scholarly journals Random Power Series in Q p , 0 Spaces

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Haiyin Li ◽  
Yan Wu
Keyword(s):  
Power Series ◽  
Probability Space ◽  
Random Variables ◽  
Random Power Series ◽  
Independent Identically Distributed

Aulaskari et al. proved if 0 < p < 1 and ε n is sequence of independent, identically distributed Rademacher random variables on a probability space, then the condition Σ n = 0 ∞ n 1 − p a n 2 < ∞ implies that the random power series R f z = ∑ n = 0 ∞ a n ε n z n ∈ Q p almost surely. In this paper, we improve this result showing that the condition Σ n = 0 ∞ n 1 − p a n 2 < ∞ actually implies R f ∈ Q p , 0 almost surely.

Most Power Series have Radius of Convergence 0 or 1*

1975 ◽  
Vol 18 (1) ◽  
pp. 39-40
Author(s):  
J. J. F. Fournier ◽  
P. M. Gauthier
Keyword(s):  
Analytic Function ◽  
Power Series ◽  
Complex Plane ◽  
Probability Space ◽  
Random Variables ◽  
Radius Of Convergence ◽  
Independent Random Variables ◽  
Natural Boundary ◽  
Random Power Series

Consider a random power series Σ0∞ cn zn, that is, with coefficients {cn}0∞ chosen independently at random from the complex plane. What is the radius of convergence of such a series likely to be?One approach to this question is to let the {cn}0∞ be independent random variables on some probability space. It turns out that, with probability one, the radius of convergence is constant. Moreover, if the cn are symmetric and have the same distribution, then the circle of convergence is almost surely a natural boundary for the analytic function given by the power series (See [1, Ch. IV, Section 3]). Our treatment of the question will be elementary and will not use these facts.

On the law of the iterated logarithm for inter-record times

1970 ◽  
Vol 7 (02) ◽  
pp. 432-439 ◽  
Author(s):  
William E. Strawderman ◽  
Paul T. Holmes
Keyword(s):  
Distribution Function ◽  
Probability Space ◽  
Continuous Distribution ◽  
Random Variables ◽  
Continuous Distribution Function ◽  
Record Times ◽  
Iterated Logarithm ◽  
The Law ◽  
Image Position ◽  
Independent Identically Distributed

Let X 1, X2, X 3 , ··· be independent, identically distributed random variables on a probability space (Ω, F, P); and with a continuous distribution function. Let the sequence of indices {Vr } be defined as Also define The following theorem is due to Renyi [5].

Power series methods and almost sure convergence

1993 ◽  
Vol 113 (1) ◽  
pp. 195-204 ◽  
Author(s):  
Rüdiger Kiesel
Keyword(s):  
Power Series ◽  
Probability Space ◽  
Random Variables ◽  
Almost Sure Convergence ◽  
Power Series Methods

Let (Ω, Σ, P) be a probability space and suppose that all random variables are defined on this space.

The distribution of the values of a random power series in the unit disk

1983 ◽  
Vol 94 (3-4) ◽  
pp. 251-263 ◽  
Author(s):  
Matthias Jakob ◽  
A. C. Offord
Keyword(s):  
Power Series ◽  
Unit Disk ◽  
Random Variables ◽  
Equal Probability ◽  
Independent Random Variables ◽  
Random Power Series ◽  
The Family ◽  
Unit Radius ◽  
Image Position ◽  
Almost All

SynopsisThis is a study of the family of power series where Σ αnZn has unit radius of convergence and the εn are independent random variables taking the values ±1 with equal probability. It is shown that ifthen almost all these power series take every complex value infinitely often in the unit disk.

On the law of the iterated logarithm for inter-record times

1970 ◽  
Vol 7 (2) ◽  
pp. 432-439 ◽  
Author(s):  
William E. Strawderman ◽  
Paul T. Holmes
Keyword(s):  
Distribution Function ◽  
Probability Space ◽  
Continuous Distribution ◽  
Random Variables ◽  
Continuous Distribution Function ◽  
Record Times ◽  
Iterated Logarithm ◽  
The Law ◽  
Image Position ◽  
Independent Identically Distributed

Let X1, X2, X3, ··· be independent, identically distributed random variables on a probability space (Ω, F, P); and with a continuous distribution function. Let the sequence of indices {Vr} be defined as Also define The following theorem is due to Renyi [5].

scholarly journals Lifetime Distributions and their Approximation in Reliability of Serial/Parallel Networks

2020 ◽  
Vol 28 (2) ◽  
pp. 161-172
Author(s):  
Alexei Leahu ◽  
Veronica Andrievschi-Bagrin
Keyword(s):  
Power Series ◽  
Limit Theorems ◽  
Random Number ◽  
Random Variables ◽  
Lifetime Distributions ◽  
Parallel Networks ◽  
Independent Identically Distributed

AbstractIn this paper we present limit theorems for lifetime distributions connected with network’s reliability as distributions of random variables(r.v.) min(Y1, Y2,..., YM) and max(Y1, Y2,..., YM ), where Y1, Y2,..., are independent, identically distributed random variables (i.i.d.r.v.), M being Power Series Distributed (PSD) r.v. independent of them and, at the same time, Yk, k = 1, 2, ..., being a sum of non-negative, i.i.d.r.v. in a Pascal distributed random number.

scholarly journals Some results on the span of families of Banach valued independent, random variables

1991 ◽  
Vol 14 (2) ◽  
pp. 381-384
Author(s):  
Rohan Hemasinha
Keyword(s):  
Banach Space ◽  
Probability Space ◽  
Linear Span ◽  
Random Variables ◽  
Isomorphic Copy ◽  
Independent Random Variables ◽  
Closed Linear Span

LetEbe a Banach space, and let(Ω,ℱ,P)be a probability space. IfL1(Ω)contains an isomorphic copy ofL1[0,1]then inLEP(Ω)(1≤P<∞), the closed linear span of every sequence of independent,Evalued mean zero random variables has infinite codimension. IfEis reflexive orB-convex and1<P<∞then the closed(in LEP(Ω))linear span of any family of independent,Evalued, mean zero random variables is super-reflexive.

A Note on the Accuracy of Normal Approximation of Random Quantities

2021 ◽  
Vol 73 (1) ◽  
pp. 62-67
Author(s):  
Ibrahim A. Ahmad ◽  
A. R. Mugdadi
Keyword(s):  
Sample Size ◽  
Order Statistic ◽  
Normal Approximation ◽  
Order Approximation ◽  
Random Variables ◽  
Random Variable ◽  
Limiting Distribution ◽  
Exact Order ◽  
Fixed Sample ◽  
Independent Identically Distributed

For a sequence of independent, identically distributed random variable (iid rv's) [Formula: see text] and a sequence of integer-valued random variables [Formula: see text], define the random quantiles as [Formula: see text], where [Formula: see text] denote the largest integer less than or equal to [Formula: see text], and [Formula: see text] the [Formula: see text]th order statistic in a sample [Formula: see text] and [Formula: see text]. In this note, the limiting distribution and its exact order approximation are obtained for [Formula: see text]. The limiting distribution result we obtain extends the work of several including Wretman[Formula: see text]. The exact order of normal approximation generalizes the fixed sample size results of Reiss[Formula: see text]. AMS 2000 subject classification: 60F12; 60F05; 62G30.

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