Title: Application of Epanechnikov kernel smoothing technique in disability data

Authors: Jumi Kalita; Pranita Sarmah

Addresses: Department of Statistics, L.C. Bharali College, 781011 Guwahati, India ' Department of Statistics, Gauhati University, 781014 Guwahati, India

Abstract: Statistical data contains noise. Smoothing is used to smooth out these noises and present the data as a meaningful one. Kernel methods are nonparametric smoothing tools that can reveal structural features in the data which may not be possible with a parametric approach. This paper applies Epanechnikov kernel method of data smoothing to smooth out the dropout rates of the children with disabilities in the special educational institutions. The continuation probabilities and dropout rates of these children in the special educational institutions are indicators of effectiveness of such education systems. The dropout rates before and after smoothing are graphically presented. The distributions of the crude and smoothed rate are examined. It has been observed that under chi-squared test the smoothed data follows log logistic distribution while the crude data follows triangular distribution.

Keywords: dropout rates; continuation probability; Epanechnikov kernel smoothing; disability data; data smoothing; children with disabilities; special education; child disability.

DOI: 10.1504/IJISDC.2017.082874

International Journal of Intelligent Systems Design and Computing, 2017 Vol.1 No.1/2, pp.198 - 204

Received: 29 Apr 2016
Accepted: 27 Sep 2016
Published online: 14 Mar 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article