Journal of Theoretical Probability 

About: Journal of Theoretical Probability is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Random walk & Central limit theorem. It has an ISSN identifier of 0894-9840. Over the lifetime, 2003 publications have been published receiving 30220 citations.

Lower bounds for covering times for reversible Markov chains and random walks on graphs

Abstract: For simple random walk on aN-vertex graph, the mean time to cover all vertices is at leastcN log(N), wherec>0 is an absolute constant. This is deduced from a more general result about stationary finite-state reversible Markov chains. Under weak conditions, the covering time for such processes is at leastc times the covering time for the corresponding i.i.d. process.

Random walks and the effective resistance of networks

Abstract: In this article we present an interpretation ofeffective resistance in electrical networks in terms of random walks on underlying graphs Using this characterization we provide simple and elegant proofs for some known results in random walks and electrical networks We also interpret the Reciprocity theorem of electrical networks in terms of traversals in random walks The byproducts are (a) precise version of thetriangle inequality for effective resistances, and (b) an exact formula for the expectedone-way transit time between vertices

A Comparison Theorem on Moment Inequalities Between Negatively Associated and Independent Random Variables

Abstract: Let {X i, 1≤i≤n} be a negatively associated sequence, and let {X* i , 1≤i≤n} be a sequence of independent random variables such that X* i and X i have the same distribution for each i=1, 2,..., n. It is shown in this paper that Ef(∑ n i=1 X i)≤Ef(∑ n i=1 X* i ) for any convex function f on R 1 and that Ef(max1≤k≤n ∑ n i=k X i)≤Ef(max1≤k≤n ∑ k i=1 X* i ) for any increasing convex function. Hence, most of the well-known inequalities, such as the Rosenthal maximal inequality and the Kolmogorov exponential inequality, remain true for negatively associated random variables. In particular, the comparison theorem on moment inequalities between negatively associated and independent random variables extends the Hoeffding inequality on the probability bounds for the sum of a random sample without replacement from a finite population.

A Change-of-Variable Formula with Local Time on Curves

Abstract: Let $$X = (X_t)_{t \geq 0}$$ be a continuous semimartingale and let $$b: \mathbb{R}_+ \rightarrow \mathbb{R}$$ be a continuous function of bounded variation. Setting $$C = \{(t, x) \in \mathbb{R} + \times \mathbb{R} | x b(t)\}$$ suppose that a continuous function $$F: \mathbb{R}_+ \times \mathbb{R} \rightarrow \mathbb{R}$$ is given such that F is C1,2 on $$\bar{C}$$ and F is $$C^{1,2}$$ on $$\bar{D}$$ . Then the following change-of-variable formula holds: $$\eqalign{ F(t,X_t) = F(0,X_0)+\int_0^{t} {1 \over 2} (F_t(s, X_s+) + F_t(s,X_s-)) ds\cr + \int_0^t {1 \over 2} (F_x(s,X_s+) + F_x(s,X_s-))dX_s\cr + {1 \over 2} \int_0^t F_{xx} (s,X_s)I (X_s eq b(s)) d \langle X, X \rangle_s\cr + {1 \over 2} \int_0^t (F_x(s,X_s+)-F_x(s,X_s-)) I(X_s = b(s)) d\ell_{s}^{b} (X),\cr} $$ where $$\ell_{s}^{b}(X)$$ is the local time of X at the curve b given by $$\ell_{s}^{b}(X) = \mathbb{P} - \lim_{\varepsilon \downarrow 0} {1 \over 2 \varepsilon} \int_0^s I(b(r)- \varepsilon < X_r < b(r) + \varepsilon) d \langle X, X \rangle_{r} $$ and $$d\ell_{s}^{b}(X)$$ refers to the integration with respect to $$s \mapsto \ell_{s}^{b}(X)$$ . A version of the same formula derived for an Ito diffusion X under weaker conditions on F has found applications in free-boundary problems of optimal stopping.

How Close is the Sample Covariance Matrix to the Actual Covariance Matrix

Abstract: Given a probability distribution in ℝ n with general (nonwhite) covariance, a classical estimator of the covariance matrix is the sample covariance matrix obtained from a sample of N independent points. What is the optimal sample size N=N(n) that guarantees estimation with a fixed accuracy in the operator norm? Suppose that the distribution is supported in a centered Euclidean ball of radius $O(\sqrt{n})$ . We conjecture that the optimal sample size is N=O(n) for all distributions with finite fourth moment, and we prove this up to an iterated logarithmic factor. This problem is motivated by the optimal theorem of Rudelson (J. Funct. Anal. 164:60–72, 1999), which states that N=O(nlog n) for distributions with finite second moment, and a recent result of Adamczak et al. (J. Am. Math. Soc. 234:535–561, 2010), which guarantees that N=O(n) for subexponential distributions.