Bayesian analysis of multistate event history data: beta-Dirichlet process prior

Kim, Yongdai; James, Lancelot F.; Weißbach, Rafael

doi:10.1093/biomet/asr067

Cited by 32 publications

(28 citation statements)

References 6 publications

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“…This leads to the conclusion that the bank tends to keep the ratings constant in economically unstable times and is even more conservative with rating downgrades. This also has been reported in Kim et al (2012). The regional effects describe the intensity-difference of the debtors' business location with respect to the reference category Germany in an average economic situation.…”

Section: Applicationsupporting

confidence: 53%

“…Extending the model for non-proportional hazards (as in Kim et al, 2012), to include transitions to beyond the adjacent class (as in Weißbach and Walter, 2010), the non-homogeneous Markov process (as in Kim et al, 2012;Weißbach et al, 2009), to cross-sectional dependence (as in Koopman et al, 2011) or to non-Markovian hazards (as in Lando and Skødeberg, 2002;Koopman et al, 2008) can be done, as long as after marginalizing out the measurement error in a quadratic Taylor expansion, the likelihood factors have closed-form solution for derivatives with respect to the error. But one must always keep in mind that further parameters reduce the quality of the asymptotic approximation.…”

Section: Discussionmentioning

confidence: 99%

“…an analysis of durations, is the more appropriate choice and the continuous-time, finite-state Markov process remains the starting point for modeling credit rating transition histories (see Jarrow et al, 1997). Therefore, Koopman et al (2008), Duffie et al (2009) and Kim et al (2012) formulate models for the Markov transition intensity. We pursue a similar approach and ask for a measurement error in the transition times.…”

Section: Introductionmentioning

confidence: 99%

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A score-test on measurement errors in rating transition times

Voß

Weißbach

2014

Journal of Econometrics

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When modeling rating transitions as a continuous time Markov process, it is crucial to monitor the solvency of the debtors in small periodic intervals, in order to obtain suitable data for parameter estimation. If this is not done, the transition times are captured with an irregular delay, which leads to measurement errors in the transition times and consequently to biased parameter estimators. We develop a score test to check for such measurement errors in the transition data and prove the asymptotic distribution of the test statistic under the null hypothesis. The test is applied to a dataset of an international corporate portfolio of a large German bank, while accounting for economic and debtor-specific covariates. The test result is positive, indicating that measurement errors in the transition times are a real problem in practice.

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Section: Applicationsupporting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A score-test on measurement errors in rating transition times

Voß

Weißbach

2014

Journal of Econometrics

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“…, π cQ ) where p(z kc = z (q) ) = π cq and Q q=1 π cq = 1. We use the beta-Dirichlet distribution 16 as the prior for π c . Conditional on C, p(z kc = z (1) ) = π c1 follows a beta distribution with parameters 1 and α/C, and (π c2 , .…”

Section: Prior Modelmentioning

confidence: 99%

A Bayesian Nonparametric Model for Reconstructing Tumor Subclones Based on Mutation Pairs

et al. 2015

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We present a feature allocation model to reconstruct tumor subclones based on mutation pairs. The key innovation lies in the use of a pair of proximal single nucleotide variants (SNVs) for the subclone reconstruction as opposed to a single SNV. Using the categorical extension of the Indian buffet process (cIBP) we define the subclones as a vector of categorical matrices corresponding to a set of mutation pairs. Through Bayesian inference we report posterior probabilities of the number, genotypes and population frequencies of subclones in one or more tumor sample. We demonstrate the proposed methods using simulated and real-world data. A free software package is available at http://www.compgenome.org/pairclone.

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“…Instead, utilizing a Beta-Dirichlet distribution defined in Ref. [18] we propose a construction given C and Q: let {1, . .…”

Section: Development Of Cibpmentioning

confidence: 99%

Bayclone: Bayesian Nonparametric Inference of Tumor Subclones Using NGS Data

et al. 2014

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In this paper, we present a novel feature allocation model to describe tumor heterogeneity (TH) using next-generation sequencing (NGS) data. Taking a Bayesian approach, we extend the Indian buffet process (IBP) to define a class of nonparametric models, the categorical IBP (cIBP). A cIBP takes categorical values to denote homozygous or heterozygous genotypes at each SNV. We define a subclone as a vector of these categorical values, each corresponding to an SNV. Instead of partitioning somatic mutations into non-overlapping clusters with similar cellular prevalences, we took a different approach using feature allocation. Importantly, we do not assume somatic mutations with similar cellular prevalence must be from the same subclone and allow overlapping mutations shared across subclones. We argue that this is closer to the underlying theory of phylogenetic clonal expansion, as somatic mutations occurred in parent subclones should be shared across the parent and child subclones. Bayesian inference yields posterior probabilities of the number, genotypes, and proportions of subclones in a tumor sample, thereby providing point estimates as well as variabilities of the estimates for each subclone. We report results on both simulated and real data. BayClone is available at

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Bayesian analysis of multistate event history data: beta-Dirichlet process prior

Cited by 32 publications

References 6 publications

A score-test on measurement errors in rating transition times

A score-test on measurement errors in rating transition times

A Bayesian Nonparametric Model for Reconstructing Tumor Subclones Based on Mutation Pairs

Bayclone: Bayesian Nonparametric Inference of Tumor Subclones Using NGS Data

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