Pages that link to "Item:Q2010898"
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The following pages link to A class of mixture of experts models for general insurance: theoretical developments (Q2010898):
Displaying 15 items.
- Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models (Q2212142) (← links)
- Fitting multivariate Erlang mixtures to data: a roughness penalty approach (Q2222165) (← 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)
- Frequency-severity experience rating based on latent Markovian risk profiles (Q2682996) (← links)
- Delta Boosting Machine with Application to General Insurance (Q4689973) (← links)
- Two-step risk analysis in insurance ratemaking (Q4959365) (← 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)
- Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models (Q5877347) (← links)
- Bivariate Mixed Poisson Regression Models with Varying Dispersion (Q6110489) (← links)
- Robust claim frequency modeling through phase-type mixture-of-experts regression (Q6116750) (← links)
- Phase-type mixture-of-experts regression for loss severities (Q6156007) (← links)
- Leveraging Weather Dynamics in Insurance Claims Triage Using Deep Learning (Q6567876) (← links)
- A new class of composite GBII regression models with varying threshold for modeling heavy-tailed data (Q6573814) (← links)
