Pages that link to "Item:Q4563757"
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The following pages link to FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM (Q4563757):
Displaying 35 items.
- On modeling left-truncated loss data using mixtures of distributions (Q124235) (← links)
- On mixed Erlang reinsurance risk: aggregation, capital allocation and default risk (Q303732) (← links)
- Using the EM algorithm for inference in a mixture of distributions with censored but partially identifiable data (Q1019905) (← links)
- Fitting the Erlang mixture model to data via a GEM-CMM algorithm (Q1643834) (← links)
- Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions (Q1681087) (← links)
- On the consistency of penalized MLEs for Erlang mixtures (Q1726761) (← links)
- On generalized log-Moyal distribution: a new heavy tailed size distribution (Q1742726) (← links)
- A stochastic EM algorithm for mixtures with censored data (Q1895370) (← links)
- EM algorithms for multivariate Gaussian mixture models with truncated and censored data (Q1927060) (← links)
- A class of mixture of experts models for general insurance: theoretical developments (Q2010898) (← links)
- On the mixtures of length-biased Weibull distributions for loss severity modeling (Q2131911) (← links)
- Ruin probability for finite Erlang mixture claims via recurrence sequences (Q2157431) (← links)
- Mixture modeling of data with multiple partial right-censoring levels (Q2201324) (← links)
- Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models (Q2212142) (← links)
- Truncated, censored, and actuarial payment-type moments for robust fitting of a single-parameter Pareto distribution (Q2223881) (← links)
- Multivariate mixtures of Erlangs for density estimation under censoring (Q2398460) (← links)
- Modeling loss data using mixtures of distributions (Q2520467) (← links)
- Gamma mixture density networks and their application to modelling insurance claim amounts (Q2665857) (← links)
- Strong consistency of the MLE under two-parameter Gamma mixture models with a structural scale parameter (Q2673361) (← links)
- Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models (Q2682986) (← links)
- EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION (Q4563784) (← links)
- A MARKED COX MODEL FOR THE NUMBER OF IBNR CLAIMS: ESTIMATION AND APPLICATION (Q4972122) (← links)
- Using Model Averaging to Determine Suitable Risk Measure Estimates (Q5027908) (← links)
- ROBUST ESTIMATION OF LOSS MODELS FOR LOGNORMAL INSURANCE PAYMENT SEVERITY DATA (Q5152546) (← links)
- GENERALIZING THE LOG-MOYAL DISTRIBUTION AND REGRESSION MODELS FOR HEAVY-TAILED LOSS DATA (Q5157764) (← 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)
- Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with conditional value-at-risk (CVaR) constraint (Q6088771) (← links)
- Ruin probabilities as functions of the roots of a polynomial (Q6166247) (← links)
- Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks (Q6173881) (← links)
- A new class of composite GBII regression models with varying threshold for modeling heavy-tailed data (Q6573814) (← links)
- Loss modeling with the size-biased lognormal mixture and the entropy regularized EM algorithm (Q6573825) (← links)
- Loss modeling with many-parameter distributions (Q6632355) (← links)
- A novel M-Lognormal–Burr regression model with varying threshold for modeling heavy-tailed claim severity data (Q6662598) (← links)
