On modeling claim frequency data in general insurance with extra zeros

By Karen C.H. Yip, Kelvin K. W. Yau. 2005

189 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3876f4e6e7db8ddabff411689454a553a1724678

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In Artificial Intelligence

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Zero-inflated and overdispersed: what's one to do? (2013, background): “For instance, the ZIP and/or ZINB models have been used in the social sciences to examine counselling session attendance [1], community policing acts [2], insurance claims [3], drug arrests [4], and adolescent drinking behaviour [5].”
Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape (2013, background): “See, e.g., Yip and Yau (2005) or Boucher et al. (2006).”
Fixed versus Random Effects in Poisson Regression Models for Claim Counts: A Case Study with Motor Insurance (2006, methodology): “YIP & YAU (2005) presented several parametric Zero-Inflated count distributions, and BOUCHER, DENUIT & GUILLEN (2006) resorted to Hurdle Models.”
Estimation of Claim Count Data using Negative Binomial, Generalized Poisson, Zero-Inflated Negative Binomial and Zero-Inflated Generalized Poisson Regression Models (2013, background): “i  , the log and logit functions can be rewritten as (Yip and Yau, 2005; Ozmen and Famoye, 2007),”
Risk Classification for Claim Counts (2007, background): “For instance, the ZI- model (which covers all the ZI–Negative Binomial cases) has probability mass functionNB(k 1) 1 k 22 k / i (1 )(1 ) for n 0i i i i 2 kf (n ) (3.3) ( j 1) 1 k 2N i i ii 2 k / nii(1 )(1 ) for n 1, 2, . . . . i i j 1 i2 k(1 ) ji Recently Yip and Yau (2005) applied this kind of mode”
Statistical concepts of a priori and a posteriori risk classification in insurance (2011, background): “See, for example, Yip and Yau (2005), Boucher et al. (2007) for cross-sectional and Boucher et al. (2009) for longitudinal data.”