On the Stability of Linear Stochastic Difference Equations Patrick René Homblé Iowa State University

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On the Stability of Linear Stochastic Difference Equations Patrick René Homblé Iowa State University Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1990 On the stability of linear stochastic difference equations Patrick René Homblé Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Statistics and Probability Commons Recommended Citation Homblé, Patrick René, "On the stability of linear stochastic difference equations " (1990). Retrospective Theses and Dissertations. 9442. https://lib.dr.iastate.edu/rtd/9442 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. 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Ann Arbor Ml 48106-1346 USA 313 761-4700 800 521-0600 Order Number 9101353 On the stability of linear stochastic difference equations Homblé, Patrick René, Ph.D. Iowa State University, 1990 UMI SOON.ZeebRd Ann Arbor, MI 48106 On the stability of linear stochastic difference equations by Patrick René Homblé A Dissertation Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Major : Statistics Approved; Members of the Committee: Signature was redacted for privacy. Signature was redacted for privacy. In Charge of Major Work Signature was redacted for privacy. Signature was redacted for privacy. For the Major Department Signature was redacted for privacy. For the Graduate College Iowa State University Ames, Iowa 1990 u TABLE OF CONTENTS NOTATION iv 1. INTRODUCTION 1 2. MATHEMATICAL REVIEW 8 2.1 Mappings 8 2.2 Topological Groups 12 2.3 Differentiable Manifolds 14 2.4 Groups, Semigroups, and their Orbits 24 2.5 Lie Groups and Lie Algebras 31 2.6 Markov Processes 37 2.7 Irredudbility and Recurrence of Markov Chains 47 2.8 Existence and Uniqueness of Invariant Measures 54 2.9 Lyapunov Exponents and Oseledec's Multiplicative Ergodic Theorem 61 3. STUDY OF CONTROL SETS FOR DETERMINISTIC SYSTEMS 78 3.1 Control Sets of Semigroups Associated with Discrete Dynamical Systems 78 3.2 Semigroups of Invertible Matrices Acting on Ro 114 3.3 A Crucial Condition: The Orbits have Nonvoid Interior 120 iii 4. STOCHASTIC DIFFERENCE EQUATIONS 138 4.1 Basic Setup 138 4.2 Ergodic Behavior of Stochastic Difference Equations 159 5. STABILITY PROPERTIES OF = A X^ 184 5.1 The Lyapunov Spectrum 184 5.2 Sample Stability 192 5.3 Moment Stability 201 5.4 Large Deviations 216 6. LINEAR OSCILLATOR 223 6.1 Setup 223 6.2 Checking the Assumptions 225 6.3 Simulation Results 230 7. BIBLIOGRAPHY 244 8. ACKNOWLEDGEMENT 249 9. APPENDIX: THE SIMULATION PROGRAM 250 9.1 The Simulation Program 250 iv NOTATION « absolutely continuous with respect to. ^ isomorphic to. 1 the identity map. Ix| the Euclidean norm of x € Dt^. ||A|| a norm (to be specified) for the operator A. \ set difference. ~ "distributed as" for random variables. , 0 {A^ ; A 6 A} is the union of the A^'s over A 6 A and similarly for the intersection 0- A the closure of A. A^ the complement of A. the cr-algebra of Borel subsets of E. B (£r) the bounded measurable functions on the space E. C a (maximal) invariant control set (p. 81). C the complex numbers. differentiable. C® infinitely differentiable. C (£) the continuous maps £rom E to E. C (t, m) a (multiplicative) cocycle associated with a flow (deterministic case) (p. 81). G (t, X, m) a (multiplicative) cocycle associated with a flow (stochastic case) (p. 82). V C (n, w) the cocycle associated with the flow of = A (w)) (p. 186). a generalized controllability matrix (p. 147). ^(A) the domain of the operator A. d. the multiplicity of the (forward) Lyapunov exponent A. (p. 64). d A the boundary of A. det A the determinant of A. DF the Jacobian matrix of F (p. 9). Di£F (M) the set of all diffeomorphisms from M to M. dim {E) the dimension of E. S a Banach space. ^ the path space of an £7 valued process. ^ Li n (p. 67). Sf a (T-algebra. F* the differential of F (p. 17). F(n,x,B) P(T (x,B) = n) (p. 48). 7(p) g (P) / P (P-264). Q an arbitrary group in Diff (M). P (%) the group generated by ^ C M. OD G (x, B) X M (n, X, B) (p. 48). n=l G1 (d, R) , the Lie group of d * d real valued invertible matrices. ^ (d, R) the Lie group of d x d real valued matrices, g (p) the p*^ moment Lyapunov exponent (pp. 204, 206). Gx the orbit generated by Q applied to x e M (p. 27). vi H|. shift operator on a path space (H^ A (s) = A (s+t)) (pp. 68, 185). I (r) level-1 entropy function (p. 216). g the indicator function of the set B. int A the interior of A. A the maximal (forward) Lyapunov exponent (= A^). Aj the i^^ (forward) Lyapunov exponent (p. 64). A (x) the (forward) Lyapunov exponent associated with the initial value X (p. 63). A (w, x) the (forward) Lyapunov exponent associated with the initial value X (stochastic case) (p. 187). Lj the i*^ subspace in the filtration associated with a (forward) Lyapunov spectrum (p. 64). L (x. B) P [0 [X^ E B] I X„ = x] (p. 48). n=l the kernel of a Markov processes (usually the { process) (pp. 39, 42). .) the kernel of the Markov pair process {(x^, ^^)}. fi (n, X, B) the n*^ step transition probability from x to B. M a connected C® Riemannian manifold (p. 15). a connected C® Riemannian manifold of dimension d (p. 15). M (£) the measurable functions on E. M{E!) the set of all measures on the measurable space {E, •S{E)). the volume element m^ restricted to the maximal invariant control set C. the volume element on the manifold M, vii M the natural numbers. Mo 1N\{0}. r the unique invariant probability measure for the pair process iK' V)' the unique invariant probability measure for the noise process p the number of distinct (forward) Lyapunov exponents (p. 64). the projective space in (p. 11). S* (is) the set of all probability measures on the measurable space (E, Jg(^). Q the support of the measure tt^. ( the rational numbers. «0 d \ {0}. Q(x,B) P(X^eBi.o. |X^ = x)(p.48). p a metric. p (x, y) the distance between x and y in the metric p. the d dimensional Euclidean space. Ro \ {0}. 00 R (x, B) % nF(n,x, B). n=l 2 a dynamical control system (p. 29). the sphere in (p. 11). S an arbitrary semigroup in Diff (M). S (t) the semigroup generated by Ï c M. Sx the (positive) orbit generated by S applied to x e M (p. 28). viii the negative orbit generated by applied to x 6 M (p. 100). the value at time n of the stochastic orbit of x, starting with ^0 ~ ^ (P- the stochastic orbit of x, starting at (w) = ^ (p- the support of the measure u. a time set ( T = IN or IR^). the tangent bundle of the manifold M (p. 16). the tangent space to the manifold M at the point p e M (p. 16). inf (n > 0 ; € B | = x) (p. 48). a semigroup, te T(p. 44). the solution at time t € T ( T = IN or IR"^) of a dynamical system with initial value x (0) = x^. the solution at time te T ( T = IN or R"*") of a stochastic dynamical system with initial value x (0) = x^. the integers. 1 1. INTRODUCTION In dynamical system theory, processes evolving over time are often described by differential or difference equations of a deterministic nature. But such processes are almost always submitted to random perturbations arising &om various sources like temperature or pressure variations, human responses, and, in general, from the unpredictable actions of the environment in which they evolve. Therefore, the observed behavior of the systems under study often departs from the mathematical solution imposed by their deterministic description. Usually, such departures from theoretical behavior are corrected by feedback or outside (human) intervention.
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