Pages that link to "Item:Q2398460"
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The following pages link to Multivariate mixtures of Erlangs for density estimation under censoring (Q2398460):
Displaying 13 items.
- 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)
- Fitting multivariate Erlang mixtures to data: a roughness penalty approach (Q2222165) (← links)
- Modeling loss data using mixtures of distributions (Q2520467) (← links)
- Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of conditional tail expectation (Q2665868) (← links)
- A Variational Bayesian Approach for Estimating Parameters of a Mixture of Erlang Distribution (Q2786223) (← links)
- Estimators based on data-driven generalized weighted Cramér-von Mises distances under censoring -- with applications to mixture models (Q2911655) (← links)
- A Reconciliation of the Top-Down and Bottom-Up Approaches to Risk Capital Allocations: Proportional Allocations Revisited (Q3385437) (← 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)
- Authors’ Reply: On the Class of Erlang Mixtures with Risk Theoretic Applications - Discussion by David C. M. Dickson; Howard R. Waters (Q5019732) (← links)
- Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models (Q5877347) (← links)
