Optimal models for the first arrival time distribution function in continuous time — With a special case

1994 ◽  
Vol 10 (2) ◽  
pp. 194-212 ◽  
Author(s):  
Yuanlie Lin ◽  
R. J. Tomkins ◽  
Chunglie Wang
Keyword(s):  
Distribution Function ◽  
Continuous Time ◽  
Time Distribution ◽  
Arrival Time ◽  
Arrival Time Distribution ◽  
First Arrival ◽  
First Arrival Time ◽  
Special Case

SPECTRUM OF COSMIC RAYS WITH ENERGY ABOVE 1017 EV

2005 ◽  
Vol 20 (29) ◽  
pp. 6878-6880 ◽  
Author(s):  
V. P. EGOROVA ◽  
A. V. GLUSHKOV ◽  
A. A. IVANOV ◽  
S. P. KNURENKO ◽  
V. A. KOLOSOV ◽  
...  
Keyword(s):  
Distribution Function ◽  
Energy Spectrum ◽  
Cosmic Rays ◽  
Time Distribution ◽  
Arrival Time ◽  
Lateral Distribution ◽  
Arrival Time Distribution ◽  
Array Data ◽  
Lateral Distribution Function ◽  
High Energies

The energy spectrum of primary cosmic rays with ultra-high energies based on the Yakutsk EAS Array data is presented. For the largest events values of S600 and axis coordinates have been obtained using revised lateral distribution function. The effect of the arrival time distribution at several axis distance on estimated density for Yakutsk and AGASA is considered.

On exponential bounds for the waiting-time distribution function in GI/G/1

1976 ◽  
Vol 13 (2) ◽  
pp. 411-417 ◽  
Author(s):  
R. Bergmann ◽  
D. Stoyan
Keyword(s):  
Distribution Function ◽  
Waiting Time ◽  
Time Distribution ◽  
Arrival Time ◽  
Distribution Functions ◽  
Integral Inequalities ◽  
Arrival Time Distribution ◽  
Waiting Time Distribution ◽  
Exponential Bounds ◽  
Stationary Waiting Time

Exponential bounds for the stationary waiting-time distribution of the type ae–θt are considered. These bounds are obtained by the use of Kingman's method of ‘integral inequalities’. Approximations of Θ and a are given which are useful especially if the service and/or inter-arrival time distribution functions are NBUE or NWUE.

On the Application of Palm Probability for Obtatning Inter-Arrival Time Distribution in Weighted Poisson Process

1983 ◽  
Vol 32 (1-2) ◽  
pp. 111-116 ◽  
Author(s):  
Suddhendu Biswas ◽  
Tapan Kumar Pachal
Keyword(s):  
Poisson Process ◽  
Gamma Distribution ◽  
Time Distribution ◽  
Arrival Time ◽  
Compound Poisson Process ◽  
Arrival Time Distribution ◽  
Compound Poisson ◽  
Palm Probability ◽  
First Arrival

The distribution of the time between the first and the n-th arrival given that the first arrival occurred at T-0 is derived for a compound Poisson process weighted by a Gamma distribution.

On exponential bounds for the waiting-time distribution function in GI/G/1

1976 ◽  
Vol 13 (02) ◽  
pp. 411-417
Author(s):  
R. Bergmann ◽  
D. Stoyan
Keyword(s):  
Distribution Function ◽  
Waiting Time ◽  
Time Distribution ◽  
Arrival Time ◽  
Distribution Functions ◽  
Integral Inequalities ◽  
Arrival Time Distribution ◽  
Waiting Time Distribution ◽  
Exponential Bounds ◽  
Stationary Waiting Time

Exponential bounds for the stationary waiting-time distribution of the type ae–θt are considered. These bounds are obtained by the use of Kingman's method of ‘integral inequalities’. Approximations of Θ and a are given which are useful especially if the service and/or inter-arrival time distribution functions are NBUE or NWUE.

Some Features of Formatting the Arrival Time Distribution

2019 ◽  
pp. 148-161
Author(s):  
Vladimir Chebotarev ◽  
Boris Davydov ◽  
Kseniya Kablukova ◽  
Vadim Gopkalo
Keyword(s):  
Time Distribution ◽  
Arrival Time ◽  
Arrival Time Distribution

First arrival time picking for microseismic data based on DWSW algorithm

2018 ◽  
Vol 22 (4) ◽  
pp. 833-840 ◽  
Author(s):  
Yue Li ◽  
Yue Wang ◽  
Hongbo Lin ◽  
Tie Zhong
Keyword(s):  
Arrival Time ◽  
Microseismic Data ◽  
First Arrival ◽  
First Arrival Time ◽  
Arrival Time Picking

CONTINUOUS-TIME FINANCE AND THE WAITING TIME DISTRIBUTION: MULTIPLE CHARACTERISTIC TIMES

2012 ◽  
Vol 26 (23) ◽  
pp. 1250151 ◽  
Author(s):  
KWOK SAU FA
Keyword(s):  
Distribution Function ◽  
Random Walk ◽  
Waiting Time ◽  
Continuous Time ◽  
Probability Distribution Function ◽  
Time Distribution ◽  
Waiting Time Distribution ◽  
Exponential Functions ◽  
Multiple Characteristic ◽  
Sum Of Products

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.

scholarly journals ANALYSES OF THE AGE OF GENES AND THE FIRST ARRIVAL TIMES IN A FINITE POPULATION

Genetics ◽  
1983 ◽  
Vol 105 (4) ◽  
pp. 1041-1059
Author(s):  
Takeo Maruyama ◽  
Paul A Fuerst
Keyword(s):  
Arrival Time ◽  
Mutant Gene ◽  
Intermediate Frequency ◽  
Lag Phase ◽  
Reversal Theory ◽  
Infinite Allele Model ◽  
First Arrival ◽  
Mean And Variance ◽  
The Mean ◽  
First Arrival Time

ABSTRACT The age of a mutant gene is studied using the infinite allele model in which every mutant is new and selectively neutral. Based on a time reversal theory of Markov processes, we develop a method of mathematical analysis that is considerably simpler for calculating the various statistics of the age than previous methods. Formulas for the mean and variance and for the distribution of age are presented together with some examples of relevance to cases in natural populations.—Theoretical studies of the first arrival time of an allele to a specified frequency, given an initially monomorphic condition of the locus, are presented. It is shown that, beginning with an allele that has frequency p = 1 or an allele with frequency p = 1/2N, there is an initial lag phase in which there is virtually no chance of an allele with a specified intermediate frequency appearing in the population. The distribution of the first arrival time is also presented. The distribution shows several characteristics that are not immediately obvious from a consideration of only the mean and variance of first arrival time. Especially noteworthy is the existence of a very long tail to the distribution. We have also studied the distribution of the age of an allele in the population. Again, the distribution of this measure is shown to be more informative for several questions than are the mean and variance alone.

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