Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366459811> ?p ?o ?g. }
Showing items 1 to 63 of
63
with 100 items per page.
- W4366459811 abstract "Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. The industry code in a workers' compensation insurance product is a prime example hereof. We commonly rely on methods such as the random effects approach (Campo and Antonio, 2023) to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down (PHiRAT) algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers' compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings (Mikolov et al., 2013; Cer et al., 2018) to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. We show that our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, when estimating the technical premium of the insurance product under study as a function of the clustered hierarchical risk factor, we obtain a better differentiation between high-risk and low-risk companies." @default.
- W4366459811 created "2023-04-22" @default.
- W4366459811 creator A5004755479 @default.
- W4366459811 creator A5032178684 @default.
- W4366459811 date "2023-04-18" @default.
- W4366459811 modified "2023-09-27" @default.
- W4366459811 title "On clustering levels of a hierarchical categorical risk factor" @default.
- W4366459811 doi "https://doi.org/10.48550/arxiv.2304.09046" @default.
- W4366459811 hasPublicationYear "2023" @default.
- W4366459811 type Work @default.
- W4366459811 citedByCount "0" @default.
- W4366459811 crossrefType "posted-content" @default.
- W4366459811 hasAuthorship W4366459811A5004755479 @default.
- W4366459811 hasAuthorship W4366459811A5032178684 @default.
- W4366459811 hasBestOaLocation W43664598111 @default.
- W4366459811 hasConcept C119043178 @default.
- W4366459811 hasConcept C119857082 @default.
- W4366459811 hasConcept C124101348 @default.
- W4366459811 hasConcept C144986985 @default.
- W4366459811 hasConcept C149782125 @default.
- W4366459811 hasConcept C162324750 @default.
- W4366459811 hasConcept C2524010 @default.
- W4366459811 hasConcept C31170391 @default.
- W4366459811 hasConcept C33923547 @default.
- W4366459811 hasConcept C34447519 @default.
- W4366459811 hasConcept C41008148 @default.
- W4366459811 hasConcept C5274069 @default.
- W4366459811 hasConcept C53059260 @default.
- W4366459811 hasConcept C73555534 @default.
- W4366459811 hasConcept C90673727 @default.
- W4366459811 hasConcept C92835128 @default.
- W4366459811 hasConceptScore W4366459811C119043178 @default.
- W4366459811 hasConceptScore W4366459811C119857082 @default.
- W4366459811 hasConceptScore W4366459811C124101348 @default.
- W4366459811 hasConceptScore W4366459811C144986985 @default.
- W4366459811 hasConceptScore W4366459811C149782125 @default.
- W4366459811 hasConceptScore W4366459811C162324750 @default.
- W4366459811 hasConceptScore W4366459811C2524010 @default.
- W4366459811 hasConceptScore W4366459811C31170391 @default.
- W4366459811 hasConceptScore W4366459811C33923547 @default.
- W4366459811 hasConceptScore W4366459811C34447519 @default.
- W4366459811 hasConceptScore W4366459811C41008148 @default.
- W4366459811 hasConceptScore W4366459811C5274069 @default.
- W4366459811 hasConceptScore W4366459811C53059260 @default.
- W4366459811 hasConceptScore W4366459811C73555534 @default.
- W4366459811 hasConceptScore W4366459811C90673727 @default.
- W4366459811 hasConceptScore W4366459811C92835128 @default.
- W4366459811 hasLocation W43664598111 @default.
- W4366459811 hasOpenAccess W4366459811 @default.
- W4366459811 hasPrimaryLocation W43664598111 @default.
- W4366459811 hasRelatedWork W1560541823 @default.
- W4366459811 hasRelatedWork W1990694217 @default.
- W4366459811 hasRelatedWork W2016878174 @default.
- W4366459811 hasRelatedWork W2063677717 @default.
- W4366459811 hasRelatedWork W2120537084 @default.
- W4366459811 hasRelatedWork W2135733469 @default.
- W4366459811 hasRelatedWork W2511442670 @default.
- W4366459811 hasRelatedWork W2782584004 @default.
- W4366459811 hasRelatedWork W3198830314 @default.
- W4366459811 hasRelatedWork W2788164509 @default.
- W4366459811 isParatext "false" @default.
- W4366459811 isRetracted "false" @default.
- W4366459811 workType "article" @default.