American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2019, 7(3), 105-111
DOI: 10.12691/ajams-7-3-4
Open AccessArticle

Choice of Appropriate Power Transformation of Skewed Distribution for Quantile Regression Model

Onyegbuchulem B.O.1, , Nwakuya M.T2, Nwabueze J.C3 and Otu Archibong Otu4

1Department of Maths/Statistics, Imo State Polytechnic Umuagwo, Nigeria

2Department of Maths/Statistics, University of Port Harcourt, River State, Nigeria

3Department of Statistics, Federal University of Agriculture Umudike, Nigeria

4Department of Research and Statistics, Central Bank of Nigeria, Owerri

Pub. Date: May 04, 2019

Cite this paper:
Onyegbuchulem B.O., Nwakuya M.T, Nwabueze J.C and Otu Archibong Otu. Choice of Appropriate Power Transformation of Skewed Distribution for Quantile Regression Model. American Journal of Applied Mathematics and Statistics. 2019; 7(3):105-111. doi: 10.12691/ajams-7-3-4


Quantile Regression (QR) performed better than Ordinary Least Square (OLS) when the Data is skewed. Its best result can be achieved when the Data is transformed. Quantreg package of R software was used to illustrate the various power transformation fitness for quantile regression model. The analysis shows that the best result was obtained from the square root of y transformation with an average error term of 0.9539, -0.0494, 0.0238, -0.5309 and -0.7544 for 10th, 25th, 50th, 75th and 90th quantile respectively. From the results obtained, it shows that model transformation can greatly improve the result of quantile regression model.

Quantile Regression skewed distribution power transformation and model selection

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