RCCM: Retained Component Criterion for the Moving Dynamic Principal Component Analysis

Fayed Awdah M Alshammri

RCCM: Retained Component Criterion for the Moving Dynamic Principal Component Analysis

Fayed Awdah M Alshammri

Mathematics And Statistics

Research output: Other contribution

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Abstract

The RCC_MDPCA criterion is a new tool to determine the optimal number of components (i.e. MDPCs) to retain for the Moving Dynamic Principal Component Analysis (MDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of MDPCA; See Alshammri and Pan (2019). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(MDPCA)

Original language	English
Type	Developing R Package
Media of output	R
Publisher	University of Strathclyde
Number of pages	1
Place of Publication	Glasgow
Publication status	Published - 10 Mar 2020

Keywords

RCCM

Access to Document

Alshammri-2020-RCCM-Retained-Component-Criterion-for-the-Moving-Dynamic-PrincipalFinal published version, 84.8 KBLicence: GNU GPL 2

Cite this

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title = "RCCM: Retained Component Criterion for the Moving Dynamic Principal Component Analysis",

abstract = "The RCC_MDPCA criterion is a new tool to determine the optimal number of components (i.e. MDPCs) to retain for the Moving Dynamic Principal Component Analysis (MDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of MDPCA; See Alshammri and Pan (2019). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(MDPCA)",

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N2 - The RCC_MDPCA criterion is a new tool to determine the optimal number of components (i.e. MDPCs) to retain for the Moving Dynamic Principal Component Analysis (MDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of MDPCA; See Alshammri and Pan (2019). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(MDPCA)

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