G-Brownian Motion as Rough Paths and Differential Equations Driven by G-Brownian Motion

The present paper is devoted to the study of sample paths of G-Brownian motion and stochastic differential equations (SDEs) driven by G-Brownian motion from the view of rough path theory. As the starting point, we show that quasi-surely, sample paths of G-Brownian motion can be enhanced to the second level in a canonical way so that they become geometric rough paths of roughness 2 < p < 3. This result enables us to introduce the notion of rough differential equations (RDEs) driven by G-Brownian motion in the pathwise sense under the general framework of rough paths. Next we establish the fundamental relation between SDEs and RDEs driven by G-Brownian motion. As an application, we introduce the notion of SDEs on a differentiable manifold driven by GBrownian motion and construct solutions from the RDE point of view by using pathwise localization technique. This is the starting point of introducing G-Brownian motion on a Riemannian manifold, based on the idea of Eells-Elworthy-Malliavin. The last part of this paper is devoted to such construction for a wide and interesting class of G-functions whose invariant group is the orthogonal group. We also develop the Euler-Maruyama approximation for SDEs driven by G-Brownian motion of independent interest

Quantum Brownian Motion on noncommutative manifolds: construction, deformation and exit times

We begin with a review and analytical construction of quantum Gaussian process (and quantum Brownian motions) in the sense of [25],[10] and others, and then formulate and study in details (with a number of interesting examples) a definition of quantum Brownian motions on those noncommutative manifolds (a la Connes) which are quantum homogeneous spaces of their quantum isometry groups in the sense of [11]. We prove that bi-invariant quantum Brownian motion can be 'deformed' in a suitable sense. Moreover, we propose a noncommutative analogue of the well-known asymptotics of the exit time of classical Brownian motion. We explicitly analyze such asymptotics for a specific example on noncommutative two-torus A{\theta}, which seems to behave like a one-dimensional manifold, perhaps reminiscent of the fact that A{\theta} is a noncommutative model of the (locally one-dimensional) 'leaf-space' of the Kronecker foliation

Inference on Riemannian Manifolds: Regression and Stochastic Differential Equations

Statistical inference for manifolds attracts much attention because of its power of working with more general forms of data or geometric objects. We study regression and stochastic differential equations on manifolds from the intrinsic point of view. Firstly, we are able to provide alternative parametrizations for data that lie on Lie group in the problem of fitting a regression model, by mapping this space intrinsically onto its Lie algebra, while we explore the behaviour of fitted values when this base point is chosen differently. Due to the nature of our data in the application of soft tissue artefacts, we employ two correlation structures, namely Matern and quasi-periodic correlation functions when using the generalized least squares, and show that some patterns of the residuals are removed. Secondly, we construct a generalization of the Ornstein-Uhlenbeck process on the cone of covariance matrices SP(n) endowed with two popular Riemannian metrics, namely Log-Euclidean (LE) and Affine-Invariant (AI) metrics. We show that the Riemannian Brownian motion on SP(n) has infinite explosion time as on the Euclidean space and establish the calculation for the horizontal lifts of smooth curves. Moreover, we provide Bayesian inference for discretely observed diffusion processes of covariance matrices associated with either the LE or the AI metrics, and present a novel diffusion bridge sampling method using guided proposals when equipping SP(n) with the AI metric. The estimation algorithms are illustrated with an application in finance, together with a goodness-of-fit test comparing models associated with different metrics. Furthermore, we explore the multivariate volatility models via simulation study, in which covariance matrices in the models are assumed to be unobservable

Developments in Random Matrix Theory

In this preface to the Journal of Physics A, Special Edition on Random Matrix Theory, we give a review of the main historical developments of random matrix theory. A short summary of the papers that appear in this special edition is also given.Comment: 22 pages, Late

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

Parabolic stable surfaces with constant mean curvature

We prove that if u is a bounded smooth function in the kernel of a nonnegative Schrodinger operator $-L=-(\Delta +q)$ on a parabolic Riemannian manifold M, then u is either identically zero or it has no zeros on M, and the linear space of such functions is 1-dimensional. We obtain consequences for orientable, complete stable surfaces with constant mean curvature $H\in\mathbb{R}$ in homogeneous spaces $\mathbb{E}(\kappa,\tau)$ with four dimensional isometry group. For instance, if M is an orientable, parabolic, complete immersed surface with constant mean curvature H in $\mathbb{H}^2\times\mathbb{R}$, then $|H|\leq 1/2$ and if equality holds, then M is either an entire graph or a vertical horocylinder.Comment: 15 pages, 1 figure. Minor changes have been incorporated (exchange finite capacity by parabolicity, and simplify the proof of Theorem 1)