New stopping criteria for segmenting DNA sequences

We propose a solution on the stopping criterion in segmenting inhomogeneous DNA sequences with complex statistical patterns. This new stopping criterion is based on Bayesian Information Criterion (BIC) in the model selection framework. When this stopping criterion is applied to a left telomere sequence of yeast Saccharomyces cerevisiae and the complete genome sequence of bacterium Escherichia coli, borders of biologically meaningful units were identified (e.g. subtelomeric units, replication origin, and replication terminus), and a more reasonable number of domains was obtained. We also introduce a measure called segmentation strength which can be used to control the delineation of large domains. The relationship between the average domain size and the threshold of segmentation strength is determined for several genome sequences.Comment: 4 pages, 4 figures, Physical Review Letters, to appea

Constructing Structural VAR Models with Conditional Independence Graphs

In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which could be given a causal interpretation. A comparison between competing models is then made by considering likelihood and economic theory.Graphical models; directed acyclic graphs; term structure; causality.

bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems

Barry and Hartigan (1993) propose a Bayesian analysis for change point problems. We provide a brief summary of selected work on change point problems, both preceding and following Barry and Hartigan. We outline Barry and Hartigan's approach and offer a new R package, pkgbcp (Erdman and Emerson 2007), implementing their analysis. We discuss two frequentist alternatives to the Bayesian analysis, the recursive circular binary segmentation algorithm (Olshen and Venkatraman 2004) and the dynamic programming algorithm of (Bai and Perron 2003). We illustrate the application of bcp with economic and microarray data from the literature.