Skewness-Kurtosis Model-Based Projection Pursuit with Application to Summarizing Gene Expression Data

Jorge M. Arevalillo and Hilario Navarro
Additional contact information
Jorge M. Arevalillo: Department of Statistics and Operational Research, University Nacional Educación a Distancia (UNED), 28040 Madrid, Spain
Hilario Navarro: Department of Statistics and Operational Research, University Nacional Educación a Distancia (UNED), 28040 Madrid, Spain

Mathematics, 2021, vol. 9, issue 9, 1-18

Abstract: Non-normality is a usual fact when dealing with gene expression data. Thus, flexible models are needed in order to account for the underlying asymmetry and heavy tails of multivariate gene expression measures. This paper addresses the issue by exploring the projection pursuit problem under a flexible framework where the underlying model is assumed to follow a multivariate skew-t distribution. Under this assumption, projection pursuit with skewness and kurtosis indices is addressed as a natural approach for data reduction. The work examines its properties giving some theoretical insights and delving into the computational side in regards to the application to real gene expression data. The results of the theory are illustrated by means of a simulation study; the outputs of the simulation are used in combination with the theoretical insights to shed light on the usefulness of skewness-kurtosis projection pursuit for summarizing multivariate gene expression data. The application to gene expression measures of patients diagnosed with triple-negative breast cancer gives promising findings that may contribute to explain the heterogeneity of this type of tumors.

Keywords: skewness; kurtosis; skew-t distribution; projection pursuit; gene expression data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/9/954/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/9/954/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:9:p:954-:d:542693

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().