J
Jinlong Huang
Researcher at East China Normal University
Publications - 5
Citations - 60
Jinlong Huang is an academic researcher from East China Normal University. The author has contributed to research in topics: Aggregate data & Poisson distribution. The author has an hindex of 3, co-authored 5 publications receiving 50 citations.
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Asymptotic behaviors of stochastic reserving: Aggregate versus individual models
TL;DR: The results show that the individual method has the smallest asymptotic variance, followed by the BF algorithm, and the Chain-Ladder and Bornhuetter–Ferguson algorithms has the largest asymptic variance.
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Stochastic Loss Reserving in Discrete Time: Individual vs. Aggregate Data Models
TL;DR: In this paper, a stochastic individual data model is considered, which accommodates occurrence times, reporting, and settlement delays and severity of every individual claims, and gives rise to a model for the corresponding aggregate data under which classical chain ladder and Bornhuetter-Ferguson algorithms apply.
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An individual loss reserving model with independent reporting and settlement
TL;DR: In this paper, the authors assess and demonstrate the advantage of claims reserving models based on individual data in forecasting future liabilities over traditional models on aggregate data both theoretically and numerically.
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Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data
TL;DR: In this article, the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data is considered.
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Bayesian ratemaking under Dirichlet process mixtures
TL;DR: In this paper, the Gibbs sampling schemes are designed for the purpose of approximating the conditional expectations of the quantities concerned, and the Bayesian experience ratemaking under Dirichlet process mixture models are investigated.