Using multiple random index dates with the reverse Waiting Time Distribution improves precision of estimated prescription durations

Author(s):  
Katrine Bødkergaard ◽  
Randi Marie Selmer ◽  
Jesper Hallas ◽  
Lars Jøran Kjerpeseth ◽  
Eva Skovlund ◽  
...  
2020 ◽  
Vol 29 (9) ◽  
pp. 1072-1078
Author(s):  
Katrine Bødkergaard ◽  
Randi M. Selmer ◽  
Jesper Hallas ◽  
Lars J. Kjerpeseth ◽  
Anton Pottegård ◽  
...  

1980 ◽  
Vol 17 (3) ◽  
pp. 814-821 ◽  
Author(s):  
J. G. Shanthikumar

Some properties of the number of up- and downcrossings over level u, in a special case of regenerative processes are discussed. Two basic relations between the density functions and the expected number of upcrossings of this process are derived. Using these reults, two examples of controlled M/G/1 queueing systems are solved. Simple relations are derived for the waiting time distribution conditioned on the phase of control encountered by an arriving customer. The Laplace-Stieltjes transform of the distribution function of the waiting time of an arbitrary customer is also derived for each of these two examples.


2021 ◽  
Author(s):  
Yosia I Nurhan ◽  
Jay Robert Johnson ◽  
Jonathan R Homan ◽  
Simon Wing

2012 ◽  
Vol 26 (23) ◽  
pp. 1250151 ◽  
Author(s):  
KWOK SAU FA

In this paper, we model the tick-by-tick dynamics of markets by using the continuous-time random walk (CTRW) model. We employ a sum of products of power law and stretched exponential functions for the waiting time probability distribution function; this function can fit well the waiting time distribution for BUND futures traded at LIFFE in 1997.


2012 ◽  
Vol 45 (6) ◽  
pp. 457-462 ◽  
Author(s):  
Chuan Shi ◽  
Stanley B. Gershwin

2002 ◽  
Vol 39 (03) ◽  
pp. 619-629 ◽  
Author(s):  
Gang Uk Hwang ◽  
Bong Dae Choi ◽  
Jae-Kyoon Kim

We consider a discrete-time queueing system with the discrete autoregressive process of order 1 (DAR(1)) as an input process and obtain the actual waiting time distribution and the virtual waiting time distribution. As shown in the analysis, our approach provides a natural numerical algorithm to compute the waiting time distributions, based on the theory of the GI/G/1 queue, and consequently we can easily investigate the effect of the parameters of the DAR(1) on the waiting time distributions. We also derive a simple approximation of the asymptotic decay rate of the tail probabilities for the virtual waiting time in the heavy traffic case.


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