Stochastic differential equations: singularity of coefficients, regression models, and stochastic approximation

Abstract

Contents Introduction Chapter I. Basic notions and results from contemporary martingale theory §1.1. General notions of the martingale theory §1.2. Convergence (a.s.) of semimartingales. The strong law of large numbers and the law of the iterated logarithm Chapter II. Stochastic differential equations driven by semimartingales §2.1. Basic notions and results of the theory of stochastic differential equations driven by semimartingales §2.2. The method of monotone approximations. Existence of strong solutions of stochastic equations with non-smooth coefficients §2.3. Linear stochastic equations. Properties of stochastic exponentials §2.4. Linear stochastic equations. Applications to models of the financial market Chapter III. Procedures of stochastic approximation as solutions of stochastic differential equations driven by semimartingales §3.1. Formulation of the problem. A general model and its relation to the classical one §3.2. A general description of the approach to the procedures of stochastic approximation. Convergence (a.s.) and asymptotic normality §3.3. The Gaussian model of stochastic approximation. Averaged procedures and their effectiveness Chapter IV. Statistical estimation in regression models with martingale noises §4.1. The formulation of the problem and classical regression models §4.2. Asymptotic properties of MLS-estimators. Strong consistency, asymptotic normality, the law of the iterated logarithm §4.3. Regression models with deterministic regressors §4.4. Sequential MLS-estimators with guaranteed accuracy and sequential statistical inferences Bibliography