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Bayesian isotonic changepoint analysis

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Abstract

A general approach to Bayesian isotonic changepoint problems is developed. Such isotonic changepoint analysis includes trends and other constraint problems and it captures linear, non-smooth as well as abrupt changes. Desired marginal posterior densities are obtained using a Markov chain Monte Carlo method. The methodology is exemplified using one simulated and two real data examples, where it is shown that our proposed Bayesian approach captures the qualitative conclusion about the shape of the trend change.

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Authors and Affiliations

  1. Departamento de Matemática y CONICET, Universidad Nacional de La Plata, Calle 50 e/115, 1900, La Plata, Argentina

    Enrique E. Alvarez

  2. Department of Statistics, University of Connecticut, 215 Glenbrook Rd., Storrs, CT, 06269-4120, USA

    Dipak K. Dey

Authors
  1. Enrique E. Alvarez
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  2. Dipak K. Dey
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Correspondence to Enrique E. Alvarez.

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Alvarez, E.E., Dey, D.K. Bayesian isotonic changepoint analysis. Ann Inst Stat Math 61, 355–370 (2009). https://doi.org/10.1007/s10463-007-0148-y

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  • DOI: https://doi.org/10.1007/s10463-007-0148-y

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