TY - JOUR

T1 - Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics

AU - Laa, Ursula

AU - Cook, Dianne

PY - 2020/9

Y1 - 2020/9

N2 - Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. Most indexes have been developed to detect departure from known distributions, such as normality, or to find separations between known groups. Here, we are interested in finding projections revealing potentially complex bivariate patterns, using new indexes constructed from scagnostics and a maximum information coefficient, with a purpose to detect unusual relationships between model parameters describing physics phenomena. The performance of these indexes is examined with respect to ideal behaviour, using simulated data, and then applied to problems from gravitational wave astronomy. The implementation builds upon the projection pursuit tools available in the R package, tourr, with indexes constructed from code in the R packages, binostics, minerva and mbgraphic.

AB - Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. Most indexes have been developed to detect departure from known distributions, such as normality, or to find separations between known groups. Here, we are interested in finding projections revealing potentially complex bivariate patterns, using new indexes constructed from scagnostics and a maximum information coefficient, with a purpose to detect unusual relationships between model parameters describing physics phenomena. The performance of these indexes is examined with respect to ideal behaviour, using simulated data, and then applied to problems from gravitational wave astronomy. The implementation builds upon the projection pursuit tools available in the R package, tourr, with indexes constructed from code in the R packages, binostics, minerva and mbgraphic.

KW - Data science

KW - Data visualisation

KW - Exploratory data analysis

KW - Guided tour

KW - Scagnostics

KW - Statistical graphics

UR - http://www.scopus.com/inward/record.url?scp=85078329633&partnerID=8YFLogxK

U2 - 10.1007/s00180-020-00954-8

DO - 10.1007/s00180-020-00954-8

M3 - Article

AN - SCOPUS:85078329633

VL - 35

SP - 1171

EP - 1205

JO - Computational Statistics

JF - Computational Statistics

SN - 0943-4062

IS - 3

ER -