Robust data analysis in innovation project portfolio management

Boris Titarenko; Amir Hasnaoui; Roman Titarenko; Liliya Buzuk

doi:10.1051/matecconf/201817001017

Robust data analysis in innovation project portfolio management

Boris Titarenko^*, Amir Hasnaoui, Roman Titarenko, Liliya Buzuk

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

Abstract

The paper states the mathematical model of portfolio management that allows to create an effective portfolio of innovation projects. Within the framework of this model the robust approach to data analysis is applied and expanded for the tasks of regression analysis of project data. The approach of robust estimation of regression parameters based on the maximum likelihood method in case of arbitrary contamination is suggested. A number of heuristic algorithms for estimating regression parameters in the case of symmetric data contamination is reviewed and modified.

Original language	English
Article number	01017
Journal	MATEC Web of Conferences
Volume	170
DOIs	https://doi.org/10.1051/matecconf/201817001017
Publication status	Published - 13 Jun 2018
Externally published	Yes
Event	2017 International Science Conference on Business Technologies for Sustainable Urban Development, SPbWOSCE 2017 - St. Petersburg, Russian Federation Duration: 20 Dec 2017 → 22 Dec 2017

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10.1051/matecconf/201817001017

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title = "Robust data analysis in innovation project portfolio management",

abstract = "The paper states the mathematical model of portfolio management that allows to create an effective portfolio of innovation projects. Within the framework of this model the robust approach to data analysis is applied and expanded for the tasks of regression analysis of project data. The approach of robust estimation of regression parameters based on the maximum likelihood method in case of arbitrary contamination is suggested. A number of heuristic algorithms for estimating regression parameters in the case of symmetric data contamination is reviewed and modified.",

author = "Boris Titarenko and Amir Hasnaoui and Roman Titarenko and Liliya Buzuk",

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AU - Titarenko, Boris

AU - Hasnaoui, Amir

AU - Titarenko, Roman

AU - Buzuk, Liliya

N1 - Publisher Copyright: © The Authors, published by EDP Sciences, 2018.

PY - 2018/6/13

Y1 - 2018/6/13

N2 - The paper states the mathematical model of portfolio management that allows to create an effective portfolio of innovation projects. Within the framework of this model the robust approach to data analysis is applied and expanded for the tasks of regression analysis of project data. The approach of robust estimation of regression parameters based on the maximum likelihood method in case of arbitrary contamination is suggested. A number of heuristic algorithms for estimating regression parameters in the case of symmetric data contamination is reviewed and modified.

AB - The paper states the mathematical model of portfolio management that allows to create an effective portfolio of innovation projects. Within the framework of this model the robust approach to data analysis is applied and expanded for the tasks of regression analysis of project data. The approach of robust estimation of regression parameters based on the maximum likelihood method in case of arbitrary contamination is suggested. A number of heuristic algorithms for estimating regression parameters in the case of symmetric data contamination is reviewed and modified.

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U2 - 10.1051/matecconf/201817001017

DO - 10.1051/matecconf/201817001017

M3 - Conference article

AN - SCOPUS:85049424565

SN - 2261-236X

VL - 170

JO - MATEC Web of Conferences

JF - MATEC Web of Conferences

M1 - 01017

T2 - 2017 International Science Conference on Business Technologies for Sustainable Urban Development, SPbWOSCE 2017

Y2 - 20 December 2017 through 22 December 2017

ER -

Robust data analysis in innovation project portfolio management

Abstract

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