Testing and Locating Variance Changepoints with Application to Stock Prices

Chen, Jie; Gupta, Arjun K.

doi:10.1080/01621459.1997.10474026

Cited by 243 publications

(145 citation statements)

References 18 publications

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“…Similar results when the changepoints are allowed to approach one another are achieved in [17]. Recent studies include many applications and it can be found in [24][25][26][27][28][29][30][31][32]. Thus, this method is now the most understood and widely cited search algorithm used within the multiple change-point literature.…”

Section: The Binary Segmentation Methodssupporting

confidence: 60%

Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Raveendran

Sofronov

2017

Annals of Computer Science and Information Systems

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Abstract-Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. We develop new methods based on a binary segmentation algorithm, which is a well-known multiple change-point detection method. The proposed algorithms are applied to artificially generated data to illustrate their usefulness. Our results show that the proposed methodologies are effective in identifying multiple domains and their boundaries in two dimensional spatial data.

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Section: The Binary Segmentation Methodssupporting

confidence: 60%

Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Raveendran

Sofronov

2017

Annals of Computer Science and Information Systems

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“…Some examples include Poisson processes with a piece-wise constant rate parameter (Raftery and Akman, 1986;Yang and Kuo, 2001;Ritov et al, 2002), changing linear regression models (Carlin et al, 1992;Lund and Reeves, 2002), Gaussian observations with varying mean (Worsley, 1979) or variance (Chen and Gupta, 1997;Johnson et al, 2003), and Markov models with time-varying transition matrices (Braun and Muller, 1998). Such models have been used for modelling stock prices, muscle activation, climatic time-series, DNA sequences and neuronal activity in the brain, amongst many other applications…”

Section: Introductionmentioning

confidence: 99%

Exact and efficient Bayesian inference for multiple changepoint problems

2006

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Summary We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on well-log data. Our method can cope with a range of models for this data, and exact simulation from the posterior distribution is possible in a matter of minutes.

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“…Determination of what phase this sector was in is therefore not completely unambiguous because the entirety of the life cycle and the associated breaks are unavailable for examination. However, it is common in the changepoint literature to refer to events contemporaneous to the study so as to identify possible causes and to better place the results in context (see for example Whittaker andFrühwirth-Schnatter, 1994, Chen andGupta, 1997). We therefore provide additional context for the patterns observed with respect to the sectors.…”

Section: Resultsmentioning

confidence: 99%

Offshoring of Information-Based Services: Structural Breaks in Industry Life Cycles

Hahn

Bunyaratavej

2011

Service Science

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he emergence of widespread offshoring of information-based services is arguably one of the more transformative business phenomena of the last ten years. A growing body of research has examined the firm-level drivers and location factors (i.e., the "whys" and "wheres") of services offshoring. However, little empirical research has examined the temporal dynamics (or "whens") of services offshoring. Adopting industry life cycle theory as a framework and a Bayesian methodological approach, we explore two key research questions: (i) when do different categories of offshoring services provision change from being emergent sectors to more mature ones relative to one another? and (ii) how do different types of offshoring activity differentially progress through this sequence? Employing a database of 1,420 offshore services FDI projects, we find that the relative skill level and the information sensitivity of the specific service category are associated with the temporal sequence of industry life cycle progression such that activities with decreased information sensitivity are offshored earlier than those with greater information sensitivity. We draw implications for our findings in terms of future waves of service offshoring.

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Testing and Locating Variance Changepoints with Application to Stock Prices

Cited by 243 publications

References 18 publications

Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Exact and efficient Bayesian inference for multiple changepoint problems

Offshoring of Information-Based Services: Structural Breaks in Industry Life Cycles

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