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

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Cited by:

  1. Debi P Bal & Badri N Rath, 2019. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India - A Reassessment," Economics Bulletin, AccessEcon, vol. 39(1), pages 592-604.
  2. Yoshiyuki Ninomiya, 2015. "Change-point model selection via AIC," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 943-961, October.
  3. Kurozumi, Eiji & Tuvaandorj, Purevdorj, 2011. "Model selection criteria in multivariate models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 164(2), pages 218-238, October.
  4. Guédon, Yann & Legave, Jean Michel, 2008. "Analyzing the time-course variation of apple and pear tree dates of flowering stages in the global warming context," Ecological Modelling, Elsevier, vol. 219(1), pages 189-199.
  5. Yu Chuan Tai & Mark N. Kvale & John S. Witte, 2010. "Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change-Point Approach," Biometrics, The International Biometric Society, vol. 66(3), pages 675-683, September.
  6. Harris, David & Leybourne, Stephen J. & Taylor, A.M. Robert, 2016. "Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point," Journal of Econometrics, Elsevier, vol. 192(2), pages 451-467.
  7. repec:jss:jstsof:33:i04 is not listed on IDEAS
  8. Lee Jaeeun & Chen Jie, 2019. "A penalized regression approach for DNA copy number study using the sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(4), pages 1-14, August.
  9. Fryzlewicz, Piotr, 2020. "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection," LSE Research Online Documents on Economics 103430, London School of Economics and Political Science, LSE Library.
  10. Yana Melnykov & Marcus Perry, 2024. "On Robust Change Point Detection and Estimation in Multisubject Studies," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 827-879, August.
  11. Jian Li & Fugee Tsung & Changliang Zou, 2013. "Directional change‐point detection for process control with multivariate categorical data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(2), pages 160-173, March.
  12. Sean Jewell & Paul Fearnhead & Daniela Witten, 2022. "Testing for a change in mean after changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1082-1104, September.
  13. Hosik Choi & Eunjung Song & Seung-sik Hwang & Woojoo Lee, 2018. "A modified generalized lasso algorithm to detect local spatial clusters for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 537-563, October.
  14. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
  15. Harris, David & Kew, Hsein & Taylor, A.M. Robert, 2020. "Level shift estimation in the presence of non-stationary volatility with an application to the unit root testing problem," Journal of Econometrics, Elsevier, vol. 219(2), pages 354-388.
  16. Asuka Takeda & Yuichi Ando & Jun Tomio, 2023. "Long- and Short-Term Trends in Outpatient Attendance by Speciality in Japan: A Joinpoint Regression Analysis in the Context of the COVID-19 Pandemic," IJERPH, MDPI, vol. 20(23), pages 1-12, December.
  17. Kaixu Yang & Tapabrata Maiti, 2022. "Ultrahigh‐dimensional generalized additive model: Unified theory and methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 917-942, September.
  18. Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2013. "Inference on Structural Breaks using Information Criteria," Manchester School, University of Manchester, vol. 81, pages 54-81, October.
  19. Jaromír Antoch & Daniela Jarušková, 2013. "Testing for multiple change points," Computational Statistics, Springer, vol. 28(5), pages 2161-2183, October.
  20. Picard, F. & Lebarbier, E. & Budinskà, E. & Robin, S., 2011. "Joint segmentation of multivariate Gaussian processes using mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1160-1170, February.
  21. Arnaud Dufays & Elysee Aristide Houndetoungan & Alain Coën, 2022. "Selective Linear Segmentation for Detecting Relevant Parameter Changes [Risks and Portfolio Decisions Involving Hedge Funds]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 762-805.
  22. Aalok Ranjan Chaurasia, 2020. "Long-Term Trend in Infant Mortality in India: A Joinpoint Regression Analysis for 1971–2018," Indian Journal of Human Development, , vol. 14(3), pages 394-406, December.
  23. Sang Gil Kang & Woo Dong Lee & Yongku Kim, 2021. "Bayesian Multiple Change-Points Detection in a Normal Model with Heterogeneous Variances," Computational Statistics, Springer, vol. 36(2), pages 1365-1390, June.
  24. Hajra Siddiqa & Sajid Ali & Ismail Shah, 2021. "Most recent changepoint detection in censored panel data," Computational Statistics, Springer, vol. 36(1), pages 515-540, March.
  25. S Kovács & P Bühlmann & H Li & A Munk, 2023. "Seeded binary segmentation: a general methodology for fast and optimal changepoint detection," Biometrika, Biometrika Trust, vol. 110(1), pages 249-256.
  26. Matúš Maciak & Ivan Mizera, 2016. "Regularization techniques in joinpoint regression," Statistical Papers, Springer, vol. 57(4), pages 939-955, December.
  27. Neil Kellard & Denise Osborn & Jerry Coakley & Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2015. "Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 741-762, September.
  28. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.
  29. Gordon J. Ross, 2020. "Tracking the evolution of literary style via Dirichlet–multinomial change point regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 149-167, January.
  30. Lu Shaochuan, 2020. "Bayesian multiple changepoints detection for Markov jump processes," Computational Statistics, Springer, vol. 35(3), pages 1501-1523, September.
  31. Julia Homann & Jessica L. Oster & Cameron B. Wet & Sebastian F. M. Breitenbach & Thorsten Hoffmann, 2022. "Linked fire activity and climate whiplash in California during the early Holocene," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  32. Yann Guédon, 2013. "Exploring the latent segmentation space for the assessment of multiple change-point models," Computational Statistics, Springer, vol. 28(6), pages 2641-2678, December.
  33. Toby Dylan Hocking & Anuraag Srivastava, 2023. "Labeled optimal partitioning," Computational Statistics, Springer, vol. 38(1), pages 461-480, March.
  34. Shi, Xuesheng & Gallagher, Colin & Lund, Robert & Killick, Rebecca, 2022. "A comparison of single and multiple changepoint techniques for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  35. Brennen T. Fagan & Marina I. Knight & Niall J. MacKay & A. Jamie Wood, 2020. "Change point analysis of historical battle deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 909-933, June.
  36. Brigida, Matt & Pratt, William R., 2017. "Fake news," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 564-573.
  37. Chulwoo Han & Abderrahim Taamouti, 2017. "Partial Structural Break Identification," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 145-164, April.
  38. Xuesong Yu & Timothy W. Randolph & Hua Tang & Li Hsu, 2010. "Detecting Genomic Aberrations Using Products in a Multiscale Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 684-693, September.
  39. Aaron Paul Lowther & Rebecca Killick & Idris Arthur Eckley, 2023. "Detecting changes in mixed‐sampling rate data sequences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  40. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
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