scholarly journals Bivariate random change point models for longitudinal outcomes

2012 ◽  
Vol 32 (6) ◽  
pp. 1038-1053 ◽  
Author(s):  
Lili Yang ◽  
Sujuan Gao
Keyword(s):  
Change Point ◽  
Longitudinal Outcomes ◽  
Change Point Models ◽  
Random Change

Smooth random change point models

2010 ◽  
Vol 30 (6) ◽  
pp. 599-610 ◽  
Author(s):  
Ardo van den Hout ◽  
Graciela Muniz-Terrera ◽  
Fiona E. Matthews
Keyword(s):  
Change Point ◽  
Change Point Models ◽  
Random Change

Random change point models: investigating cognitive decline in the presence of missing data

2011 ◽  
Vol 38 (4) ◽  
pp. 705-716 ◽  
Author(s):  
G. Muniz Terrera ◽  
A. van den Hout ◽  
F. E. Matthews
Keyword(s):  
Missing Data ◽  
Cognitive Decline ◽  
Change Point ◽  
Change Point Models ◽  
Random Change

scholarly journals Closed-Form Estimation of Multiple Change-Point Models

2013 ◽  
Author(s):  
Greg Jensen
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Change Point ◽  
Marginal Likelihood ◽  
Stationary Time Series ◽  
Change Points ◽  
Model Complexity ◽  
Marginal Model ◽  
General Strategy ◽  
Change Point Models

Identifying discontinuities (or change-points) in otherwise stationary time series is a powerful analytic tool. This paper outlines a general strategy for identifying an unknown number of change-points using elementary principles of Bayesian statistics. Using a strategy of binary partitioning by marginal likelihood, a time series is recursively subdivided on the basis of whether adding divisions (and thus increasing model complexity) yields a justified improvement in the marginal model likelihood. When this approach is combined with the use of conjugate priors, it yields the Conjugate Partitioned Recursion (CPR) algorithm, which identifies change-points without computationally intensive numerical integration. Using the CPR algorithm, methods are described for specifying change-point models drawn from a host of familiar distributions, both discrete (binomial, geometric, Poisson) and continuous (exponential, Gaussian, uniform, and multiple linear regression), as well as multivariate distribution (multinomial, multivariate normal, and multivariate linear regression). Methods by which the CPR algorithm could be extended or modified are discussed, and several detailed applications to data published in psychology and biomedical engineering are described.

Momentum liquidation under partial information

2016 ◽  
Vol 53 (2) ◽  
pp. 341-359
Author(s):  
Erik Ekström ◽  
Martin Vannestål
Keyword(s):  
Change Point ◽  
Optimal Strategy ◽  
Partial Information ◽  
External Shocks ◽  
Momentum Effect ◽  
The Past ◽  
Momentum Trading ◽  
Partially Observable ◽  
Random Change ◽  
Do So

Abstract Momentum is the notion that an asset that has performed well in the past will continue to do so for some period. We study the optimal liquidation strategy for a momentum trade in a setting where the drift of the asset drops from a high value to a smaller one at some random change-point. This change-point is not directly observable for the trader, but it is partially observable in the sense that it coincides with one of the jump times of some exogenous Poisson process representing external shocks, and these jump times are assumed to be observable. Comparisons with existing results for momentum trading under incomplete information show that the assumption that the disappearance of the momentum effect is triggered by observable external shocks significantly improves the optimal strategy.

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