Boundedness of Multilinear Caldern–Zygmund Singular Operators on Morrey–Herz Spaces with Variable Exponents

In this paper,we introduce Morrey–Herz spaces M K˙(·)q,p(·)(Rn) with variable exponents α(·) and p(·),and prove the boundedness of multilinear Caldern–Zygmund singular operators on the product of these spaces.     

Stability in Probability of Nonlinear Stochastic Volterra Difference Equations with Continuous Variable

Abstract

The general method of Lyapunov functionals construction, that was proposed by Kolmanovskii and Shaikhet and successfully used already for functional-differential equations, difference equations with discrete time, difference equations with continuous time, and is used here to investigate the stability in probability of nonlinear stochastic Volterra difference equations with continuous time. It is shown that the investigation of the stability in probability of nonlinear stochastic difference equation with order of nonlinearity more than one can be reduced to investigation of the asymptotic mean square stability of the linear part of this equation.     

Instrumental Variable Estimation for Linear Stochastic Differential Equations Driven by Fractional Brownian Motion

Abstract

We investigate the asymptotic properties of instrumental variable estimators of the drift parameter for stochastic processes satisfying linear stochastic differential equations driven by fractional Brownian motion.     

Terminal-Dependent Statistical Inferences for FBSDE

Backward stochastic differential equation (BSDE) has been well studied and widely applied in mathematical finance. The main difference from the original stochastic differential equation (OSDE) is that the BSDE is designed to depend on a terminal condition, which plays key roles in certain financial and ecological circumstances. However, to the best of our knowledge, the terminal-dependent statistical inference for such model has not been explored in the existing literature. This article proposes two terminal-dependent estimation methods via terminal control variable the integral form models of forward-backward stochastic differential equation (FBSDE). We take these measures because the resulting models contain terminal condition as model variable, and therefore, the corresponding estimators inherit the terminal-dependent characteristic. In this article, the FBSDE is first rewritten as regression versions and then two semi-parametric estimation approaches are proposed. Because of the control variable and integral form, our regression versions are more complex than the classical ones, and the inference methods are somewhat different from which designed for the OSDE. Even so, the statistical properties of the terminal-dependent methods are similar to the classical ones. Simulations are conducted to demonstrate finite sample behaviors.     

Computational scheme for solving nonlinear fractional stochastic differential equations with delay

Abstract

This paper studies the numerical solution of fractional stochastic delay differential equations driven by Brownian motion. The proposed algorithm is based on linear B-spline interpolation. The convergence and the numerical performance of the method are analyzed. The technique is adopted for determining the statistical indicators of stochastic responses of fractional Langevin and Mackey-Glass models with stochastic excitations.     

On Fourth-order Difference Equations for Orthogonal Polynomials of a Discrete Variable: Derivation,Factorization and Solutions

We derive and factorize the fourth-order difference equations satisfied by orthogonal polynomials obtained from some modifications of the recurrence coefficients of classical discrete orthogonal polynomials such as: the associated, the general co-recursive, co-recursive associated, co-dilated and the general co-modified classical orthogonal polynomials. Moreover, we find four linearly independent solutions of these fourth-order difference equations, and show how the results obtained for modified classical discrete orthogonal polynomials can be extended to modified semi-classical discrete orthogonal polynomials. Finally, we extend the validity of the results obtained for the associated classical discrete orthogonal polynomials with integer order of association from integers to reals.     

Markov Chain Sampling Methods for Dirichlet Process Mixture Models

Abstract

This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis—Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors.     

Markov processes generated by linear stochastic evolution equations †

A class of Hilbert space-valued Markov processes which can be expressed as the mild solution of a linear abstract evolution equation is studied. Sufficient conditions for the generator of the Markov process to be well-defined are given and Kolmogorov's equation and an equation for the characteristic function of the process are derived. The theory is illustrated by examples of parabolic, hyperbolic and delay stochastic differential equations.     

On the solution of stochastic ordinary differential equations via small delays

dx(t)=g(x{t))dW(t) is proved using an approximating sequence of stochastic delay equationsGeneralizations of the approximation scheme are indicated for the Stratonovich case and when the Brownian motion W is replaced by a continuous semi-martingale.