation of parameters for the generalized extreme value regression model with a real data application.
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Authors
Aba Diop
Département de Mathématiques, Université Alioune Diop de Bambey, Sénégal
Elhadji Deme
Département de Mathématiques, Université Gaston Berger de Saint-Louis, Sénégal