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On universal estimates for binary renewal processes

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TLDR
In this article, the authors prove universal estimates for the expected time to renewal as well as the conditional distribution of the time-to-renewal distribution of a binary renewal process.
Abstract
A binary renewal process is a stochastic process $\{X_n\}$ taking values in $\{0,1\}$ where the lengths of the runs of 1's between successive zeros are independent. After observing ${X_0,X_1,...,X_n}$ one would like to predict the future behavior, and the problem of universal estimators is to do so without any prior knowledge of the distribution. We prove a variety of results of this type, including universal estimates for the expected time to renewal as well as estimates for the conditional distribution of the time to renewal. Some of our results require a moment condition on the time to renewal and we show by an explicit construction how some moment condition is necessary.

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Citations
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Journal ArticleDOI

Long-Term Prediction Intervals of Time Series

TL;DR: In contrast with the conventional prediction problem in which one predicts a future value given past values of the process, in this setting the number of aggregates can go to infinity with respect to thenumber of available observations.
Journal ArticleDOI

On universal algorithms for classifying and predicting stationary processes

TL;DR: This is a survey of results on universal algorithms for classification and prediction of stationary processes and the forward and the backward prediction problems are discussed with the emphasis being on pointwise results.
Proceedings ArticleDOI

Estimating the residual waiting time for binary stationary time series

TL;DR: A universal estimation scheme for the problem of estimating the residual waiting time until the next occurrence of a zero after observing the first n outputs of a stationary and ergodic binary process is presented.
Journal ArticleDOI

A versatile scheme for predicting renewal times

TL;DR: This note gives a single scheme where the average error is eventually small for all time instants, while the error itself tends to zero along a sequence of stopping times of density one.
References
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Book

The Ergodic Theory of Discrete Sample Paths

TL;DR: In this article, the B-processes Bibliography index is used to find entropy-related properties for restricted classes of B-Processes, including entropy related properties and properties.
Journal ArticleDOI

Inequalities for the $r$th Absolute Moment of a Sum of Random Variables, $1 \leqq r \leqq 2$

TL;DR: In this article, it was shown that (4) is not generally true with $C(r, n) = 1$ even when r.v.'s are independent and have zero means.
Book

Single Orbit Dynamics

TL;DR: In this paper, the authors define single orbit dynamics topological and topological dynamics, ergodicity and unique Ergodicity Ergodic and uniquely ergodic orbits Translation invariant graphs and recurrence patterns in large sets Entropy and disjointness.