A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data

By Nancy Ruonan Zhang, David O. Siegmund. Biometrics, 2007

434 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/1720998874dbcebfbe72856945633c097e69387f

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Citations in the corpus

MODEL SELECTION FOR HIGH-DIMENSIONAL, MULTI-SEQUENCE CHANGE-POINT PROBLEMS (2012, methodology): “To deal with (ii), Zhang and Siegmund (2007) assumed that there exists a vector ρ = (ρ0, . . . , ρm+1), with 0 = ρ0 ρ1 · · · ρm+1 = 1 such that τ/T → ρ, and showed that up to terms of constant order ℓ(τ̂ , µ̂)− 2−1 m+1∑ i=1 log(τ̂i − τ̂i−1)− ( m− 1 2 ) log T (1.2) is an approximation to the…”
Testing for Change Points in Time Series (2010, methodology): “One approach is to treat the change point estimation and testing as a model selection problem and adopt a suitably chosen information criterion; see Yao (1988) and Zhang and Siegmund (2007) for the use of BIC and a modified version.”
Intrinsic inference difficulties for trait evolution with Ornstein‐Uhlenbeck models (2014, methodology): “In particular, Zhang & Siegmund (2007) introduced amodifiedBICwith the following penalty: 3 log nþ 2m 1 log nþ Xm i¼0 log ni wherem is the number of shifts, n is the sample size.”
Optimal detection of changepoints with a linear computational cost (2011, methodology): “, Inclan and Tiao 1994; Rigaill 2010), or those based on both the segment log-likelihood and the length of the segment (Zhang and Siegmund 2007).”
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “For example, Johnson et al. (2011) discussed an approach that is rooted in information theory, and ideas from model selection are applied for determining both the number and the location of change points in Yao (1987) and Zhang and Siegmund (2007).”
Detecting Changes in Slope With an L0 Penalty (2017, methodology): “This covers the common choices of how the penalty depends on segment length (e.g., Davis, Lee, and Rodriguez-Yam 2006; Zhang and Siegmund 2007).”
Fast and accurate detection of evolutionary shifts in Ornstein–Uhlenbeck models (2016, methodology): “In this setting, however, Ho & An e (2014a) showed that AIC is biased towards model overfit and suggested using instead a modified Bayesian information criterion (mBIC; Zhang & Siegmund 2007) to reduce the detection of false shifts.”
A review of change point detection methods (2018, methodology): “The main application of Pelt is DNA sequence data [69, 70, 135, 137] (with the cost function cL2 .”