Pages that link to "Item:Q2520467"

The following pages link to Modeling loss data using mixtures of distributions (Q2520467):

Displaying 50 items.

• On modeling left-truncated loss data using mixtures of distributions (Q124235) (← links)
• Hidden semi-Markov-switching quantile regression for time series (Q830112) (← links)
• Bayesian nonparametric regression models for modeling and predicting healthcare claims (Q1622503) (← links)
• Fitting the Erlang mixture model to data via a GEM-CMM algorithm (Q1643834) (← links)
• Compound unimodal distributions for insurance losses (Q1667415) (← links)
• Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions (Q1681087) (← links)
• On generalized log-Moyal distribution: a new heavy tailed size distribution (Q1742726) (← links)
• A class of mixture of experts models for general insurance: theoretical developments (Q2010898) (← links)
• The exponential T-X family of distributions: properties and an application to insurance data (Q2036067) (← links)
• Two-part models for assessing misrepresentation on risk status (Q2066782) (← links)
• On the mixtures of length-biased Weibull distributions for loss severity modeling (Q2131911) (← links)
• Modeling right-skewed financial data streams: a likelihood inference based on the generalized Birnbaum-Saunders mixture model (Q2177677) (← links)
• The arcsine exponentiated-\(X\) family: validation and insurance application (Q2185070) (← 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)
• Data breaches: goodness of fit, pricing, and risk measurement (Q2364015) (← links)
• Conditional tail risk measures for the skewed generalised hyperbolic family (Q2415969) (← links)
• Skew mixture models for loss distributions: a Bayesian approach (Q2447415) (← links)
• Gamma mixture density networks and their application to modelling insurance claim amounts (Q2665857) (← links)
• A class of generalised hyper-elliptical distributions and their applications in computing conditional tail risk measures (Q2665869) (← links)
• Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models (Q2682986) (← links)
• Frequency-severity experience rating based on latent Markovian risk profiles (Q2682996) (← links)
• Dependence modelling in insurance via copulas with skewed generalised hyperbolic marginals (Q2699605) (← links)
• Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims (Q3390597) (← links)
• Fat-Tailed Regression Modeling with Spliced Distributions (Q4633996) (← links)
• (Q4900322) (← links)
• Using Model Averaging to Determine Suitable Risk Measure Estimates (Q5027908) (← links)
• Risk measure estimation under two component mixture models with trimmed data (Q5036563) (← links)
• A new look at the inverse Gaussian distribution with applications to insurance and economic data (Q5036583) (← links)
• A family of density-hazard distributions for insurance losses (Q5042172) (← links)
• Estimation of lifetime parameters of the modified extended exponential distribution with application to a mechanical model (Q5055187) (← links)
• From grouped to de-grouped data: a new approach in distribution fitting for grouped data (Q5107324) (← 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)
• Extending composite loss models using a general framework of advanced computational tools (Q5193489) (← links)
• A new class of skew distributions with climate data analysis (Q5861149) (← links)
• PHASE-TYPE DISTRIBUTIONS FOR CLAIM SEVERITY REGRESSION MODELING (Q5866174) (← links)
• Assessing the performance of confidence intervals for high quantiles of Burr XII and Inverse Burr mixtures (Q5867490) (← links)
• Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models (Q5877347) (← links)
• Loss Models (Q5900178) (← links)
• Parameter estimation for power function-lognormal composite distribution (Q6106244) (← links)
• Mixture Composite Regression Models with Multi-type Feature Selection (Q6110498) (← links)
• Phase-type mixture-of-experts regression for loss severities (Q6156007) (← links)
• Ruin probabilities as functions of the roots of a polynomial (Q6166247) (← links)
• Method of Winsorized Moments for Robust Fitting of Truncated and Censored Lognormal Distributions (Q6549261) (← links)
• Leveraging Weather Dynamics in Insurance Claims Triage Using Deep Learning (Q6567876) (← 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)
• A class of claim distributions: Properties, characterizations and applications to insurance claim data (Q6587720) (← links)