CALL FOR PAPERS

CERTIFICATE

IMPACT FACTOR 2019

Subject Area

  • Life Sciences / Biology
  • Architecture / Building Management
  • Asian Studies
  • Business & Management
  • Chemistry
  • Computer Science
  • Economics & Finance
  • Engineering / Acoustics
  • Environmental Science
  • Agricultural Sciences
  • Pharmaceutical Sciences
  • General Sciences
  • Materials Science
  • Mathematics
  • Medicine
  • Nanotechnology & Nanoscience
  • Nonlinear Science
  • Chaos & Dynamical Systems
  • Physics
  • Social Sciences & Humanities

Why Us? >>

  • Open Access
  • Peer Reviewed
  • Rapid Publication
  • Life time hosting
  • Free promotion service
  • Free indexing service
  • More citations
  • Search engine friendly

Plagiarism Detection

IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies.

 

 

 

 

 

 

 

 

 

 

 

 

Evaluation of classical and robust discriminant methods under apparent error rate

Author: 
Muthukrishnan, R. and Mahesh, K.
Subject Area: 
Physical Sciences and Engineering
Abstract: 

All classical statistical methods rely explicitly or implicitly on parametric models based on number of assumptions. The most widely used assumption is that the observed data have normal distribution. This assumption about the structural and the stochastic parts of the model have been present in statistics for two centuries, and have been the framework for all the classical methods. Classical methods perform well if the data obeys the assumptions. Now-a-days data collected and stored at enormous speed (GB/TB/hr) and pressure to provide better customized service for an edge. The data does not follow the so-called assumptions then the result using classical methods get affected. In this context traditional techniques are infeasible due to enormity of data, high dimensionality of data and heterogeneous of data. The robust methods can be seen as extensions to the classical ones which can cope with deviations from the stochastic assumptions. Classification and data reduction techniques play an important role while handling large data. A reliable and precise classification aspect is essential in analyzing multivariate data. This paper presents the evaluation aspects such as apparent error rate of various classical and robust discriminant methods on a simulation study using R package.

PDF file: 

IJMCE RECOMMENDATION

ONLINE PAYPAL PAYMENT

CURRENT ISSUE

NEWS

CHIEF EDITOR
Rosane Cavalcante Fragoso, Brasil
ASSOCIATE CHIEF EDITOR

   

Jean-Marc SABATIER
Chief Scientific Officer and Head of a Research Group
France

Advantages of IJCR

  • Rapid Publishing
  • Professional publishing practices
  • Indexing in leading database
  • High level of citation
  • High Qualitiy reader base
  • High level author suport

 

 

 

 

 

 

 

 

 

 

 

EDITORIAL BOARD

Luai Farhan Zghair
Iraq
Hasan Ali Abed Al-Zu’bi
Jordanian
Fredrick OJIJA
Tanzanian
Firuza M. Tursunkhodjaeva
Uzbekistan
Faraz Ahmed Farooqi
Saudi Arabia
Eric Randy Reyes Politud
Philippines
Elsadig Gasoom FadelAlla Elbashir
Sudan
Eapen, Asha Sarah
United State
Dr.Arun Kumar A
India
Dr. Zafar Iqbal
Pakistan
Dr. SHAHERA S.PATEL
India
Dr. Ruchika Khanna
India
Dr. Recep TAS
Turkey
Dr. Rasha Ali Eldeeb
Egypt
Dr. Pralhad Kanhaiyalal Rahangdale
India
DR. PATRICK D. CERNA
Philippines
Dr. Nicolas Padilla- Raygoza
Mexico
Dr. Mustafa Y. G. Younis
Libiya
Dr. Muhammad shoaib Ahmedani
Saudi Arabia
DR. MUHAMMAD ISMAIL MOHMAND
United State
DR. MAHESH SHIVAJI CHAVAN
India
DR. M. ARUNA
India
Dr. Lim Gee Nee
Malaysia
Dr. Jatinder Pal Singh Chawla
India
DR. IRAM BOKHARI
Pakistan
Dr. FARHAT NAZ RAHMAN
Pakistan
Dr. Devendra kumar Gupta
India
Dr. ASHWANI KUMAR DUBEY
India
Dr. Ali Seidi
Iran
Dr. Achmad Choerudin
Indonesia
Dr Ashok Kumar Verma
India
Thi Mong Diep NGUYEN
France
Dr. Muhammad Akram
Pakistan
Dr. Imran Azad
Oman
Dr. Meenakshi Malik
India
Aseel Hadi Hamzah
Iraq
Anam Bhatti
Malaysia
Md. Amir Hossain
Bangladesh
Ahmet İPEKÇİ
Turkey
Mirzadi Gohari
Iran