Pages that link to "Item:Q1643834"
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The following pages link to Fitting the Erlang mixture model to data via a GEM-CMM algorithm (Q1643834):
Displaying 15 items.
- Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions (Q1681087) (← links)
- Mixture modeling of data with multiple partial right-censoring levels (Q2201324) (← links)
- Fitting multivariate Erlang mixtures to data: a roughness penalty approach (Q2222165) (← links)
- Multivariate mixtures of Erlangs for density estimation under censoring (Q2398460) (← links)
- Gamma mixture density networks and their application to modelling insurance claim amounts (Q2665857) (← links)
- The multivariate mixed negative binomial regression model with an application to insurance a posteriori ratemaking (Q2665879) (← links)
- Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models (Q2682986) (← links)
- FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM (Q4563757) (← links)
- EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION (Q4563784) (← links)
- A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES (Q4972120) (← links)
- A New Class of Severity Regression Models with an Application to IBNR Prediction (Q5165010) (← links)
- Multivariate Cox Hidden Markov models with an application to operational risk (Q5193491) (← links)
- Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models (Q5877347) (← links)
- Mixture Composite Regression Models with Multi-type Feature Selection (Q6110498) (← links)
- Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks (Q6173881) (← links)
