Abstract Details
Activity Number:
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46
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Type:
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Contributed
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Date/Time:
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Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #309299 |
Title:
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Inference for the Broken-Stick Model: A Computationally Faster Approach
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Author(s):
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Ritabrata Das*+ and Moulinath Banerjee and Bin Nan
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Companies:
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and University of Michigan and University of Michigan
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Keywords:
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broken-stick model ;
change-point ;
Newton-Raphson ;
asymptotics ;
computational economy ;
smoothing
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Abstract:
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The existence of one or more change-points in linear regression problems has significant applications in climate data, economic time series and for modeling biological processes, where the change-points mostly pertain to the onset of biologically important phenomena. Estimation of change-point(s) in a broken-stick model using the exact likelihood has been discussed in some depth in the literature but most of the methods are computationally quite expensive: the non-differentiability at the kink(s) necessitates an exhaustive search across tuples of order statistics. In this article, we present a smoothing based approach to address this difficulty. We smooth the broken-stick in a shrinking neighborhood of the kinks by quadratic functions and use this as our working model, which allows the use of Newton-Raphson type methods for the working likelihood function. Asymptotic properties of our estimates are presented. We find that our estimates converge at square-root rate and are fully efficient. Simulations clearly vindicate the computational economy of our approach with quite remarkable gains in computation times for the two change points problem.
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Authors who are presenting talks have a * after their name.
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