Data Research, Vol. 2, Issue 2, Apr  2018, Pages 54-64; DOI: 10.31058/j.data.2018.22001 10.31058/j.data.2018.22001

An Investigation of Bootstrap Methods in Parametric Estimations in Simple Linear Regression

Data Research, Vol. 2, Issue 2, Apr  2018, Pages 54-64.

DOI: 10.31058/j.data.2018.22001

Acha, Chigozie K 1* , Nwabueze, Joy C. 1

1 Department of Statistics, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Received: 5 June 2018; Accepted: 25 June 2018; Published: 17 July 2018

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Abstract

This study compares the bootstrap methods in parametric estimations in simple linear regression. To achieve this set objective theoretical datasets replicated from normal distribution with different ability levels were used. Simple linear regression (SLR) models were employed to fit the datasets and all the scores were considered.  Evidence showed that the original data set (O310Mt) without bootstrap resulted in a poor model with high bias. Results also showed that when the sample size was small ≤ 200, in almost all the different assessment conditions, the parametric bootstrap model (H311Mt) performed better than all of the parametric bootstrap models including (<em style=

Keywords

Parametric Bootstrap, Linear Regression, Estimations, information Criteria, Models

Copyright

© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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