Statistical methods for DNA sequence segmentation

DOI10.1214/ss/1028905933zbMath0960.62121OpenAlexW2005711658MaRDI QIDQ5926347

Publication date: 1998

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/ss/1028905933

zbMATH Keywords

amino acids bacteriophage lambda change-point problem chromosome banding deoxyri-bonucleic acid sequences hidden Markov chain longe range correlation models nucleotide patchiness proteins quasideviance split local polynomial statistical genetics

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40)

Related Items

Estimation of regression functions with a discontinuity in a derivative with local polynomial fits, Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection, A Bayesian Approach to DNA Sequence Segmentation, Optimal method in multiple regression with structural changes, Bayesian hidden Markov models in DNA sequence segmentation using R: the case of Simian Vacuolating virus (SV40), HMM with emission process resulting from a special combination of independent Markovian emissions, Spectral entropy criteria for structural segmentation in genomic DNA sequences, Convergence in distribution of multiple change point estimators, Asymptotic properties of maximum likelihood estimators in models with multiple change points, Change point detection for nonparametric regression under strongly mixing process, Analyzing growth components in trees, Detection of multiple change-points in multivariate data, Asymptotic properties of pseudo maximum likelihood estimators and test in semi-parametric copula models with multiple change points, GPS trajectory data segmentation based on probabilistic logic, Variable length Markov chains, Wild binary segmentation for multiple change-point detection, Estimating change-points in biological sequences via the cross-entropy method, From data to stochastic models, Kernel estimation of discontinuous regression functions, Regression discontinuity designs with unknown discontinuity points: testing and estimation, Tail-greedy bottom-up data decompositions and fast multiple change-point detection, A semiparametric maximum likelihood ratio test for the change point in copula models, Bayesian hidden Markov model for DNA sequence segmentation: a prior sensitivity analysis, Multiple change-point estimation with U-statistics, Exploring the latent segmentation space for the assessment of multiple change-point models, Subsampling methods for genomic inference, Asymptotic operating characteristics of an optimal change point detection in hidden Markov models, Strong approximation of multidimensional $\mathbb P$-$\mathbb P$ plots processes by Gaussian processes with applications to statistical tests, Detection of a change point based on local-likelihood, Inferring the Location and Effect of Tumor Suppressor Genes by Instability‐Selection Modeling of Allelic‐Loss Data, An MDL approach to the climate segmentation problem, Asymptotic fluctuations of mutagrams, On optimal multiple changepoint algorithms for large data, A dynamic programming segmentation procedure for hydrological and environmental time series, Exact asymptotic distribution of change-point MLE for change in the mean of Gaussian se\-quences, Bayesian multiple change-points detection in a normal model with heterogeneous variances, Heterogeneity in DNA Multiple Alignments: Modeling, Inference, and Applications in Motif Finding, Change-Point Estimation in Long Memory Nonparametric Models with Applications, Exact Spike Train Inference Via $\ell_0$ Optimization, Estimation of General Stationary Processes by Variable Length Markov Chains, Identifying Clusters in Spatial Data Via Sequential Importance Sampling, Some Nonparametric Tests for Change-Point Detection Based on the ℙ-ℙ and ℚ-ℚ Plot Processes

Cites Work