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Exceptional Times and Invariance for Dynamical Random Walks
Probability · Submitted 24 Sep 2004
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We prove also that viewed as an infinite-dimensional process, the rescaled dynamical random walk converges weakly in D(D([0,1])) to the Ornstein-Uhlenbeck process in C([0,1]).
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The abstract, sentence by sentence
Consider a sequence {X(i,0) : i = 1, ..., n} of i.i.d. random variables. Associate to each X(i,0) an independent mean-one Poisson clock. Every time a clock rings replace that X-variable by an independent copy. In this way, we obtain i.i.d. stationary processes {X(i,t) : t >= 0} (i=1,2, ...) whose invariant distribution is the law of X(1,0). Benjamini, Haggstrom, Peres, and Steif (2003) introduced the dynamical walk S(n,t) = X(1,t) + ... + X(n,t), and proved among other things that the LIL holds for {S(n,t) : n =1,2, ...} simultaneously for all t. In other words, the LIL is dynamically stable. Subsequently, we showed that in the case that the X(i,0)'s are standard normal, the classical integral test is not dynamically stable. Presently, we study the set of times t when {S(n,t) : n=1,2, ...} exceeds a given envelope infinitely often. Our analysis is made possible thanks to a connection to the Kolmogorov epsilon-entropy. When used in conjunction with the invariance principle of this paper, this connection has other interesting by-products some of which we relate. We prove also that viewed as an infinite-dimensional process, the rescaled dynamical random walk converges weakly in D(D([0,1])) to the Ornstein-Uhlenbeck process in C([0,1]). For this we assume only that the increments have mean zero and variance one. In addition, we extend a result of Benjamini, Haggstrom, Peres and Steif (2003) by proving that if the X(i,0)'s are lattice, mean-zero variance-one, and possess 2 + epsilon finite absolute moments for some positive epsilon, then the recurrence of the origin is dynamically stable. To prove this we derive a gambler's ruin estimate that is valid for all lattice random walks that have mean zero and finite variance. We believe the latter may be of independent interest.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Consider a sequence {X(i,0) : i = 1, ..., n} of i.i.d. random variables. | method 0.61 | 0.23 | 0.01 | asserted 0.69 |
| s1 | Associate to each X(i,0) an independent mean-one Poisson clock. | method 0.96 | 0.53 | 0.01 | asserted 0.87 |
| s2 | Every time a clock rings replace that X-variable by an independent copy. | method 0.95 | 0.31 | 0.02 | asserted 0.99 |
| s3 | In this way, we obtain i.i.d. stationary processes {X(i,t) : t >= 0} (i=1,2, ...) whose invariant distribution is the law of X(1,0). | method 0.72 | 0.53 | 0.04 | asserted 1.00 |
| s4 | Benjamini, Haggstrom, Peres, and Steif (2003) introduced the dynamical walk S(n,t) = X(1,t) + ... + X(n,t), and proved among other things that the LIL holds for {S(n,t) : n =1,2, ...} simultaneously for all t. | background 1.00 | 0.03 | 0.08 | asserted 1.00 |
| s5 | In other words, the LIL is dynamically stable. | interpretation 0.90 | 0.07 | 0.10 | asserted 1.00 |
| s6 | Subsequently, we showed that in the case that the X(i,0)'s are standard normal, the classical integral test is not dynamically stable. | result 0.49 | 0.91 | 0.07 | asserted 1.00 |
| s7 | Presently, we study the set of times t when {S(n,t) : n=1,2, ...} exceeds a given envelope infinitely often. | contribution 1.00 | 0.77 | 0.07 | asserted 0.64 |
| s8 | Our analysis is made possible thanks to a connection to the Kolmogorov epsilon-entropy. | method 0.97 | 0.52 | 0.04 | asserted 0.98 |
| s9 | When used in conjunction with the invariance principle of this paper, this connection has other interesting by-products some of which we relate. | interpretation 0.58 | 0.67 | 0.05 | asserted 0.87 |
| s10 | We prove also that viewed as an infinite-dimensional process, the rescaled dynamical random walk converges weakly in D(D([0,1])) to the Ornstein-Uhlenbeck process in C([0,1]). | result 0.97 | 0.95 | 0.29 | asserted 1.00 |
| s11 | For this we assume only that the increments have mean zero and variance one. | limitation 0.76 | 0.73 | 0.06 | asserted 0.66 |
| s12 | In addition, we extend a result of Benjamini, Haggstrom, Peres and Steif (2003) by proving that if the X(i,0)'s are lattice, mean-zero variance-one, and possess 2 + epsilon finite absolute moments for some positive epsilon, then the recurrence of the origin is dynamically stable. | result 0.88 | 0.93 | 0.17 | asserted 1.00 |
| s13 | To prove this we derive a gambler's ruin estimate that is valid for all lattice random walks that have mean zero and finite variance. | method 0.90 | 0.94 | 0.05 | asserted 1.00 |
| s14 | We believe the latter may be of independent interest. | interpretation 0.87 | 0.29 | 0.02 | hedged possibility 0.92 |
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Original abstract
Consider a sequence {X(i,0) : i = 1, ..., n} of i.i.d. random variables. Associate to each X(i,0) an independent mean-one Poisson clock. Every time a clock rings replace that X-variable by an independent copy. In this way, we obtain i.i.d. stationary processes {X(i,t) : t >= 0} (i=1,2, ...) whose invariant distribution is the law of X(1,0). Benjamini, Haggstrom, Peres, and Steif (2003) introduced the dynamical walk S(n,t) = X(1,t) + ... + X(n,t), and proved among other things that the LIL holds for {S(n,t) : n =1,2, ...} simultaneously for all t. In other words, the LIL is dynamically stable. Subsequently, we showed that in the case that the X(i,0)'s are standard normal, the classical integral test is not dynamically stable. Presently, we study the set of times t when {S(n,t) : n=1,2, ...} exceeds a given envelope infinitely often. Our analysis is made possible thanks to a connection to the Kolmogorov epsilon-entropy. When used in conjunction with the invariance principle of this paper, this connection has other interesting by-products some of which we relate. We prove also that viewed as an infinite-dimensional process, the rescaled dynamical random walk converges weakly in D(D([0,1])) to the Ornstein-Uhlenbeck process in C([0,1]). For this we assume only that the increments have mean zero and variance one. In addition, we extend a result of Benjamini, Haggstrom, Peres and Steif (2003) by proving that if the X(i,0)'s are lattice, mean-zero variance-one, and possess 2 + epsilon finite absolute moments for some positive epsilon, then the recurrence of the origin is dynamically stable. To prove this we derive a gambler's ruin estimate that is valid for all lattice random walks that have mean zero and finite variance. We believe the latter may be of independent interest.
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