Package: ADLP
Type: Package
Title: Accident and Development Period Adjusted Linear Pools for
        Actuarial Stochastic Reserving
Version: 0.1.0
Authors@R: c(
    person("Benjamin", "Avanzi", role = "aut", email = "b.avanzi@unimelb.edu.au"),
    person("William", "Ho", role = "aut", email = "yunwaiho@gmail.com"),
    person("Yanfeng", "Li", role = c("aut", "cre"), email = "yanfeng.li@student.unsw.edu.au"),
    person("Bernard", "Wong", role = "aut", email = "bernard.wong@unsw.edu.au"),
    person("Alan", "Xian", role = "aut", email = "alanxian@hotmail.com"))
Author: Benjamin Avanzi [aut],
  William Ho [aut],
  Yanfeng Li [aut, cre],
  Bernard Wong [aut],
  Alan Xian [aut]
Maintainer: Yanfeng Li <yanfeng.li@student.unsw.edu.au>
Description: Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data.
    This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from
    various loss reserving models, often based on subjective assessments.
        This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our
    framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central
    estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development,
    calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning.
        Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In
    particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators).
    Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.
License: GPL-3
Encoding: UTF-8
URL: https://github.com/agi-lab/ADLP
BugReports: https://github.com/agi-lab/ADLP/issues
LazyData: true
RoxygenNote: 7.3.1
Imports: methods
Suggests: knitr, tidyverse, SynthETIC (>= 1.0.0)
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-04-18 07:21:50 UTC; jimli
Repository: CRAN
Date/Publication: 2024-04-18 19:23:01 UTC
Built: R 4.3.3; ; 2025-01-24 03:25:22 UTC; unix
