Discrete chaotic calculus and covariance identities

We show that for the binomial process (or Bernoulli random walk) the orthogonal functionals constructed in Kroeker, J.P. (1980) "Wiener analysis of functionals of a Markov chain: application to neural transformations of random signals", Biol. Cybernetics 36 , 243-248, [14] for Markov chains can be expressed using the Krawtchouk polynomials, and by iterated stochastic integrals. This allows to construct a chaotic calculus based on gradient and divergence operators and structure equations, and to establish a Clark representation formula. As an application we obtain simple infinite dimensional proofs of covariance identities on the discrete cube.     

Tapered Covariance: Bayesian Estimation and Asymptotics

The method of maximum tapered likelihood has been proposed as a way to quickly estimate covariance parameters for stationary Gaussian random fields. We show that under a useful asymptotic regime, maximum tapered likelihood estimators are consistent and asymptotically normal for covariance models in common use. We then formalize the notion of tapered quasi-Bayesian estimators and show that they too are consistent and asymptotically normal. We also present asymptotic confidence intervals for both types of estimators and show via simulation that they accurately reflect sampling variability, even at modest sample sizes. Proofs, an example, and detailed derivations are provided in the supplementary materials, available online.     

Fast Kalman Filtering and Forward–Backward Smoothing via a Low-Rank Perturbative Approach

Kalman filtering-smoothing is a fundamental tool in statistical time-series analysis. However, standard implementations of the Kalman filter-smoother require O(d³) time and O(d²) space per time step, where d is the dimension of the state variable, and are therefore impractical in high-dimensional problems. In this article we note that if a relatively small number of observations are available per time step, the Kalman equations may be approximated in terms of a low-rank perturbation of the prior state covariance matrix in the absence of any observations. In many cases this approximation may be computed and updated very efficiently (often in just O(k²d) or O(k²d + kdlog?d) time and space per time step, where k is the rank of the perturbation and in general k ? d), using fast methods from numerical linear algebra. We justify our approach and give bounds on the rank of the perturbation as a function of the desired accuracy. For the case of smoothing, we also quantify the error of our algorithm because of the low-rank approximation and show that it can be made arbitrarily low at the expense of a moderate computational cost. We describe applications involving smoothing of spatiotemporal neuroscience data. This article has online supplementary material.     

量测噪声有限记忆在线估计简化算法

量测噪声有限记忆在线估计方法通过对新息序列的实时统计计算,更新系统量测噪声阵 R,增强了滤波器的自适应能力。但量测噪声有限记忆在线估计方法需要在每个滤波周期内对量测噪声阵 R 进行估计并更新统计周期内的量测新息,存在着信息统计与数据更新计算量大的不足。针对此问题,提出了一种基于协方差匹配技术的自适应滤波算法,将协方差匹配技术与量测噪声有限记忆在线估计方法相结合,根据协方差匹配结果,选择性统计量测噪声阵 R。仿真结果表明,简化算法可以在保证滤波精度相当的前提下,减小计算量,提高实时性。     

Pre-Envelope Covariance Differential Equations For White and Nonwhite Input Processes

The pre-envelope process is a complex process whose statistics are strictly related to the statistics of the envelope of a given process. The paper deals with the evaluation of the covariances of the pre-envelope output process of classically and nonclassically damped linear systems subjected to stationary and nonstationary white and nonwhite pre-envelope input process. More precisely, the pre-envelope covariances for nonwhite complex input processes are evaluated as solution of a set of first order differential equations. Furthermore, in the paper the pre-envelope of the white input process is defined, and for such input the pre-envelope covariance differential equations are determined by means of an extension to the complex field of the stochastic differential calculus. Sommario.Il processo 'pre-inviluppo' é un processo complesso i cui parametri statistici sono strettamente legati a quelli del processo 'inviluppo'. Il lavoro riguarda la valutazione delle covarianze del processo pre–inviluppo della risposta di sistemi dinamici classicamente e non classicamente smorzati soggetti a processi pre–inviluppo bianchi e filtrati, stazionari e non stazionari. Piprecisamente, nel caso di un processo filtrato pre–inviluppo le covarianze della risposta sono valutate come soluzione di un sistema di equazioni differenziali del primo ordine. Inoltre, nel lavoro definito il processo pre–inviluppo di un processo bianco e per tale caso sono presentate le equazioni differenziali delle covarianze della risposta ottenute attraverso l'estensione al campo complesso del classico calcolo differenziale stocastico.     

Equivalence of kinetic-theory and random-matrix approaches to Lyapunov spectra of hard-sphere systems

In the study of chaotic behaviour of systems of many hard spheres, Lyapunov exponents of small absolute values exhibit interesting characteristics leading to speculations about connections to non-equilibrium statistical mechanics. Analytical approaches to these exponents so far can be divided into two groups, macroscopically oriented approaches, using kinetic theory or hydrodynamics, and more microscopically oriented random-matrix approaches in quasi-one-dimensional systems. In this paper, I present an approach using random matrices and weak-disorder expansion in an arbitrary number of dimensions. Correlations between subsequent collisions of a particle are taken into account. It is shown that the results are identical to those of a previous approach based on an extended Enskog equation. I conclude that each approach has its merits, and provides different insights into the approximations made, which include the Stoßzahlansatz, the continuum limit, and the long wavelength approximation. The comparison also gives insight into possible connections between Lyapunov exponents and fluctuations.     

二次型在经济管理中的应用简介

本文是二次型及其定性理论在经济管理中的一些应用的归纳总结,包括多元函数极值存在的充分条件、线性回归模型中参数估计的最小二乘法、随机向量及其线性函数的数字特征、线性组合预测模型的误差平方和以及线性组合投资收益率的方差（风险）等。     

Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso

The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time-series. Traditionally, graphical models are estimated under the assumption that data are drawn identically from a generating distribution. Introducing sparsity and sparse-difference inducing priors, we relax these assumptions and propose a novel regularized M-estimator to jointly estimate both the graph and changepoint structure. The resulting estimator possesses the ability to therefore favor sparse dependency structures and/or smoothly evolving graph structures, as required. Moreover, our approach extends current methods to allow estimation of changepoints that are grouped across multiple dependencies in a system. An efficient algorithm for estimating structure is proposed. We study the empirical recovery properties in a synthetic setting. The qualitative effect of grouped changepoint estimation is then demonstrated by applying the method on a genetic time-course dataset. Supplementary material for this article is available online.