Strong approximation for sums of asymptotically negatively dependent Gaussian sequences with applications

By applying the Skorohod martingale embedding method, a strong approximation theorem for partial sums of asymptotically negatively dependent (AND) Gaussian sequences, under polynomial decay rates, is established. As applications, the law of the iterated logarithm, the Chung-type law of the iterated logarithm and the almost sure central limit theorem for AND Gaussian sequences are derived.     

An extrapolation theorem for nonlinear approximation and its applications

We prove an extrapolation theorem for the nonlinear m-term approximation with respect to a system of functions satisfying very mild conditions. This theorem allows us to prove endpoint L^p-L^q estimates in nonlinear approximation. As a consequence, some known endpoint estimates can be deduced directly and some new estimates are also obtained. Finally, applications of these new estimates are given to spherical m-widths and m-term approximation of the weighted Besov classes.     

A Berry--Esseen theorem for weakly negatively dependent random variables and its applications

Summary We provide uniform rates of convergence in the central limit theorem for linear negative quadrant dependent (LNQD) random variables. Let <InlineEquation ID=IE"1"><EquationSource Format="TEX"><![CDATA[<InlineEquation ID=IE"2"><EquationSource Format="TEX"><![CDATA[<InlineEquation ID=IE"3"><EquationSource Format="TEX"><![CDATA[<InlineEquation ID=IE"4"><EquationSource Format="TEX"><![CDATA[<InlineEquation ID=IE"5"><EquationSource Format="TEX"><![CDATA[<InlineEquation ID=IE"6"><EquationSource Format="TEX"><![CDATA[$]]></EquationSource></InlineEquation>]]></EquationSource></InlineEquation>]]></EquationSource></InlineEquation>]]></EquationSource></InlineEquation>]]></EquationSource></InlineEquation>]]></EquationSource></InlineEquation>\{X_{n},\allowbreak n\ge1\}$ be a LNQD sequence of random variables with $EX_{n}=0$, set $S_{n}=\sum_{j=1}^{n}X_{j}$ and $B_{n}^{2}=\Var\, (S_{n})$. We show that \begin{gather*} \sup_{x} \left|P\left(\frac{S_{n}}{B_{n}}<x\right)-\Phi(x)\right|= O\bigg(n^{-\delta/(2+3\delta)}\vee \frac{n^{3\delta^{2}/(4+6\delta)}}{B^{2+\delta}_{n}} \sum_{i=1}^{n} E{|X_{i}|}^{2+\delta}\bigg) \end{gather*} under finite $(2+\delta)$th moment and a power decay rate of covariances. Moreover, by the truncation method, we obtain a Berry--Esseen type estimate for negatively associated (NA) random variables with only finite second moment. As applications, we obtain another convergence rate result in the central limit theorem and precise asymptotics in the law of the iterated logarithm for NA sequences, and also for LNQD sequences.     

A general central limit theorem for strong mixing sequences

A central limit theorem for strong mixing sequences is given that applies to both non-stationary sequences and triangular array settings. The result improves on an earlier central limit theorem for this type of dependence given by Politis, Romano and Wolf in 1997.     

A strong limit theorem for generalized Cantor-like random sequences

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一类随机适应序列部分和的局部极限定理的一个注记

研究了一类随机适应序列的强极限定理,推广了最近发表的几个结果,并进一步推广了Borel—Cantelli引理．     

A mixture-type limit theorem for nonlinear functions of Gaussian sequences

We show that, for a certain class of nonlinear functions of Gaussian sequences, the limiting distribution of normalized sums of the nonlinear function values of a sequence is the convolution of a Gaussian distribution with another non-Gaussian distribution.     

The almost sure limit theorem for the maxima and minima of strongly dependent Gaussian vector sequences

We derive the joint limiting distribution and the almost sure limit theorem for the maxima and minima for a strongly dependent stationary Gaussian vector sequence.     

On the chernoff-savage theorem for dependent sequences

Summary Given a sequence of ϕ-mixing random variables not necessarily stationary, a Chernoff-Savage theorem for two-sample linear rank statistics is proved using the Pyke-Shorack [5] approach based on weak convergence properties of empirical processes in an extended metric. This result is a generalization of Fears and Mehra [4] in that the stationarity is not required and that the condition imposed on the mixing numbers is substantially relaxed. A similar result is shown to hold for strong mixing sequences under slightly stronger conditions on the mixing numbers. Research partially supported by the National Research Council of Canada under Grant No. A-3954.     

A noncentral limit theorem for quadratic forms of Gaussian stationary sequences

We examine the limit behavior of quadratic forms of stationary Gaussian sequences with long-range dependence. The matrix that characterizes the quadratic form is Toeplitz and the Fourier transform of its entries is a regularly varying function at the origin. The spectral density of the stationary sequence is also regularly varying at the origin. We show that the normalized quadratic form converges inD[0, 1] to a new type of non-Gaussian self-similar process, which can be represented as a Wiener-Itô integral onR ².     

An empirical central limit theorem for dependent sequences

We prove a central limit theorem for the d$d$-dimensional distribution function of a class of stationary sequences. The conditions are expressed in terms of some coefficients which measure the dependence between a given σ$\sigma$-algebra and indicators of quadrants. These coefficients are weaker than the corresponding mixing coefficients, and can be computed in many situations. In particular, we show that they are well adapted to functions of mixing sequences, iterated random functions, and a class of dynamical systems.     

On Piterbarg theorem for maxima of stationary Gaussian sequences

Limit distributions of maxima of dependent Gaussian sequence are different according to the convergence rate of their correlations. For three different conditions on convergence rate of the correlations, in this paper, we establish the Piterbarg theorem for maxima of stationary Gaussian sequences.     

A note on a theorem of Berkes and Philipp for dependent sequences

We improve an almost sure invariance principle for f-mixing sequences of real random variables with finite (2 + δ)th moment (0 < δ 2) due to Berkes and Philipp (1979). The exponent of the slow logarithmic rate of mixing in that theorem is relaxed from 160/δ to (1 + ε)(1 + 2/δ) and the undetermined quantities a_N are replaced by Nσ² for some σ > 0.     

A mean-value theorem and its applications

For a function f defined in an interval I, satisfying the conditions ensuring the existence and uniqueness of the Lagrange mean L[f], we prove that there exists a unique two variable mean M[f] such that

     

On a joint strong approximation theorem for record and inter-record times

Summary We present a simple joint strong approximation for the logarithms of record and inter-record times from an exchangeable sequence, including an exact estimation for the rate of convergence in terms of upper and lower class functions of a Wiener process. The approach chosen here allows for simple proofs of exact and asymptotic (joint) results for record and inter-record times, such as the Law of Large Numbers (LLN), Central Limit Theorem (CLT) and Law of the Iterated Logarithm (LIL), and others.Research supported by the Air Force Office of Scientific Research Contract No. F49620 85 C0144     

A note on the strong law of large numbers for positively dependent random variables

We obtain strong laws of large numbers for sequences of random variables which are either pairwise positive quadrant dependent or associated. Our results imply extensions of Kolmogoron's classical strong law to the positively dependent case.     

A substitution theorem for graceful trees and its applications

A graceful labeling of a graph G=(V,E) assigns |V| distinct integers from the set {0,…,|E|} to the vertices of G so that the absolute values of their differences on the |E| edges of G constitute the set {1,…,|E|}. A graph is graceful if it admits a graceful labeling. The forty-year old Graceful Tree Conjecture, due to Ringel and Kotzig, states that every tree is graceful.We prove a Substitution Theorem for graceful trees, which enables the construction of a larger graceful tree through combining smaller and not necessarily identical graceful trees. We present applications of the Substitution Theorem, which generalize earlier constructions combining smaller trees.     

A spectra comparison theorem and its applications

We give a sharp comparison between the spectra of two Riemannian manifolds (Y, g) and \((X,g_0)\) under the following assumptions: \((X,g_0)\) has bounded geometry, (Y, g) admits a continuous Gromov–Hausdorff \(\varepsilon \)-approximation onto \((X,g_0)\) of non zero absolute degree, and the volume of (Y, g) is almost smaller than the volume of \((X,g_0)\). These assumptions imply no restrictions on the local topology or geometry of (Y, g) in particular no curvature assumption is supposed or inferred.     

Diffusion approximation of normalized integrals of weakly dependent processes and its applications

We obtain exact estimates for the rate of convergence of normalized integrals of weakly dependent stationary processes to the standard Wiener process in the uniform metric in probability. These estimates are then applied to the investigation of the behavior of stochastic systems with curvilinear boundaries subjected to the action of weakly dependent random perturbations.Translated from Ukrainskii Matematicheskii Zhumal, Vol.46, No. 11, pp. 1449–1466, November, 1994.The present work was performed as a part of Project No. 8 of the Ministry of Education of the Ukraine. The manager of the project is Prof. N. A. Perestyuk.     

A Hahn-Banach theorem for separation of semigroups and its applications

Summary In the first part of this paper a separation theorem is proved for disjoint subsemigroups of a given abelian semigroup. Applying this result, separation theorems and characterization theorems are obtained for semiinternal functions.