A Survey of Approaches to a Changepoint Problem in an Actuarial Context

Avraham Adler

doi:10.66573/001c.127620

January 1, 2015

A Survey of Approaches to a Changepoint Problem in an Actuarial Context

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Key Points

The aim is to compare classical and Bayesian methods for identifying changepoints in data sequences.
Used a representative dataset for analysis
Compared classical and Bayesian inference methods
Analyzed shifts in the underlying distribution of random variables
Showed R code for transparency and reproducibility in methods
Identified differences in effectiveness between Bayesian and classical methods
Provided insights into the underlying distributions at changepoints
Highlighted the strengths of each approach in handling data shifts

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Implication

Survey compares classical and Bayesian methods for detecting distribution shifts in data, indicating their effectiveness.

Abstract

A representative dataset is used to provide an example comparing classical and Bayesian approaches to making inferences about the point in a sequence of random variables at which the underlying distribution may shift. Inferences about the underlying distributions themselves are also made. Most of the underlying R code used in the analysis is shown in the appendix.

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Avraham Adler (2015) studied this question.

synapsesocial.com/papers/69e320fd40886becb6540221 https://doi.org/10.66573/001c.127620

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A Survey of Approaches to a Changepoint Problem in an Actuarial Context

Key Points

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Implication

Abstract

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A Survey of Approaches to a Changepoint Problem in an Actuarial Context

Key Points

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Implication

Abstract

Discussion

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