Modification of Tukey’s smoothing techniques for extreme data

Two Tukey’s techniques which are resistant line for linear trend and resistant smoothing for non linear trend have been reviewed in this research. The new resistant line for method of dividing the batches using range dealing with ties and non ties is recommended. The determination of sample size...

Full description

Saved in:
Bibliographic Details
Main Author: Husain, Qasim Nasir
Format: Thesis
Language:English
Published: 2017
Subjects:
Online Access:http://psasir.upm.edu.my/id/eprint/67711/1/FS%202017%2084%20%20IR.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-upm-ir.67711
record_format uketd_dc
spelling my-upm-ir.677112019-03-26T08:15:39Z Modification of Tukey’s smoothing techniques for extreme data 2017-08 Husain, Qasim Nasir Two Tukey’s techniques which are resistant line for linear trend and resistant smoothing for non linear trend have been reviewed in this research. The new resistant line for method of dividing the batches using range dealing with ties and non ties is recommended. The determination of sample size in each batch is also being introduced. In resistant smoothing, mathematical terms have been initiated and incorporated in this technique, the part which has been neglected by introducer of exploratory data analysis. New symmetric mean, right mean, left mean, right median, and left median have been proposed, leading to more simple process of smoothing technique. Later, the proposed methods have been used for the suggested compound smoothing techniques and hannings with simpler, faster and better smooth. Additionally, in order to evaluate the efficiency of variant proposed techniques together with the smoothing index and extra balance test, simulation data with big data size have successfully been applied. Mathematical models Quantitative research Symmetric spaces 2017-08 Thesis http://psasir.upm.edu.my/id/eprint/67711/ http://psasir.upm.edu.my/id/eprint/67711/1/FS%202017%2084%20%20IR.pdf text en public doctoral Universiti Putra Malaysia Mathematical models Quantitative research Symmetric spaces
institution Universiti Putra Malaysia
collection PSAS Institutional Repository
language English
topic Mathematical models
Quantitative research
Symmetric spaces
spellingShingle Mathematical models
Quantitative research
Symmetric spaces
Husain, Qasim Nasir
Modification of Tukey’s smoothing techniques for extreme data
description Two Tukey’s techniques which are resistant line for linear trend and resistant smoothing for non linear trend have been reviewed in this research. The new resistant line for method of dividing the batches using range dealing with ties and non ties is recommended. The determination of sample size in each batch is also being introduced. In resistant smoothing, mathematical terms have been initiated and incorporated in this technique, the part which has been neglected by introducer of exploratory data analysis. New symmetric mean, right mean, left mean, right median, and left median have been proposed, leading to more simple process of smoothing technique. Later, the proposed methods have been used for the suggested compound smoothing techniques and hannings with simpler, faster and better smooth. Additionally, in order to evaluate the efficiency of variant proposed techniques together with the smoothing index and extra balance test, simulation data with big data size have successfully been applied.
format Thesis
qualification_level Doctorate
author Husain, Qasim Nasir
author_facet Husain, Qasim Nasir
author_sort Husain, Qasim Nasir
title Modification of Tukey’s smoothing techniques for extreme data
title_short Modification of Tukey’s smoothing techniques for extreme data
title_full Modification of Tukey’s smoothing techniques for extreme data
title_fullStr Modification of Tukey’s smoothing techniques for extreme data
title_full_unstemmed Modification of Tukey’s smoothing techniques for extreme data
title_sort modification of tukey’s smoothing techniques for extreme data
granting_institution Universiti Putra Malaysia
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/67711/1/FS%202017%2084%20%20IR.pdf
_version_ 1747812506511867904