Experimental Design and Statistical Inference for Cluster Point Processes– with Applications to the Fruit Dispersion of Anemochorous Forest Trees

2003 ◽  
Vol 45 (8) ◽  
pp. 1006-1022 ◽  
Author(s):  
W. Näther ◽  
K. Wälder
Keyword(s):  
Experimental Design ◽  
Statistical Inference ◽  
Point Processes ◽  
Cluster Point ◽  
Forest Trees

Convergence to equilibrium in a traffic model with restricted passing

1979 ◽  
Vol 16 (4) ◽  
pp. 881-889 ◽  
Author(s):  
Hans Dieter Unkelbach
Keyword(s):  
Poisson Process ◽  
Limit Theorems ◽  
Point Processes ◽  
Road Traffic ◽  
Poisson Processes ◽  
Traffic Model ◽  
Cluster Point ◽  
Convergence To Equilibrium ◽  
Constant Intensity

A road traffic model with restricted passing, formulated by Newell (1966), is described by conditional cluster point processes and analytically handled by generating functionals of point processes.The traffic distributions in either space or time are in equilibrium, if the fast cars form a Poisson process with constant intensity combined with Poisson-distributed queues behind the slow cars (Brill (1971)). It is shown that this state of equilibrium is stable, which means that this state will be reached asymptotically for general initial traffic distributions. Furthermore the queues behind the slow cars dissolve asymptotically like independent Poisson processes with diminishing rate, also independent of the process of non-queuing cars. To get these results limit theorems for conditional cluster point processes are formulated.

Optimal stopping and cluster point processes

2003 ◽  
Vol 21 (3-2003) ◽  
pp. 261-282 ◽  
Author(s):  
Robert Kühne ◽  
Ludger Rüschendorf
Keyword(s):  
Optimal Stopping ◽  
Point Processes ◽  
Cluster Point

Point Processes and Their Statistical Inference.

1993 ◽  
Vol 88 (423) ◽  
pp. 1168
Author(s):  
Richard F. Serfozo ◽  
Alan F. Karr
Keyword(s):  
Statistical Inference ◽  
Point Processes

scholarly journals Statistical Inference for the Weibull Distribution Based on δ-Record Data

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Raúl Gouet ◽  
F. Javier López ◽  
Lina Maldonado ◽  
Gerardo Sanz
Keyword(s):  
Maximum Likelihood ◽  
Experimental Design ◽  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Statistical Inference ◽  
Numerical Computation ◽  
Weibull Model ◽  
Rainfall Series ◽  
Estimation Of Parameters ◽  
Record Data

We consider the maximum likelihood and Bayesian estimation of parameters and prediction of future records of the Weibull distribution from δ -record data, which consists of records and near-records. We discuss existence, consistency and numerical computation of estimators and predictors. The performance of the proposed methodology is assessed by Montecarlo simulations and the analysis of monthly rainfall series. Our conclusion is that inferences for the Weibull model, based on δ -record data, clearly improve inferences based solely on records. This methodology can be recommended, more so as near-records can be collected along with records, keeping essentially the same experimental design.

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