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Modeling bimodal discrete data using Conway-Maxwell-Poisson mixture models

Sur, Pragya; Shmueli, Galit; Bose, Smarajit; Dubey, Paromita - In: Journal of business & economic statistics : JBES ; a … 33 (2015) 3, pp. 352-365

Persistent link: https://www.econbiz.de/10011390190

Downturn loss given default : mixture distribution estimation

Calabrese, Raffaella - In: European journal of operational research : EJOR 237 (2014) 1, pp. 271-277

Persistent link: https://www.econbiz.de/10010378609

Multivariate mixtures of normal distributions : properties, random vector generation, fitting, and as models of market daily changes

Wang, Jin; Taaffe, Michael R. - In: INFORMS journal on computing : JOC 27 (2015) 2, pp. 193-203

Persistent link: https://www.econbiz.de/10011291337

Inverse Gaussian processes with correlated random effects for multivariate degradation modeling

Fang, Guanqi; Pan, Rong; Wang, Yukun - In: European journal of operational research : EJOR 300 (2022) 3, pp. 1177-1193

Persistent link: https://www.econbiz.de/10013207332

Modelling healthcare demand count data with excessive zeros and overdispersion

Park, Myung Hyun; Kim, Joseph H. T. - In: Global economic review 50 (2021) 4, pp. 358-381

Persistent link: https://www.econbiz.de/10012694162

On discriminating between lognormal and Pareto tail : a mixture-based approach

Bee, Marco - 2020

Persistent link: https://www.econbiz.de/10012416592

An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion

Tzougas, George; Karlis, Dimitris - In: ASTIN bulletin : the journal of the International … 50 (2020) 2, pp. 555-583

Persistent link: https://www.econbiz.de/10012243386

EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion : an application to insurance ratemaking

Tzougas, George - In: Risks : open access journal 8 (2020) 3/97, pp. 1-23

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the...

Persistent link: https://www.econbiz.de/10012293316

Efficient estimation of erlang mixtures using iSCAD penalty with insurance application

Yin, Cuihong; Lin, X. Sheldon - In: Astin bulletin : the journal of the International … 46 (2016) 3, pp. 779-799

Persistent link: https://www.econbiz.de/10011670006

Modeling loss data using mixtures of distributions

Miljkovic, Tatjana; Grün, Bettina - In: Insurance / Mathematics & economics 70 (2016), pp. 387-396

Persistent link: https://www.econbiz.de/10011597334