Multiple Changepoint Fitting via Quasilikelihood,
with Application to DNA Sequence Segmentation
JV Braun1, RK Braun2, H-G Müller3
1
Kings Mountain Research, 1942 Kings Mountain Road, Woodside, CA 94062-4234, USA,
E-mail: jerome.braum@kmri.com,
2
Stottler Henke Associates, Inc., 1107 NE 45th Street,
Suite 401, Seattle, WA 98105, USA, E-mail: rbraum@shai-seattle.com,
3
Division of Statistics, University of California, One Shields
Avenue, Davis, CA 95616, USA, E-mail: mueller@wald.ucdavis.edu
Biometrika, 87(2):301-314 (2000)
Abstract
We consider situations where a step function with a variable
number of steps provides an adequate model for a regression
relationship, while the variance of the observations depends
on their mean. This model provides for discontinuous jumps
at changepoints and for constant means and error variances
in between changepoints. The basic statistical problem
consists of identification of the number of changepoints, their
locations and the levels the function assumes in between. We
embed this problem into a quasilikelihood formulation and
utilise the minimum deviance criterion to fit the model; for
the choice of the number of changepoints, we discuss a
modified Schwarz criterion. A dynamic programming
algorithm makes the segmentation feasible for sequences of
moderate length. The performance of the segmentation
method is demonstrated in an application to the
segmentation of the Bacteriophage lambda sequence.
