×

Modelling directional dispersion through hyperspherical log-splines. (English) Zbl 1095.62066

Summary: We introduce the directionally dispersed class of multivariate distributions, a generalization of the elliptical class. By allowing dispersion of multivariate random variables to vary with direction it is possible to generate a very wide and flexible class of distributions. Directionally dispersed distributions have a simple form for their density, which extends a spherically symmetric density function by including a function \(D\), modelling directional dispersion.
Under a mild condition, the class of distributions is shown to preserve both unimodality and moment existence. By adequately defining \(D\), it is possible to generate skewed distributions. Using spline models on hyperspheres, we suggest a very flexible, yet practical, implementation for modelling directional dispersion in any dimension. Finally, we use the new class of distributions in a Bayesian regression set-up and analyse the distributions of a set of biomedical measurements and a sample of US manufacturing firms.

MSC:

62H05 Characterization and structure theory for multivariate probability distributions; copulas
62F15 Bayesian inference
62H11 Directional data; spatial statistics
62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI

References:

[1] Abramowitz M., Handbook of Mathematical Functions: with Formulas, Graphs and Mathematical Tables (1972) · Zbl 0543.33001
[2] Arnold B. C., Am. Statistn 49 pp 34– (1995)
[3] Chib S., Am. Statistn 49 pp 327– (1995)
[4] Cook R. D., An Introduction to Regression Graphics (1994) · Zbl 0925.62287 · doi:10.1002/9780470316863
[5] DOI: 10.1093/biomet/88.4.1055 · Zbl 0986.62026 · doi:10.1093/biomet/88.4.1055
[6] Fang K. T., Symmetric Multivariate and Related Distributions (1990) · Zbl 0699.62048 · doi:10.1007/978-1-4899-2937-2
[7] Fernandez C., J. Am. Statist. Ass. 90 pp 1331– (1995)
[8] Ferreira J. T. A. S., Mimeo (2004)
[9] Ferreira J. T. A. S., Statistics Research Report 429 (2004)
[10] Genton M. G., Skew-elliptical Distributions and Their Applications: a Journey Beyond Normality (2004) · Zbl 1069.62045 · doi:10.1201/9780203492000
[11] Gilks W. R., Markov Chain Monte Carlo in Practice (1996) · Zbl 0832.00018 · doi:10.1007/978-1-4899-4485-6
[12] Green P. J., Biometrika 82 pp 711– (1995)
[13] Hall B. H., Am. Econ. Rev. 83 pp 259– (1993)
[14] Kass R. E., J. Am. Statist. Ass. 90 pp 773– (1995)
[15] Kelker D., Sankhya 32 pp 419– (1970)
[16] Muller C., Lect. Notes Math. 17 (1966) · Zbl 0138.05101 · doi:10.1007/BFb0094775
[17] Newton M. A., J. R. Statist. Soc. 56 pp 3– (1994)
[18] Osiewalski J., Biometrika 80 pp 456– (1993)
[19] DOI: 10.1137/0915068 · Zbl 0812.41009 · doi:10.1137/0915068
[20] Tierney L., Ann. Statist. 22 pp 1701– (1994)
[21] DOI: 10.1007/BF01437407 · Zbl 0299.65008 · doi:10.1007/BF01437407
[22] DOI: 10.1137/0902002 · Zbl 0537.65008 · doi:10.1137/0902002
[23] Wahba G., Spline Models for Observational Data (1990) · Zbl 0813.62001 · doi:10.1137/1.9781611970128
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.