Multiscale Quantile Segmentation
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[1] B. Sakmann,et al. Single-Channel Recording , 1995, Springer US.
[2] David Siegmund,et al. Change-Points: From Sequential Detection to Biology and Back , 2013 .
[3] M. Virji. Pathogenic neisseriae: surface modulation, pathogenesis and infection control , 2009, Nature Reviews Microbiology.
[4] D. Paindaveine,et al. Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth , 2010, 1002.4486.
[5] Hongzhe Li,et al. Optimal Sparse Segment Identification With Application in Copy Number Variation Analysis , 2010, Journal of the American Statistical Association.
[6] P. Massart. The Tight Constant in the Dvoretzky-Kiefer-Wolfowitz Inequality , 1990 .
[7] C. Holmes,et al. Multiscale Blind Source Separation , 2016, 1608.07173.
[8] A. Munk,et al. Multiscale change point inference , 2013, 1301.7212.
[9] Yuan Liao,et al. Oracle Estimation of a Change Point in High-Dimensional Quantile Regression , 2016, Journal of the American Statistical Association.
[10] J. S. Silva,et al. Quantiles for Counts , 2002 .
[11] Song Liu,et al. Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges , 2013, Oncotarget.
[12] Arne Kovac,et al. Extensions of Smoothing via Taut Strings , 2008, 0803.2931.
[13] Heping Zhang,et al. THE SCREENING AND RANKING ALGORITHM TO DETECT DNA COPY NUMBER VARIATIONS. , 2012, The annals of applied statistics.
[14] Radhakrishnan Gnanasambandam,et al. Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length: Application to Human PIEZO1-Channels , 2017, Front. Neuroinform..
[15] J. Tukey. Curves As Parameters, and Touch Estimation , 1961 .
[16] Axel Munk,et al. Autocovariance Estimation in Regression with a Discontinuous Signal and m‐Dependent Errors: A Difference‐Based Approach , 2015, 1507.02485.
[17] Alexander Aue,et al. Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle , 2014 .
[18] Paul H. C. Eilers,et al. Quantile smoothing of array CGH data , 2005, Bioinform..
[19] R. Tibshirani,et al. Spatial smoothing and hot spot detection for CGH data using the fused lasso. , 2008, Biostatistics.
[20] Zongwu Cai,et al. Partially varying coefficient instrumental variables models , 2012 .
[21] Alexander Aue,et al. Piecewise quantile autoregressive modeling for nonstationary time series , 2016, 1609.08882.
[22] P. Fearnhead,et al. Optimal detection of changepoints with a linear computational cost , 2011, 1101.1438.
[23] Walter Krämer,et al. Recursive computation of piecewise constant volatilities , 2012, Comput. Stat. Data Anal..
[24] Julia Hirschberg,et al. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.
[25] Alexandre B. Tsybakov,et al. Introduction to Nonparametric Estimation , 2008, Springer series in statistics.
[26] Shin-Ho Chung,et al. Biological membrane ion channels : dynamics, structure, and applications , 2006 .
[27] Z. Harchaoui,et al. Multiple Change-Point Estimation With a Total Variation Penalty , 2010 .
[28] Axel Munk,et al. Heterogeneous change point inference , 2015, 1505.04898.
[29] V. Spokoiny,et al. Multiscale testing of qualitative hypotheses , 2001 .
[30] L. Duembgen,et al. Multiscale inference about a density , 2007, 0706.3968.
[31] Xuming He. Quantile Curves without Crossing , 1997 .
[32] Hao Chen,et al. Asymptotic distribution-free change-point detection for multivariate and non-Euclidean data , 2017, The Annals of Statistics.
[33] S. Shapiro,et al. An Analysis of Variance Test for Normality (Complete Samples) , 1965 .
[34] Merle Behr. Finite Alphabet Blind Separation , 2018 .
[35] David O Siegmund,et al. A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data , 2007, Biometrics.
[36] David S. Matteson,et al. A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data , 2013, 1306.4933.
[37] R. Koenker. Quantile Regression: Name Index , 2005 .
[38] Yi Yu,et al. Estimating whole‐brain dynamics by using spectral clustering , 2015, 1509.03730.
[39] Axel Munk,et al. Multiscale DNA partitioning: statistical evidence for segments , 2014, Bioinform..
[40] Ji Zhu,et al. BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm364 Data and text mining Analysis of array CGH data for cancer studies using , 2022 .
[41] H. Dette,et al. Detection of Multiple Structural Breaks in Multivariate Time Series , 2013, 1309.1309.
[42] Paul Fearnhead,et al. Changepoint Detection in the Presence of Outliers , 2016, Journal of the American Statistical Association.
[43] A. Munk,et al. FDR-Control in Multiscale Change-point Segmentation , 2014, 1412.5844.
[44] R Bellman,et al. On the Theory of Dynamic Programming. , 1952, Proceedings of the National Academy of Sciences of the United States of America.
[45] P. Davies,et al. Local Extremes, Runs, Strings and Multiresolution , 2001 .
[46] Alessandro Rinaldo,et al. Optimal nonparametric change point detection and localization. , 2019, 1905.10019.
[47] A. Belloni,et al. L1-Penalized Quantile Regression in High Dimensional Sparse Models , 2009, 0904.2931.
[48] Paul Fearnhead,et al. Exact and efficient Bayesian inference for multiple changepoint problems , 2006, Stat. Comput..
[49] Changliang Zou,et al. Nonparametric maximum likelihood approach to multiple change-point problems , 2014, 1405.7173.
[50] V. Liebscher,et al. Consistencies and rates of convergence of jump-penalized least squares estimators , 2009, 0902.4838.
[51] Piotr Fryzlewicz,et al. Wild binary segmentation for multiple change-point detection , 2014, 1411.0858.
[52] A. Futschik,et al. Estimating the Effective Population Size from Temporal Allele Frequency Changes in Experimental Evolution , 2016, Genetics.
[53] P. Fryzlewicz. Tail-greedy bottom-up data decompositions and fast multiple change-point detection , 2018, The Annals of Statistics.
[54] S. Kou,et al. Stepwise Signal Extraction via Marginal Likelihood , 2016, Journal of the American Statistical Association.
[55] C. Small. A Survey of Multidimensional Medians , 1990 .
[56] V. Chernozhukov,et al. QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING , 2007, 0704.3649.
[57] Cun-Hui Zhang,et al. Minimax Risk Bounds for Piecewise Constant Models , 2017 .
[58] M. Wigler,et al. Circular binary segmentation for the analysis of array-based DNA copy number data. , 2004, Biostatistics.
[59] R. Koenker. Quantile Regression: Fundamentals of Quantile Regression , 2005 .
[60] Guanghui Wang,et al. Change-point detection in multinomial data with a large number of categories , 2018, The Annals of Statistics.
[61] Huihui Shen. The Detection and Empirical Study of Variance Change Points on Housing Prices —Taking Wuhan City Commodity Prices as an Example , 2016 .
[62] Paul Fearnhead,et al. A computationally efficient nonparametric approach for changepoint detection , 2016, Statistics and Computing.
[63] P. Perron,et al. Estimating and testing linear models with multiple structural changes , 1995 .
[64] G. Winkler,et al. Complexity Penalized M-Estimation , 2008 .
[65] B. Russell,et al. Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve , 2019 .
[66] Jaakko Astola,et al. On computation of the running median , 1989, IEEE Trans. Acoust. Speech Signal Process..
[67] Lutz Dümbgen,et al. New goodness-of-fit tests and their application to nonparametric confidence sets , 1998 .
[68] Alain Celisse,et al. New efficient algorithms for multiple change-point detection with reproducing kernels , 2018, Comput. Stat. Data Anal..
[69] V. Spokoiny. Multiscale local change point detection with applications to value-at-risk , 2009, 0906.1698.
[70] P. Fryzlewicz,et al. Narrowest‐over‐threshold detection of multiple change points and change‐point‐like features , 2016, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[71] J. Wolfowitz,et al. On a Test Whether Two Samples are from the Same Population , 1940 .
[72] Eric Ruggieri,et al. A pruned recursive solution to the multiple change point problem , 2018, Comput. Stat..