Modelling Poisson marked point processes using bivariate mixture transition distributions

DOI10.1080/00949655.2012.662683zbMath1453.62634OpenAlexW2039097404MaRDI QIDQ5218877

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Publication date: 6 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2012.662683

zbMATH Keywords

EM algorithmmarked point processesidentifiabilityobservation-driven modelshigh-frequency financial datanegative binomial modelsInternet trafficcontinuous-discrete bivariate distribution modelsPoisson time series models

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (3)

General framework and model building in the class of hidden mixture transition distribution models ⋮ On some aspects of a bivariate alternative zero-inflated logarithmic series distribution ⋮ JOINT MODELING OF CORRELATED TIME DURATIONS AND THEIR MARKS USING A WEIBULL POISSON MARKED POINT PROCESS MIXTURE MODELS

Uses Software

• SAS
• PROC GENMOD

Cites Work

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• Choosing initial values for the EM algorithm for finite mixtures
• On the convergence properties of the EM algorithm
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• Conditionally specified distributions
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• The analysis of packet loss prediction for Gilbert-model with loss rate uplink
• Model-Checking Techniques Based on Cumulative Residuals
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Modeling Flat Stretches, Bursts, and Outliers in Time Series Using Mixture Transition Distribution Models
• On a Mixture Autoregressive Model
• Multivariate Normal Mixtures: A Fast Consistent Method of Moments
• Modeling Marked Point Processes via Bivariate Mixture Transition Distribution Models

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