Histogram maximum likelihood estimator in the multiplicative intensity model

DOI10.1016/0304-4149(89)90108-7zbMath0669.62081OpenAlexW2024332523MaRDI QIDQ1118959

Jacek Leśkow, Roman Różański

Publication date: 1989

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4149(89)90108-7

zbMATH Keywords

asymptotic normalitypoint processmaximum likelihood estimatormultiplicative intensity modelConsistencymethod of sievespsi-mixing

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Signal detection and filtering (aspects of stochastic processes) (60G35) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (3)

A note on smoothed estimating functions ⋮ A General Approach To Nonparametric Histogram Estimation ⋮ The impact of stationarity assessment on studies of volatility and value-at-risk.

Cites Work

• Cox's regression model for counting processes: A large sample study
• Convergence of sums of mixing triangular arrays of random vectors with stationary rows
• Inference for earthquake models: A self-correcting model
• Smoothing counting process intensities by means of kernel functions
• Statistical analysis of counting processes
• Inference for thinned point processes, with application to Cox processes
• Maximum likelihood estimation in the multiplicative intensity model via sieves
• Point processes and queues. Martingale dynamics
• Nonparametric maximum likelihood estimation by the method of sieves
• Nonparametric inference for a family of counting processes
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