A SIMPLE BAYESIAN APPROACH TO MULTIPLE CHANGE-POINTS

By Tze Leung Lai, Haipeng Xing. 2011

60 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dda2441b5274ab2787d74e852fcffa0f0e4da446

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pengyu Liu
2. I. Macneill
3. Stergios B. Fotopoulos
4. Venkata K. Jandhyala
5. Tze Leung Lai
6. Haipeng Xing

In Finance

1. Pengyu Liu
2. I. Macneill
3. Stergios B. Fotopoulos
4. Venkata K. Jandhyala
5. Tze Leung Lai
6. Haipeng Xing
7. Noureddine El Karoui

In Statistics and Probability

1. Pengyu Liu
2. I. Macneill
3. Stergios B. Fotopoulos
4. Venkata K. Jandhyala
5. Tze Leung Lai
6. Haipeng Xing
7. M. Ernst
8. Yen-Chi Chen

Citations in the corpus

• Inference for single and multiple change‐points in time series (2013, methodology): “…include those of Yao (1988), Barry and Hartigan (1992, 1993), Chib (1998), Wang and Zivot (2000), Caussinus and Mestre (2004), Fearnhead (2006), Zhao and Chu (2006), Seidou and Ouarda (2007), Giordani and Kohn (2008), Koop and Potter (2009), Chong and Ko (2012) and Lai and Xing (2011, 2012).”
• Sequential Change-Point Detection When the Pre- and Post-Change Parameters are Unknown (2010, background): “…equivalence actually holds more generally for the prior densities assumed in the theorem because Laplace’s asymptotic formula for integrals can be used for more general prior densities of (θ0, θ1) than conjugate priors that are independent, as pointed out in Section 6 of Lai and Xing (2008a).”