The maximum and mean of a random length sequence

1992 ◽  
Vol 29 (02) ◽  
pp. 460-466
Author(s):  
Peter J. Haas
Keyword(s):  
Limit Theorem ◽  
Joint Distribution ◽  
Random Number ◽  
Finite Interval ◽  
Random Variables ◽  
Convergence Result ◽  
Interval Length ◽  
Sample Mean ◽  
Maximum Value ◽  
The Mean

We obtain a limit theorem for the joint distribution of the maximum value and sample mean of a random length sequence of independent and identically distributed random variables. This extends a previous bivariate convergence result for fixed length sequences and incidentally yields a new proof of Berman's classical limit theorem for the maximum value of a random number of random variables. Our approach uses a property of record time sequences and leads to probabilistically intuitive proofs. We also consider the partition of a finite interval into a random number of subintervals by the points of a non-delayed renewal process. Using the bivariate convergence result for random length sequences, we establish a limit theorem for the joint distribution of the number and maximum length of the subintervals as the interval length becomes large. This leads to limiting results for the ratio of the maximum to the mean subinterval length. Such results are of interest in connection with a simple model of parallel processing.

The maximum and mean of a random length sequence

1992 ◽  
Vol 29 (2) ◽  
pp. 460-466 ◽  
Author(s):  
Peter J. Haas
Keyword(s):  
Limit Theorem ◽  
Joint Distribution ◽  
Random Number ◽  
Finite Interval ◽  
Random Variables ◽  
Convergence Result ◽  
Interval Length ◽  
Sample Mean ◽  
Maximum Value ◽  
The Mean

We obtain a limit theorem for the joint distribution of the maximum value and sample mean of a random length sequence of independent and identically distributed random variables. This extends a previous bivariate convergence result for fixed length sequences and incidentally yields a new proof of Berman's classical limit theorem for the maximum value of a random number of random variables. Our approach uses a property of record time sequences and leads to probabilistically intuitive proofs. We also consider the partition of a finite interval into a random number of subintervals by the points of a non-delayed renewal process. Using the bivariate convergence result for random length sequences, we establish a limit theorem for the joint distribution of the number and maximum length of the subintervals as the interval length becomes large. This leads to limiting results for the ratio of the maximum to the mean subinterval length. Such results are of interest in connection with a simple model of parallel processing.

Limit theorems and estimation theory for branching processes with an increasing random number of ancestors

1997 ◽  
Vol 34 (2) ◽  
pp. 309-327 ◽  
Author(s):  
J. P. Dion ◽  
N. M. Yanev
Keyword(s):  
Limit Theorem ◽  
Limit Theorems ◽  
Random Number ◽  
Branching Processes ◽  
Asymptotic Properties ◽  
Random Variables ◽  
Transfer Theorem ◽  
Asymptotically Normal ◽  
The Mean ◽  
Watson Process

This paper deals with a Bienaymé-Galton-Watson process having a random number of ancestors. Its asymptotic properties are studied when both the number of ancestors and the number of generations tend to infinity. This yields consistent and asymptotically normal estimators of the mean and the offspring distribution of the process. By exhibiting a connection with the BGW process with immigration, all results can be transported to the immigration case, under an appropriate sampling scheme. A key feature of independent interest is a new limit theorem for sums of a random number of random variables, which extends the Gnedenko and Fahim (1969) transfer theorem.

Limit theorems and estimation theory for branching processes with an increasing random number of ancestors

1997 ◽  
Vol 34 (02) ◽  
pp. 309-327 ◽  
Author(s):  
J. P. Dion ◽  
N. M. Yanev
Keyword(s):  
Limit Theorem ◽  
Limit Theorems ◽  
Random Number ◽  
Branching Processes ◽  
Asymptotic Properties ◽  
Random Variables ◽  
Transfer Theorem ◽  
Asymptotically Normal ◽  
The Mean ◽  
Watson Process

This paper deals with a Bienaymé-Galton-Watson process having a random number of ancestors. Its asymptotic properties are studied when both the number of ancestors and the number of generations tend to infinity. This yields consistent and asymptotically normal estimators of the mean and the offspring distribution of the process. By exhibiting a connection with the BGW process with immigration, all results can be transported to the immigration case, under an appropriate sampling scheme. A key feature of independent interest is a new limit theorem for sums of a random number of random variables, which extends the Gnedenko and Fahim (1969) transfer theorem.

The distribution of the maximum of a random number of random variables with applications

1973 ◽  
Vol 10 (01) ◽  
pp. 122-129 ◽  
Author(s):  
Janos Galambos
Keyword(s):  
Service Time ◽  
Joint Distribution ◽  
Random Number ◽  
Random Variables ◽  
Stochastic Independence ◽  
Fixed Integer ◽  
Number Of Components ◽  
Distribution Of The Maximum ◽  
Distribution Of Elements ◽  
Mild Assumption

The asymptotic distribution of the maximum of a random number of random variables taken from the model below is shown to be the same as when their number is a fixed integer. Applications are indicated to determine the service time of a system of a large number of components, when the number of components to be serviced is not known in advance. A much slighter assumption is made than the stochastic independence of the periods of time needed for servicing the different components. In our model we assume that the random variables can be grouped into a number of subcollections with the following properties: (i) the random variables taken from different groups are asymptotically independent, (ii) the largest number of elements in a subgroup is of smaller order than the overall number of random variables. In addition, a very mild assumption is made for the joint distribution of elements from the same group.

On Complete Convergence of the Sum of a Random Number of Random Variables

1988 ◽  
Vol 37 (3-4) ◽  
pp. 161-170 ◽  
Author(s):  
Andre Adler
Keyword(s):  
Joint Distribution ◽  
Complete Convergence ◽  
Random Number ◽  
Random Variables ◽  
Random Index

Complete convergence of the normed random index sum of randomvariables of the form [Formula: see text] is established. While the random variables { Xn, n ⩾ 1} are i.i.d., no assumptions are placed on the joint distribution of the indices { Tn, n ⩾ 1}.

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