We strengthen the convergence result in our paper, ibid. 5, No. 6, 1059-1098 (1999; Zbl 0983.62049), proving the local asymptotic mixed normality property in one of the 11 cases considered in that paper.
For the stochastic differential equationdX(t)=a X ( t ) + b X ( t - 1 )dt+dW(t),t≥0,
the local asymptotic properties of the likelihood function are studied. They depend strongly on the true value of the parameter ϑ=(a,b) * . Eleven different cases are possible if ϑ runs through ℝ 2 . Let ϑ ^ T be the maximum likelihood estimator of ϑ based on (X(t), t≥T). Applications to the asymptotic behaviour of ϑ ^ T as T→∞ are given.
Мы рассматриваем треугольный массив марковских цепей, слабо сходящихся к диффузионному процессу. Для переходных плотностей получено разложение типа Эджворта порядка o(n-1-δ), δ>0. Для доказательства применяется метод параметрикса, позволяющий представить переходную плотность как функционал от плотностей сумм независимых и одинаково распределённых случайных величин. Затем применяются классические разложения Эджворта для плотностей. Получающиеся в результате ряды дают разложения типа Эджворта для переходной плотности цепи Маркова.
We derive tight non-asymptotic bounds for the Kolmogorov distance between the probabilities of two Gaussian elements to hit a ball in a Hilbert space. The key property of these bounds is that they are dimension-free and depend on the nuclear (Schatten-one) norm of the difference between the covariance operators of the elements and on the norm of the mean shift. The obtained bounds significantly improve the bound based on Pinsker's inequality via the Kullback-Leibler divergence. We also establish an anti-concentration bound for a squared norm of a non-centered Gaussian element in Hilbert space. The paper presents a number of examples motivating our results and applications of the obtained bounds to statistical inference and to high-dimensional CLT.
Motivated by a problem arising when analysing data from quarantine searches, we explore properties of distributions of sums of independent means of independent lattice-valued random variables. The aim is to determine the extent to which approximations to those sums require continuity corrections. We show that, in cases where there are only two different means, the main effects of distribution smoothness can be understood in terms of the ratio rho_12=(e_2 n_1)/(e_1 n_2), where e_1 and e_2 are the respective maximal lattice edge widths of the two populations, and n_1 and n_2 are the respective sample sizes used to compute the means. If rho_12 converges to an irrational number, or converges sufficiently slowly to a rational number; and in a number of other cases too, for example those where rho_12 does not converge; the effects of the discontinuity of lattice distributions are of smaller order than the effects of skewness. However, in other instances, for example where rho_12 converges relatively quickly to a rational number, the effects of discontinuity and skewness are of the same size. We also treat higher-order properties, arguing that cases where rho_12 converges to an algebraic irrational number can be less prone to suffer the effects of discontinuity than cases where the limiting irrational is transcendental. These results are extended to the case of three or more different means, and also to problems where distributions are estimated using the bootstrap. The results have practical interpretation in terms of the accuracy of inference for, among other quantities, the sum or difference of binomial proportions.ρ12=(e2n1)/(e1n2)
The subject of this paper is the M/G/∞ estimation problem: the goal is to estimate the service time distribution G of the M/G/∞ queue from the arrival–departure observations without identification of customers. We develop estimators of G and derive exact non-asymptotic expressions for their mean squared errors. The problem of estimating the service time expectation is addressed as well. We present some numerical results on comparison of different estimators of the service time distribution.