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Mixture Composite Regression Models with Multi-type Feature Selection - MaRDI portal

Mixture Composite Regression Models with Multi-type Feature Selection (Q6110498)

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scientific article; zbMATH DE number 7720847

Language	Label	Description	Also known as
English	Mixture Composite Regression Models with Multi-type Feature Selection	scientific article; zbMATH DE number 7720847

Statements

scholarly article

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Mixture Composite Regression Models with Multi-type Feature Selection (English)

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Mario V. Wüthrich

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North American Actuarial Journal

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publication date

1 August 2023

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full work available at URL

https://arxiv.org/abs/2103.07200

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MaRDI profile type

MaRDI publication profile

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Modeling loss data using composite models

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On modeling left-truncated loss data using mixtures of distributions

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Modeling claims data with composite Stoppa models

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Modeling actuarial data with a composite lognormal-Pareto model

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Sparse regression with multi-type regularized feature modeling

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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Regression Modeling with Actuarial and Financial Applications

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A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES

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A New Class of Severity Regression Models with an Application to IBNR Prediction

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Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models

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Extending composite loss models using a general framework of advanced computational tools

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Fitting the Erlang mixture model to data via a GEM-CMM algorithm

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A non-convex regularization approach for stable estimation of loss development factors

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New estimation and feature selection methods in mixture-of-experts models

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Variable Selection in Finite Mixture of Regression Models

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Severity modeling of extreme insurance claims for tariffication

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Modeling loss data using mixtures of distributions

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New composite models for the Danish fire insurance data

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A uniform framework for the combination of penalties in generalized structured models

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A GENERALISED PROPERTY EXPOSURE RATING FRAMEWORK THAT INCORPORATES SCALE-INDEPENDENT LOSSES AND MAXIMUM POSSIBLE LOSS UNCERTAINTY

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Composite Lognormal–Pareto model with random threshold

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Heavy-Tail Phenomena

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Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions

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On composite lognormal-Pareto models

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Modeling with Weibull-Pareto Models

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Efficient calculation of the NPMLE of a mixing distribution for mixtures of exponentials

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OPTIMAL BONUS-MALUS SYSTEMS USING FINITE MIXTURE MODELS

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Bonus-Malus Systems with Two-Component Mixture Models Arising from Different Parametric Families

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The Adaptive Lasso and Its Oracle Properties

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Identifiers

zbMATH Open document ID

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10.1080/10920277.2022.2099426

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:6110498

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