CALL FOR PAPERS

Author Information

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.

Multivariate normal sampling; an approach through principal component analysis

Author:

Bushra Shamshad and Junaid Sagheer Siddiqi

Subject Area:

Physical Sciences and Engineering

Abstract:

In this paper we discuss the use of Principal Component Analysis (PCA) for simulating random samples from multivariate normal distribution, using mean vector and covariance matrix. Sampling is an important aspect in the field of Statistics. We can generate random samples from various univariate distributions either discrete or continuous. We can also generate samples from bivariate distributions for that purpose there are different tables available. But, sampling in that manner is troublesome. In this article we use PCA; a multivariate technique for the purpose of sampling. Furthermore, various properties related to the multivariate normal data can be verified by simulating the samples.

PDF file: