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Comparisons between Resampling Techniques in Linear Regression: A Simulation Study
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2022 The classic methods used in estimating the parameters in linear regression need to fulfill some assumptions. If the assumptions are not fulfilled, the conclusion is questionable. Resampling is one of the ways to avoid such problems.
Anwar Fitrianto, Punitha Linganathan
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Bridge headwater afflux estimation using bootstrap resampling method [PDF]
Archives of Civil Engineering, 2023 The bridge structure’s development causes a riverbed cross-sections contraction. This influences the flow regime, being visible during catastrophic floods.
Marta Kiraga +2 more
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Re-sampling in Linear Regression Model Using Jackknife and Bootstrap [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 Statistical inference is based generally on some estimates that are functions of the data. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic.
Zakariya Y. Algamal, Khairy B. Rasheed
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Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration
JTAM (Jurnal Teori dan Aplikasi Matematika), 2022 Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant ...
Fandi Rezian Pratama Gultom +2 more
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Percentile Bootstrap Interval on Univariate Local Polynomial Regression Prediction
JTAM (Jurnal Teori dan Aplikasi Matematika), 2023 This study offers a new technique for constructing percentile bootstrap intervals to predict the regression of univariate local polynomials. Bootstrap regression uses resampling derived from paired and residual bootstrap methods.
Abil Mansyur +2 more
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Journal of Intelligent Systems, 2023 In recent years, there have been several calls by practitioners of machine learning to provide more guidelines on how to use its methods and techniques.
Nakatsu Robbie T.
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Remote Sensing, 2020 This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes
Bahareh Kalantar +5 more
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The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses. [PDF]
PLoS ONE, 2017 Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not ...
David I Warton +2 more
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Smoothed estimator of the periodic hazard function [PDF]
Opuscula Mathematica, 2009 A smoothed estimator of the periodic hazard function is considered and its asymptotic probability distribution and bootstrap simultaneous confidence intervals are derived.
Anna Dudek
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On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
Econometrics, 2015 This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling.
Antonio F. Galvao, Gabriel Montes-Rojas
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