Rate of convergence in the strong law of large numbers for martingales

Summary The rate of convergence in the random strong law of large numbers for martingale differences is established. The results obtained generalize theorems given by R. Chen (1976) and I.A. Ahmad (1980).     

On the rate of convergence in the strong law of large numbers for martingales

The aim of this note is to establish the Baum–Katz type rate of convergence in the Marcinkiewicz–Zygmund strong law of large numbers for martingales, which improves the recent works of Stoica [Series of moderate deviation probabilities for martingales, J. Math. Anal. Appl. 336 (2005), pp. 759–763; Baum–Katz–Nagaev type results for martingales, J. Math. Anal. Appl. 336 (2007), pp. 1489–1492; A note on the rate of convergence in the strong law of large numbers for martingales, J. Math. Anal. Appl. 381 (2011), pp. 910–913]. Furthermore, we also study some relevant limit behaviours for the uniform mixing process. Under some uniform mixing conditions, the sufficient and necessary condition of the convergence of the martingale series is established.     

A note on the rate of convergence in the strong law of large numbers for martingales

We prove a Baum-Katz-Nagaev type rate of convergence in the Marcinkiewicz-Zygmund and Kolmogorov strong laws of large numbers for norm bounded martingale difference sequences.     

A generalized strong law of large numbers

Strong laws of large numbers have been stated in the literature for measurable functions taking on values on different spaces. In this paper, a strong law of large numbers which generalizes some previous ones (like those for real-valued random variables and compact random sets) is established. This law is an example of a strong law of large numbers for Borel measurable nonseparably valued elements of a metric space. Received: 24 February 1998 / Revised version: 3 January 1999     

A bivariate strong law of large numbers

Probability Theory and Related Fields -     

A strong law of large numbers for non-additive probabilities

In this paper, with the notion of independence for random variables under upper expectations, we derive a strong law of large numbers for non-additive probabilities. This result is a natural extension of the classical Kolmogorov’s strong law of large numbers to the case where the probability is no longer additive. As an application of our result, we give most frequent interpretation for Bernoulli-type experiments with ambiguity.     

Some estimates for large deviations and their application to strong law of large numbers

Siberian Mathematical Journal -     

A strong law of large numbers for orthogonal random variables

A generalization of one theorem of K. Tandori is proved. A sufficient condition is derived for application of a strong law of large numbers to a sequence of orthogonal random variables, expressed in terms of the growth of sums of second moments of these variables.     

A strong law of large numbers for fuzzy random variables

A limit of a sequence of fuzzy numbers is defined and its some properties are shown. Based on these concept and properties, an independent sequence of fuzzy random variables is considered and a strong law of large numbers for fuzzy random variables is shown.     

The strong law of large numbers for a stationary sequence

General results on the applicability of the strong law of large numbers to a sequence of dependent random variables, as formulated in terms of estimates for the moments of sums of such variables, are applied to give new conditions of the applicability of this law to (in a wide sense) a stationary sequence of random variables.     

Maximal inequalities for demimartingales and a strong law of large numbers

Chow's maximal inequality for (sub)martingales is extended to the case of demi(sub)martingales introduced by Newman and Wright (Z. Wahrsch. Verw. Geb. 59 (1982) 361–371). This result serves as a “source” inequality for other inequalities such as the Hajek–Renyi inequality and Doob's maximal inequality and leads to a strong law of large numbers. The partial sum of mean zero associated random variables is a demimartingale. Therefore, maximal inequalities and a strong law of large numbers are obtained for associated random variables as special cases.     

A remark on the strong law of large numbers

The strong law of large numbers for independent and identically distributed random variablesX _i ,i=1, 2, 3,... with finite expectationE|X ₁| can be stated as, for any >0, the number of integersn such that \varepsilon $$ " align="middle" border="0"> ,N is finite a. s. It is known thatEN < iffEX ₁ ² < and that ² EN var X₁ as 0, ifE X ₁ ² <. Here we consider the asymptotic behaviour ofEN (n) asn, whereN (n) is the number of integerskn such that \varepsilon $$ " align="middle" border="0"> andE N ₁ ² =.     

A strong law of large numbers for random biased connected graphs

We consider a class of random connected graphs with random vertices and random edges in which the randomness of the vertices is determined by a continuous probability distribution and the randomness of the edges is determined by a connection function. We derive a strong law of large numbers on the total lengths of all random edges for a random biased connected graph that is a generalization of a directed k-nearest-neighbor graph.     

A remark on strong law of large numbers for weighted U-statistics

Let {X,Xn; n ≥ 1} be a sequence of i.i.d.random variables with values in a measurable space(S,S) such that E|h(X1,X2,...,Xm)| ∞,where h is a measurable symmetric function from Sminto R =(-∞,∞).Let {wn,i1,i2,...,im; 1 ≤ i1 i2 ··· im ≤ n,n ≥ m} be a matrix array of real numbers.Motivated by a result of Choi and Sung(1987),in this note we are concerned with establishing a strong law of large numbers for weighted U-statistics with kernel h of degree m.We show that lim n→∞m!(n-m)!n!1≤i1i2···im≤n wn,i1,i2,...,im(h(Xi1,Xi2,...,Xim)-θ)=0 a.s.whenever supn≥mmax1≤i1i2···im≤n|wn,i1,i2,...,im|∞,whereθ=Eh(X1,X2,...,Xm).The proof of this result is based on a new general result on complete convergence,which is a fundamental tool,for array of real-valued random variables under some mild conditions.     

A strong law of large numbers for nonexpansive vector-valued stochastic processes

A mapT: X→X on a normed linear space is callednonexpansive if ‖Tx-Ty‖≤‖x-y‖∀x, y∈X. Let (Ω, Σ,P) be a probability space, an increasing chain of σ-fields spanning Σ,X a Banach space, andT: X→X. A sequence (x_n) of strongly -measurable and stronglyP-integrable functions on Ω taking on values inX is called aT-martingale if . LetT: H→H be a nonexpansive mapping on a Hilbert spaceH and let (x_n) be aT-martingale taking on values inH. If then x _n /n converges a.e. LetT: X→X be a nonexpansive mapping on ap-uniformly smooth Banach spaceX, 1<p≤2, and let (x_n) be aT-martingale (taking on values inX). If then there exists a continuous linear functionalf∈X ^* of norm 1 such that If, in addition, the spaceX is strictly convex, x _n /n converges weakly; and if the norm ofX ^* is Fréchet differentiable (away from zero), x _n /n converges strongly. This work was supported by National Science Foundation Grant MCS-82-02093     

Weak law of large numbers for linear processes

We establish sufficient conditions for the Marcinkiewicz–Zygmund type weak law of large numbers for a linear process \({\{X_k:k\in\mathbb Z\}}\) defined by \({X_k=\sum_{j=0}^\infty\psi_j\varepsilon_{k-j}}\) for \({k\in\mathbb Z}\), where \({\{\psi_j:j\in\mathbb Z\}\subset\mathbb R}\) and \({\{\varepsilon_k:k\in\mathbb Z\}}\) are independent and identically distributed random variables such that \({{x^p\Pr\{|\varepsilon_0| > x\}\to 0}}\) as \({{x\to \infty}}\) with \({1 < p < 2}\) and \({E \varepsilon_0=0}\). We use an abstract norming sequence that does not grow faster than \({n^{1/p}}\) if \({\sum|\psi_j| < \infty}\). If \({\sum|\psi_j|=\infty}\), the abstract norming sequence might grow faster than \({n^{1/p}}\) as we illustrate with an example. Also, we investigate the rate of convergence in the Marcinkiewicz–Zygmund type weak law of large numbers for the linear process.     

The strong law of large numbers for random processes

Analogs of the Kolmogorov, Zygmund-Martsenkevich, and Brunk-Prokhorov strong law of large numbers are proved for martingales with continuous parameter. A new generalization of the Brunk—Prokhorov strong law of large numbers is given for martingales with discrete times. Along with convergence almost everywhere, we also prove the average convergence.     

Maximal inequalities for N-demimartingale and strong law of large numbers

In this paper, we establish some maximal inequalities for N-demimartingale. The maximal inequality for N-demimartingale is used as a key inequality to establish other results including the strong law of large numbers, strong growth rate and the integrability of supremum for N-demimartingale.