Mixture modeling of data with multiple partial right-censoring levels
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Publication:2201324
DOI10.1007/s11634-020-00391-xzbMath1459.62183OpenAlexW3018503926MaRDI QIDQ2201324
Semhar Michael, Tatjana Miljkovic, Volodymyr Melnykov
Publication date: 29 September 2020
Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11634-020-00391-x
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Censored data models (62N01) Actuarial mathematics (91G05)
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- On modeling left-truncated loss data using mixtures of distributions
- Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Estimating the dimension of a model
- Fitting bivariate loss distributions with copulas
- Bayesian model averaging: A tutorial. (with comments and a rejoinder).
- Fitting the Erlang mixture model to data via a GEM-CMM algorithm
- Consistent test for parametric models with right-censored data using projections
- A stochastic EM algorithm for mixtures with censored data
- EM algorithms for multivariate Gaussian mixture models with truncated and censored data
- Left truncated and right censored Weibull data and likelihood inference with an illustration
- Likelihood inference for lognormal data with left truncation and right censoring with an illustration
- Modeling loss data using composite models
- An effective strategy for initializing the EM algorithm in finite mixture models
- Modeling loss data using mixtures of distributions
- Simultaneous estimation for non-crossing multiple quantile regression with right censored data
- Composite Lognormal–Pareto model with random threshold
- Fitting Mixture Models to Grouped and Truncated Data via the EM Algorithm
- Finite mixture models
- FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM
- Modeling claims data with composite Stoppa models
- Statistics of Extremes
- Modeling actuarial data with a composite lognormal-Pareto model
- On composite lognormal-Pareto models
- Understanding Relationships Using Copulas
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