ExponentialL 2-convergence of quantum Markov semigroups on\mathcal{B}(h)

With a quantum Markov semigroup (Τ _t ) _t≥0 on , whichhas a faithful normal invariant state ρ, we associate semigroupsT ^(s) (s∈[0],[1]) on the set of Hilbert-Schmidt operators onh defined by the rule . This allows us to use spectral theory to study the infinitesimal generatorL ^(s) of the semigroupT ^(s) and deduce information on the rate of the decay to equilibrium of Τ by means of estimates of the spectral gap ofL ^(s) . Fors=1/2, this method is applied to a class of quantum Markov semigroups on . We prove simple but reasonably general sufficient conditions, as well as necessary and sufficient conditions, for the gap(L ^(1/2)) to be positive. The exact value of the gap(L ^(1/2)) is computed or estimated for a certain class of equations motivated by classical probability or physical applications. Translated fromMatematicheskie Zametki, Vol. 68, No. 4, pp. 523–538, October, 2000.     

Integrability andL 1-convergence of sine series with generalized quasi-convex coefficients

In this paper we obtain a necessary and sufficient condition for the sine series with generalized quasi-convex coefficients to be a Fourier series. Also we studyL ¹-convergence of this series under the said condition on the coefficients.     

The L 2spectral gap of certain positive recurrent Markov chains and jump processes

Summary Let D denote the generator of a continuous time positive recurrent Markov process with state space N (or R ⁺). Sufficient conditions are given to imply the existence of >0 such that if 0 is a point of the spectrum of D considered as an operator on the L ² space of the equilibrium distribution, then Re()–. A related result is given for discrete time Markov chains.     

A χ2 goodness-of-fit test for Markov renewal processesgoodness-of-fit test for Markov renewal processes

A Markov Renewal Process (M.R.P.) is a process similar to a Markov chain, except that the time required to move from one state to another is not fixed, but is a random variable whose distribution may depend on the two states between which the transition is made. For an M.R.P. ofm (<∞) states we derive a goodness-of-fit test for a hypothetical matrix of transition probabilities. This test is similar to the test Bartlett has derived for Markov chains. We calculate the first two moments of the test statistic and modify it to fit the moments of a standard χ². Finally, we illustrate the above procedure numeerically for a particular case of a two-state M.R.P. Dwight B. Brock is mathematical statistican, Office of Statistical Methods, National Center for Health Statistics, Rockville, Maryland. A. M. Kshisagar is Associate Professor, Department of Statistics, Southern Methodist University. This research was partially supported by Office of Naval Research Contract No. N000 14-68-A-0515, and by NIH Training Grant GM-951, both with Southern Methodist University. This article is partially based on Dwight B. Brock's Ph.D. dissertation accepted by Southern Methodist University.     

Transportation-information inequalities for Markov processes

In this paper, one investigates the transportation-information T _c I inequalities: α(T _c (ν, μ)) ≤ I (ν|μ) for all probability measures ν on a metric space ${(\mathcal{X}, d)}$ , where μ is a given probability measure, T _c (ν, μ) is the transportation cost from ν to μ with respect to the cost function c(x, y) on ${\mathcal{X}^2}$ , I(ν|μ) is the Fisher–Donsker–Varadhan information of ν with respect to μ and α : [0, ∞) → [0, ∞] is a left continuous increasing function. Using large deviation techniques, it is shown that T _c I is equivalent to some concentration inequality for the occupation measure of a μ-reversible ergodic Markov process related to I(·|μ). The tensorization property of T _c I and comparisons of T _c I with Poincaré and log-Sobolev inequalities are investigated. Several easy-to-check sufficient conditions are provided for special important cases of T _c I and several examples are worked out.     

Estimation of spectral gap for Markov chains

The study of the convergent rate (spectral gap) in theL ²-sense is motivated from several different fields: probability, statistics, mathematical physics, computer science and so on and it is now an active research topic. Based on a new approach (the coupling technique) introduced in [7] for the estimate of the convergent rate and as a continuation of [4], [5], [7–9], [23] and [24], this paper studies the estimate of the rate for time-continuous Markov chains. Two variational formulas for the rate are presented here for the first time for birth-death processes. For diffusions, similar results are presented in an accompany paper [10]. The new formulas enable us to recover or improve the main known results. The connection between the sharp estimate and the corresponding eigenfunction is explored and illustrated by various examples. A previous result on optimal Markovian couplings^[4] is also extended in the paper.Research supported in part by NSFC, Qin Shi Sci & Tech. Foundation and the State Education Commission of China.     

Markov cubature rules for polynomial processes

We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using algebraic techniques. Markov cubature rules aid the tractability of path-dependent tasks such as American option pricing in models where the underlying factors are polynomial processes.     

Crossing estimates for symmetric Markov processes

A crossing estimate is established for symmetric Markov processes on general state spaces. Received: 20 May 1999 / Revised version: 30 August 2000 / Published online: 9 March 2001     

Goodness-of-fit test for homogeneous Markov processes

We give chi-squared goodness-of-fit tests for homogeneous Markov processes with unknown transition intensities or with transition intensities of known form depending on a finite-dimensional parameter.     

Ratio limit theorems for Markov processes

Convergence of andμP ⁿ(B)/μP ⁿ(a) is established for a certain class of Markov operators,P, whereμ is a measure andB is a subset ofA. The results are proved under certain conditions onP and the setA.     

Estimation for mixtures of Markov processes

Finite mixtures of Markov processes with densities belonging to exponential families are introduced. Quasi-likelihood and maximum likelihood methods are used to estimate the parameters of the mixing distributions and of the component distributions. The E-M algorithm is used to compute the ML estimates. Mixture of Autoregressive processes and of two-state Markov chains are discussed as specific examples. Simulation results on the comparison of quasi-likelihood and ML estimates are reported.