scholarly journals Run Statistics Defined on the Multicolor URN Model

2008 ◽  
Vol 45 (4) ◽  
pp. 1007-1023 ◽  
Author(s):  
Serkan Eryilmaz
Keyword(s):  
Type Systems ◽  
Exact Distribution ◽  
Sampling Scheme ◽  
Combinatorial Approach ◽  
Urn Model ◽  
Joint Distributions ◽  
Start Up ◽  
Exact Distributions ◽  
Binary Trials ◽  
Success Run

Recently, Makri, Philippou and Psillakis (2007b) studied the exact distribution of success run statistics defined on an urn model. They derived the exact distributions of various success run statistics for a sequence of binary trials generated by the Pólya-Eggenberger sampling scheme. In our study we derive the joint distributions of run statistics defined on the multicolor urn model using a simple unified combinatorial approach and extend some of the results of Makri, Philippou and Psillakis (2007b). As a consequence of our results, we obtain the joint distributions of success and failure runs defined on the two-color urn model. The results enable us to compute the characteristics of particular consecutive-type systems and start-up demonstration tests.

scholarly journals Run Statistics Defined on the Multicolor URN Model

2008 ◽  
Vol 45 (04) ◽  
pp. 1007-1023 ◽  
Author(s):  
Serkan Eryilmaz
Keyword(s):  
Type Systems ◽  
Exact Distribution ◽  
Sampling Scheme ◽  
Combinatorial Approach ◽  
Urn Model ◽  
Joint Distributions ◽  
Start Up ◽  
Exact Distributions ◽  
Binary Trials ◽  
Success Run

Recently, Makri, Philippou and Psillakis (2007b) studied the exact distribution of success run statistics defined on an urn model. They derived the exact distributions of various success run statistics for a sequence of binary trials generated by the Pólya-Eggenberger sampling scheme. In our study we derive the joint distributions of run statistics defined on the multicolor urn model using a simple unified combinatorial approach and extend some of the results of Makri, Philippou and Psillakis (2007b). As a consequence of our results, we obtain the joint distributions of success and failure runs defined on the two-color urn model. The results enable us to compute the characteristics of particular consecutive-type systems and start-up demonstration tests.

Joint distributions of circular runs of various lengths based on multi-colour Pólya urn model

2012 ◽  
Vol 49 (3) ◽  
pp. 283-300
Author(s):  
Sonali Bhattacharya
Keyword(s):  
Probability Generating Function ◽  
Discrete Distribution ◽  
Time Sharing ◽  
Urn Model ◽  
Joint Distributions ◽  
Pólya Urn ◽  
Start Up ◽  
Polya Urn ◽  
Pólya Urn Model ◽  
First Time

In this paper, we have used Eryilmaz’s (2008) multi-colour Pólya urn model to obtain joint distributions of runs of t-types of exact lengths (k1, k2, …, kt), at least lengths (k1, k2, …, kt), non-overlapping runs of lengths (k1, k2, … kt) and overlapping runs of lengths (k1, k2, … kt) when counting of runs is done in a circular setup. We have also derived joint distributions of longest runs of various types under similar conditions. Distributions of runs have found applications in fields of reliability of consecutive-k-out-of n: F system, consecutive k-out-of-r-from n: F system, start-up demonstration test, molecular biology, radar detection, time sharing systems and quality control. The literature is profound in discussion of marginal distribution and joint distribution of runs of various types under linear and circular setup using techniques like urn model with balls of two or more colours, probability generating function and compounding discrete distribution with suitable beta functions. Through this paper for first time effort been made to discuss joint distributions of runs of various lengths and types using Multi-colour urn model.

Lengths of runs and waiting time distributions by using Pólya-Eggenberger sampling scheme

2002 ◽  
Vol 39 (3-4) ◽  
pp. 309-332 ◽  
Author(s):  
K. Sen ◽  
Manju L. Agarwal ◽  
S. Chakraborty
Keyword(s):  
Waiting Time ◽  
Lattice Path ◽  
Sampling Scheme ◽  
Bernoulli Trials ◽  
Success Runs ◽  
Waiting Time Distributions ◽  
Joint Distributions ◽  
First Occurrence

In this paper, joint distributions of number of success runs of length k and number of failure runs of length k' are obtained by using combinatorial techniques including lattice path approach under Pólya-Eggenberger model. Some of its particular cases, for different values of the parameters, are derived. Sooner and later waiting time problems and joint distributions of number of success runs of various types until first occurrence of consecutive success runs of specified length under the model are obtained. The sooner and later waiting time problems for Bernoulli trials (see Ebneshahrashoob and Sobel [3]) and joint distributions of the type discussed by Uchiada and Aki [11] are shown as particular cases. Assuming Ln and Sn to be the lengths of longest and smallest success runs, respectively, in a sample of size n drawn by Pólya-Eggenberger sampling scheme, the joint distributions of Ln and  Sn as well as distribution of M=max(Ln,Fn)n, where Fn is the length of longest failure run, are also  obtained.

scholarly journals Success run statistics defined on an urn model

2007 ◽  
Vol 39 (04) ◽  
pp. 991-1019 ◽  
Author(s):  
Frosso S. Makri ◽  
Andreas N. Philippou ◽  
Zaharias M. Psillakis
Keyword(s):  
Joint Probability ◽  
Nonparametric Tests ◽  
Distribution Functions ◽  
Binary Sequences ◽  
Binomial Coefficients ◽  
Joint Probability Distribution ◽  
Urn Model ◽  
Success Runs ◽  
Mean Values ◽  
Success Run

Statistics denoting the numbers of success runs of length exactly equal and at least equal to a fixed length, as well as the sum of the lengths of success runs of length greater than or equal to a specific length, are considered. They are defined on both linearly and circularly ordered binary sequences, derived according to the Pólya-Eggenberger urn model. A waiting time associated with the sum of lengths statistic in linear sequences is also examined. Exact marginal and joint probability distribution functions are obtained in terms of binomial coefficients by a simple unified combinatorial approach. Mean values are also derived in closed form. Computationally tractable formulae for conditional distributions, given the number of successes in the sequence, useful in nonparametric tests of randomness, are provided. The distribution of the length of the longest success run and the reliability of certain consecutive systems are deduced using specific probabilities of the studied statistics. Numerical examples are given to illustrate the theoretical results.

Some exact distributions of a last one-sided exit time in the simple random walk

1986 ◽  
Vol 23 (02) ◽  
pp. 332-340
Author(s):  
Chern-Ching Chao ◽  
John Slivka
Keyword(s):  
Random Walk ◽  
Positive Integer ◽  
Exit Time ◽  
Random Variables ◽  
Random Variable ◽  
Simple Random Walk ◽  
Exact Distribution ◽  
Partial Sum ◽  
Exact Distributions ◽  
Special Case

For each positive integer n, let Sn be the nth partial sum of a sequence of i.i.d. random variables which assume the values +1 and −1 with respective probabilities p and 1 – p, having mean μ= 2p − 1. The exact distribution of the random variable , where sup Ø= 0, is given for the case that λ > 0 and μ+ λ= k/(k + 2) for any non-negative integer k. Tables to the 99.99 percentile of some of these distributions, as well as a limiting distribution, are given for the special case of a symmetric simple random walk (p = 1/2).

scholarly journals Near-Exact Distributions for Likelihood Ratio Statistics Used in the Simultaneous Test of Conditions on Mean Vectors and Patterns of Covariance Matrices

2016 ◽  
Vol 2016 ◽  
pp. 1-25 ◽  
Author(s):  
Carlos A. Coelho ◽  
Filipe J. Marques ◽  
Sandra Oliveira
Keyword(s):  
Covariance Matrix ◽  
Likelihood Ratio ◽  
Exact Distribution ◽  
Covariance Matrices ◽  
Distribution Functions ◽  
Small Samples ◽  
Chi Square ◽  
Exact Distributions ◽  
Mean Vectors ◽  
Likelihood Ratio Statistics

The authors address likelihood ratio statistics used to test simultaneously conditions on mean vectors and patterns on covariance matrices. Tests for conditions on mean vectors, assuming or not a given structure for the covariance matrix, are quite common, since they may be easily implemented. But, on the other hand, the practical use of simultaneous tests for conditions on the mean vectors and a given pattern for the covariance matrix is usually hindered by the nonmanageability of the expressions for their exact distribution functions. The authors show the importance of being able to adequately factorize the c.f. of the logarithm of likelihood ratio statistics in order to obtain sharp and highly manageable near-exact distributions, or even the exact distribution in a highly manageable form. The tests considered are the simultaneous tests of equality or nullity of means and circularity, compound symmetry, or sphericity of the covariance matrix. Numerical studies show the high accuracy of the near-exact distributions and their adequacy for cases with very small samples and/or large number of variables. The exact and near-exact quantiles computed show how the common chi-square asymptotic approximation is highly inadequate for situations with small samples or large number of variables.

Exact distributions of kin numbers in a Galton-Watson process

1982 ◽  
Vol 19 (04) ◽  
pp. 767-775 ◽  
Author(s):  
A. Joffe ◽  
W. A. O'n. Waugh
Keyword(s):  
Theoretical Investigation ◽  
Special Group ◽  
Joint Distributions ◽  
Exact Distributions ◽  
Number Problem ◽  
Watson Process ◽  
The Relationship

The kin number problem involves the relationship between sibship sizes and offspring numbers, and also numbers of relatives of other degrees of affinity of a random member of a population, to be called Ego. The problem has been well known to demographers for some time, but results obtained only gave expected numbers. Recently a study of it, based on the Galton-Watson process, was made, with a view to obtaining joint distributions (Waugh (1981)). In the latter study it was assumed that the population was large, and thus some of the results obtained were approximations. In the present paper exact distributions are obtained, for any size of population. This can be of use in applications, where the population considered may be a small, isolated tribe or other special group. As a theoretical investigation, it replaces some heuristic arguments with limiting properties that are intrinsic to the process and it makes it possible to evaluate the previous approximations.

scholarly journals ON THE DISTRIBUTION OF MAXIMAL PERCENTAGES IN PÓLYA'S URN

2016 ◽  
Vol 31 (3) ◽  
pp. 357-365
Author(s):  
Saralees Nadarajah
Keyword(s):  
Closed Form ◽  
Computational Efficiency ◽  
Exact Distribution ◽  
Applied Probability ◽  
Urn Model ◽  
Pólya’S Urn

Schulte-Geers and Stadje [Journal of Applied Probability, 2015, 52: 180–190] gave several closed form expressions for the exact distribution of the all-time maximal percentage in Pólya's urn model. But all these expressions corresponded to an integer parameter taking the value 1. Here, we derive much more general closed form expressions applicable for all possible values of the integer parameter. We also illustrate their computational efficiency.

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