Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Constructing Reference Metrics on Multicube Representations of Arbitrary Manifolds

Reference metrics are used to define the differential structure on multicube representations of manifolds, i.e., they provide a simple and practical way to define what it means globally for tensor fields and their derivatives to be continuous. This paper introduces a general procedure for constructing reference metrics automatically on multicube representations of manifolds with arbitrary topologies. The method is tested here by constructing reference metrics for compact, orientable two-dimensional manifolds with genera between zero and five. These metrics are shown to satisfy the Gauss-Bonnet identity numerically to the level of truncation error (which converges toward zero as the numerical resolution is increased). These reference metrics can be made smoother and more uniform by evolving them with Ricci flow. This smoothing procedure is tested on the two-dimensional reference metrics constructed here. These smoothing evolutions (using volume-normalized Ricci flow with DeTurck gauge fixing) are all shown to produce reference metrics with constant scalar curvatures (at the level of numerical truncation error).Comment: 37 pages, 16 figures; additional introductory material added in version accepted for publicatio

A High-Order Kernel Method for Diffusion and Reaction-Diffusion Equations on Surfaces

In this paper we present a high-order kernel method for numerically solving diffusion and reaction-diffusion partial differential equations (PDEs) on smooth, closed surfaces embedded in $\mathbb{R}^d$. For two-dimensional surfaces embedded in $\mathbb{R}^3$, these types of problems have received growing interest in biology, chemistry, and computer graphics to model such things as diffusion of chemicals on biological cells or membranes, pattern formations in biology, nonlinear chemical oscillators in excitable media, and texture mappings. Our kernel method is based on radial basis functions (RBFs) and uses a semi-discrete approach (or the method-of-lines) in which the surface derivative operators that appear in the PDEs are approximated using collocation. The method only requires nodes at "scattered" locations on the surface and the corresponding normal vectors to the surface. Additionally, it does not rely on any surface-based metrics and avoids any intrinsic coordinate systems, and thus does not suffer from any coordinate distortions or singularities. We provide error estimates for the kernel-based approximate surface derivative operators and numerically study the accuracy and stability of the method. Applications to different non-linear systems of PDEs that arise in biology and chemistry are also presented

Three-dimensional Analytical Description of Magnetised Winds from Oblique Pulsars

Rotating neutron stars, or pulsars, are plausibly the source of power behind many astrophysical systems, such as gamma-ray bursts, supernovae, pulsar wind nebulae and supernova remnants. In the past several years, 3D numerical simulations made it possible to compute pulsar spindown luminosity from first principles and revealed that oblique pulsar winds are more powerful than aligned ones. However, what causes this enhanced power output of oblique pulsars is not understood. In this work, using time-dependent 3D magnetohydrodynamic (MHD) and force-free simulations, we show that, contrary to the standard paradigm, the open magnetic flux, which carries the energy away from the pulsar, is laterally non-uniform. We argue that this non-uniformity is the primary reason for the increased luminosity of oblique pulsars. To demonstrate this, we construct simple analytic descriptions of aligned and orthogonal pulsar winds and combine them to obtain an accurate 3D description of the pulsar wind for any obliquity. Our approach describes both the warped magnetospheric current sheet and the smooth variation of pulsar wind properties outside of it. We find that generically the magnetospheric current sheet separates plasmas that move at mildly relativistic velocities relative to each other. This suggests that the magnetospheric reconnection is a type of driven, rather than free, reconnection. The jump in magnetic field components across the current sheet decreases with increasing obliquity, which could be a mechanism that reduces dissipation in near-orthogonal pulsars. Our analytical description of the pulsar wind can be used for constructing models of pulsar gamma-ray emission, pulsar wind nebulae, and magnetar-powered core-collapse gamma-ray bursts and supernovae.Comment: Submitted to MNRAS, comments welcome. 12 pages, 16 figures, uses mn2e.cl

Testing uniformity on high-dimensional spheres against monotone rotationally symmetric alternatives

We consider the problem of testing uniformity on high-dimensional unit spheres. We are primarily interested in non-null issues. We show that rotationally symmetric alternatives lead to two Local Asymptotic Normality (LAN) structures. The first one is for fixed modal location $\theta$ and allows to derive locally asymptotically most powerful tests under specified $\theta$. The second one, that addresses the Fisher-von Mises-Langevin (FvML) case, relates to the unspecified-$\theta$ problem and shows that the high-dimensional Rayleigh test is locally asymptotically most powerful invariant. Under mild assumptions, we derive the asymptotic non-null distribution of this test, which allows to extend away from the FvML case the asymptotic powers obtained there from Le Cam's third lemma. Throughout, we allow the dimension $p$ to go to infinity in an arbitrary way as a function of the sample size $n$. Some of our results also strengthen the local optimality properties of the Rayleigh test in low dimensions. We perform a Monte Carlo study to illustrate our asymptotic results. Finally, we treat an application related to testing for sphericity in high dimensions

Inference Under Convex Cone Alternatives for Correlated Data

In this research, inferential theory for hypothesis testing under general convex cone alternatives for correlated data is developed. While there exists extensive theory for hypothesis testing under smooth cone alternatives with independent observations, extension to correlated data under general convex cone alternatives remains an open problem. This long-pending problem is addressed by (1) establishing that a "generalized quasi-score" statistic is asymptotically equivalent to the squared length of the projection of the standard Gaussian vector onto the convex cone and (2) showing that the asymptotic null distribution of the test statistic is a weighted chi-squared distribution, where the weights are "mixed volumes" of the convex cone and its polar cone. Explicit expressions for these weights are derived using the volume-of-tube formula around a convex manifold in the unit sphere. Furthermore, an asymptotic lower bound is constructed for the power of the generalized quasi-score test under a sequence of local alternatives in the convex cone. Applications to testing under order restricted alternatives for correlated data are illustrated.Comment: 31 page

Quantization of generic chaotic 3D billiard with smooth boundary I: energy level statistic

Numerical calculation and analysis of extremely high-lying energy spectra, containing thousands of levels with sequential quantum number up to 62,000 per symmetry class, of a generic chaotic 3D quantum billiard is reported. The shape of the billiard is given by a simple and smooth de formation of a unit sphere which gives rise to (almost) fully chaotic classical dynamics. We present an analysis of (i) quantum length spectrum whose smooth part agrees with the 3D Weyl formula and whose oscillatory part is peaked around the periods of classical periodic orbits, (ii) nearest neighbor level spacing distribution and (iii) number variance. Although the chaotic classical dynamics quickly and uniformly explores almost entire energy shell, while the measure of the regular part of phase space is insignificantly small, we find small but significant deviations from GOE statistics which are explained in terms of localization of eigenfunctions onto lower dimensional classically invariant manifolds.Comment: 10 pages in plain Latex (6 figures in PCL format available upon request) submitted to Phys. Lett.