LSE creators

Number of items: 72.
Article
  • Li, Mengbing, Shi, Chengchun, Wu, Zhenke, Fryzlewicz, Piotr (2025). Testing stationarity and change point detection in reinforcement learning. Annals of Statistics, 53(3), 1230 - 1256. https://doi.org/10.1214/25-aos2501 picture_as_pdf
  • Gavioli-Akilagun, Shakeel, Fryzlewicz, Piotr (2025). Fast and optimal inference for change points in piecewise polynomials via differencing. Electronic Journal of Statistics, 19(1), 593 - 655. https://doi.org/10.1214/25-ejs2345 picture_as_pdf
  • Fryzlewicz, Piotr (2024). Robust Narrowest Significance Pursuit: inference for multiple change-points in the median. Journal of Business and Economic Statistics, 42(4), 1389-1402. https://doi.org/10.1080/07350015.2024.2316103 picture_as_pdf
  • Baranowski, Rafal, Chen, Yining, Fryzlewicz, Piotr (2024). Multiscale autoregression on adaptively detected timescales. Statistica Sinica, picture_as_pdf
  • Cho, Haeran, Fryzlewicz, Piotr (2024). Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm. Journal of Time Series Analysis, 45(3), 479 - 494. https://doi.org/10.1111/jtsa.12722 picture_as_pdf
  • Fryzlewicz, Piotr (2024). Book review: Telling stories with data: with applications in R. American Statistician, 78(4), 488 - 490. https://doi.org/10.1080/00031305.2024.2339562 picture_as_pdf
  • Li, Jie, Fearnhead, Paul, Fryzlewicz, Piotr, Wang, Tengyao (2024). Authors' reply to the discussion of 'Automatic change-point detection in time series via deep learning' at the discussion meeting on 'Probabilistic and statistical aspects of machine learning'. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 86(2), 332 - 334. https://doi.org/10.1093/jrsssb/qkae008 picture_as_pdf
  • Li, Jie, Fearnhead, Paul, Fryzlewicz, Piotr, Wang, Tengyao (2024). Automatic change-point detection in time series via deep learning. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 86(2), 273 - 285. https://doi.org/10.1093/jrsssb/qkae004 picture_as_pdf
  • Fryzlewicz, Piotr (2023). Narrowest Significance Pursuit: inference for multiple change-points in linear models. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2023.2211733 picture_as_pdf
  • Maeng, Hyeyoung, Fryzlewicz, Piotr (2023). Detecting linear trend changes in data sequences. Statistical Papers, 16, https://doi.org/10.1007/s00362-023-01458-5 picture_as_pdf
  • Li, Yu-Ning, Li, Degui, Fryzlewicz, Piotr (2022). Detection of multiple structural breaks in large covariance matrices. Journal of Business and Economic Statistics, https://doi.org/10.1080/07350015.2022.2076686 picture_as_pdf
  • Yuen, Christine, Fryzlewicz, Piotr (2022). Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization. Journal of Computational and Graphical Statistics, 31(2), 351 - 359. https://doi.org/10.1080/10618600.2021.2000421 picture_as_pdf
  • Anastasiou, Andreas, Fryzlewicz, Piotr (2022). Detecting multiple generalized change-points by isolating single ones. Metrika, 85(2), 141 - 174. https://doi.org/10.1007/s00184-021-00821-6 picture_as_pdf
  • Anastasiou, Andreas, Cribben, Ivor, Fryzlewicz, Piotr (2022). Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity. Medical Image Analysis, 75, https://doi.org/10.1016/j.media.2021.102252 picture_as_pdf
  • Blaser, Rico, Fryzlewicz, Piotr (2021). Regularizing axis-aligned ensembles via data rotations that favor simpler learners. Statistics and Computing, 31(2). https://doi.org/10.1007/s11222-020-09973-3 picture_as_pdf
  • Fryzlewicz, Piotr (2020). Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection. Journal of the Korean Statistical Society, 49(4), 1027 - 1070. https://doi.org/10.1007/s42952-020-00060-x picture_as_pdf
  • Fryzlewicz, Piotr (2020). Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection—rejoinder. Journal of the Korean Statistical Society, 49(4), 1099 - 1105. https://doi.org/10.1007/s42952-020-00085-2 picture_as_pdf
  • Baranowski, Rafal, Chen, Yining, Fryzlewicz, Piotr (2020). Ranking-based variable selection for high-dimensional data. Statistica Sinica, 30(3), 1485 - 1516. https://doi.org/10.5705/ss.202017.0139
  • Antier, S, Barynova, K, Fryzlewicz, Piotr, Lauchard, C, Marchal-Duval, G (2020). Detection of gamma-ray transients with wild binary segmentation. Monthly Notices of the Royal Astronomical Society, 493(3), 4428 – 4441. https://doi.org/10.1093/mnras/staa263 picture_as_pdf
  • Kley, Tobias, Preuss, Philip, Fryzlewicz, Piotr (2019). Predictive, finite-sample model choice for time series under stationarity and non-stationarity. Electronic Journal of Statistics, 13(2), 3710 - 3774. https://doi.org/10.1214/19-EJS1606 picture_as_pdf
  • Baranowski, Rafal, Chen, Yining, Fryzlewicz, Piotr (2019). Narrowest-over-threshold detection of multiple change points and change-point-like features. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 81(3), 649 - 672. https://doi.org/10.1111/rssb.12322 picture_as_pdf
  • Maeng, Hye Young, Fryzlewicz, Piotr (2019). Regularised forecasting via smooth-rough partitioning of the regression coefficients. Electronic Journal of Statistics, 13(1), 2093-2120. https://doi.org/10.1214/19-EJS1573 picture_as_pdf
  • Fryzlewicz, Piotr (2018). Tail-greedy bottom-up data decompositions and fast mulitple change-point detection. Annals of Statistics, 46(6B), 3390-3421. https://doi.org/10.1214/17-AOS1662
  • Fryzlewicz, Piotr (2018). Likelihood ratio Haar variance stabilization and normalization for Poisson and other non-Gaussian noise removal. Statistica Sinica, 28, 2885-2901. https://doi.org/10.5705/ss.202017.0029
  • Barigozzi, Matteo, Cho, Haeran, Fryzlewicz, Piotr (2018). Simultaneous multiple change-point and factor analysis for high-dimensional time series. Journal of Econometrics, 206(1), 187-225. https://doi.org/10.1016/j.jeconom.2018.05.003
  • Huang, Na, Fryzlewicz, Piotr (2018). NOVELIST estimator of large correlation and covariance matrices and their inverses. Test, https://doi.org/10.1007/s11749-018-0592-4
  • Hamilton, Jean, Nunes, Matthew A., Knight, Marina I., Fryzlewicz, Piotr (2018). Complex-valued wavelet lifting and applications. Technometrics, 60(1), 48-60. https://doi.org/10.1080/00401706.2017.1281846
  • Kang, Xinyu, Fryzlewicz, Piotr, Chu, Catherine, Kramer, Mark, Kolaczyk, Eric D. (2018). Multiscale network analysis through tail-greedy bottom-up approximation, with applications in neuroscience. 2017 51st Asilomar Conference on Signals, Systems, and Computers, 1549-1554. https://doi.org/10.1109/ACSSC.2017.8335617
  • Korkas, Karolos K., Fryzlewicz, Piotr (2017). Multiple change-point detection for non-stationary time series using wild binary segmentation. Statistica Sinica, 27(1), 287-311. https://doi.org/10.5705/ss.202015.0262
  • Blaser, Rico, Fryzlewicz, Piotr (2016). Random rotation ensembles. Journal of Machine Learning Research, 17(4), 1-26.
  • Fryzlewicz, Piotr, Timmermans, Catherine (2016). SHAH: SHape-Adaptive Haar wavelets for image processing. Journal of Computational and Graphical Statistics, 25(3), 879-898. https://doi.org/10.1080/10618600.2015.1048345
  • Cho, Haeran, Fryzlewicz, Piotr (2015). Multiple-change-point detection for high dimensional time series via sparsified binary segmentation. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 77(2), 475 - 507. https://doi.org/10.1111/rssb.12079
  • Valenzuela, Marcela, Zer, Ilknur, Fryzlewicz, Piotr, Rheinlander, Thorsten (2015). Relative liquidity and future volatility. Journal of Financial Markets, 24, 25-48. https://doi.org/10.1016/j.finmar.2015.03.001
  • Fryzlewicz, Piotr (2014). Wild binary segmentation for multiple change-point detection. Annals of Statistics, 42(6), 2243-2281. https://doi.org/10.1214/14-AOS1245
  • Fryzlewicz, Piotr, Subba Rao, Suhasini (2014). Multiple-change-point detection for auto-regressive conditional heteroscedastic processes. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 76(5), 903-924. https://doi.org/10.1111/rssb.12054
  • Fryzlewicz, P. (2013). High-dimensional volatility matrix estimation via waveletsand thresholding. Biometrika, 100(4), 921-938. https://doi.org/10.1093/biomet/ast033
  • Schroeder, Anna Louise, Fryzlewicz, Piotr (2013). Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery. Statistics and Its Interface, 6(4), 449-461.
  • Cho, Haeran, Fryzlewicz, Piotr (2013). Errata on 'Multiscale and multilevel technique for consistent segmentation of nonstationary time series', Statistica Sinica (2012), vol. 22, no. 1, pp. 207-229. Statistical Science, 23(4), p. 1793.
  • Cho, Haeran, Fryzlewicz, Piotr (2012). High dimensional variable selection via tilting. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 74(3), 593-622. https://doi.org/10.1111/j.1467-9868.2011.01023.x
  • Fryzlewicz, Piotr (2012). Rejoinder: time-threshold maps: using information from wavelet reconstructions with all threshold values simultaneously. Journal of the Korean Statistical Society, 41(2), 173-175. https://doi.org/10.1016/j.jkss.2012.02.008
  • Fryzlewicz, Piotr (2012). Time–Threshold Maps: Using information from wavelet reconstructions with all threshold values simultaneously. Journal of the Korean Statistical Society, 41(2), 145-159. https://doi.org/10.1016/j.jkss.2012.02.006
  • Cho, Haeran, Fryzlewicz, Piotr (2012). Multiscale and multilevel technique for consistent segmentation of nonstationary time series. Statistica Sinica, 22(1), 207-229. https://doi.org/10.5705/ss.2009.280
  • Fryzlewicz, Piotr, Oh, H. S. (2011). Thick pen transformation for time series. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 73(4), 499-529. https://doi.org/10.1111/j.1467-9868.2011.00773.x
  • Fryzlewicz, Piotr, Subba Rao, Suhasini (2011). Mixing properties of ARCH and time-varying ARCH processes. Bernoulli, 17(1), 320-346. https://doi.org/10.3150/10-BEJ270
  • Cho, Haeran, Fryzlewicz, Piotr (2011). Multiscale interpretation of taut string estimation and its connection to Unbalanced Haar wavelets. Statistics and Computing, 21, 671-681. https://doi.org/10.1007/s11222-010-9200-5
  • Antoniadis, Anestis, Fryzlewicz, Piotr, Letué, Frédérique (2010). The Dantzig selector in Cox's proportional hazards model. Scandinavian Journal of Statistics, 37(4), 531-552. https://doi.org/10.1111/j.1467-9469.2009.00685.x
  • Sanderson, Jean, Fryzlewicz, Piotr, Jones, M. W. (2010). Estimating linear dependence between nonstationary time series using the locally stationary wavelet model. Biometrika, 97(2), 435-446. https://doi.org/10.1093/biomet/asq007
  • Fryzlewicz, Piotr, Oh, Hee-Seok (2010). On the thick-pen transformation for time series. Oberwolfach Reports, 7(1), 179-216. https://doi.org/10.4171/OWR/2010/05
  • Fryzlewicz, Piotr (2010). Wavelet methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(6), 654-667. https://doi.org/10.1002/wics.124
  • Fryzlewicz, Piotr, Ombao, Hernando (2009). Consistent classification of non-stationary time series using stochastic wavelet representations. Journal of the American Statistical Association, 104(485), 299-312. https://doi.org/10.1198/jasa.2009.0110
  • Fryzlewicz, Piotr, Nason, Guy P., von Sachs, Rainer (2008). A wavelet-Fisz approach to spectrum estimation. Journal of Time Series Analysis, 29(5), 868-880. https://doi.org/10.1111/j.1467-9892.2008.00586.x
  • Fryzlewicz, Piotr (2008). Data-driven wavelet-Fisz methodology for nonparametric function estimation. Electronic Journal of Statistics, 2, 863-896. https://doi.org/10.1214/07-EJS139
  • Fryzlewicz, Piotr, Sapatinas, Theofanis, Subba Rao, Suhasini (2008). Normalized least-squares estimation in time-varying ARCH models. Annals of Statistics, 36(2), 742-786. https://doi.org/10.1214/07-AOS510
  • Fryzlewicz, Piotr (2007). Unbalanced Haar technique for nonparametric function estimation. Journal of the American Statistical Association, 102(480), 1318-1327. https://doi.org/10.1198/016214507000000860
  • Fryzlewicz, Piotr (2007). Bivariate hard thresholding in wavelet function estimation. Statistica Sinica, 17(4), 1457-1481.
  • Fryzlewicz, Piotr, Delouille, V´eronique, Nason, Guy P. (2007). GOES-8 X-ray sensor variance stabilization using the multiscale data-driven Haar-Fisz transform. Journal of the Royal Statistical Society. Series C: Applied Statistics, 56(1), 99-116. https://doi.org/10.1111/j.1467-9876.2007.00567.x
  • Fryzlewicz, Piotr, Sapatinas, Theofanis, Subba Rao, Suhasini (2006). A Haar-Fisz technique for locally stationary volatility estimation. Biometrika, 93(3), 687-704. https://doi.org/10.1093/biomet/93.3.687
  • Motakis, E. S., Nason, Guy P., Fryzlewicz, Piotr, Rutter, G. A (2006). Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach. Bioinformatics, 22(20), 2547-2553. https://doi.org/10.1093/bioinformatics/btl412
  • Antoniadis, Anestis, Fryzlewicz, Piotr (2006). Parametric modelling of thresholds across scales in wavelet regression. Biometrika, 93(2), 465-471. https://doi.org/10.1093/biomet/93.2.465
  • Fryzlewicz, Piotr, Nason, Guy P. (2006). Haar-Fisz estimation of evolutionary wavelet spectra. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 68(4), 611-634. https://doi.org/10.1111/j.1467-9868.2006.00558.x
  • Fryzlewicz, Piotr (2005). Modelling and forecasting financial log-returns as locally stationary wavelet processes. Journal of Applied Statistics, 32(5), 503-528.
  • Fryzlewicz, Piotr, Nason, Guy P. (2004). A Haar-Fisz algorithm for poisson intensity estimation. Journal of Computational and Graphical Statistics, 13(3), 621-638. https://doi.org/10.1198/106186004X2697
  • Fryzlewicz, Piotr, van Bellegem, Sébastien, von Sachs, Rainer (2003). Forecasting non-stationary time series by wavelet process modelling. Annals of the Institute of Statistical Mathematics, 55(4), 737-764. https://doi.org/10.1007/BF02523391
    • Chapter
  • Sanderson, Jean, Fryzlewicz, Piotr (2007). Locally stationary wavelet coherence with application to neuroscience. In Barber, S., Baxter, P. D., Mardia, K. V. (Eds.), Systems Biology and Statistical Bioinformatics (pp. 69-72). Leeds University Press.
  • Fryzlewicz, Piotr, Delouille, V (2006). A data-driven HAAR-FISZ transform for multiscale variance stabilization. In Proceedings of the 13th IEEE/Sp Workshop on Statistical Signal Processing (pp. 539-544). IEEE. https://doi.org/10.1109/SSP.2005.1628654
  • van Bellegem, Sébastien, Fryzlewicz, Piotr, von Sachs, Rainer (2003). A wavelet-based model for forecasting non-stationary processes. In Gazeau, J-P, Kerner, R, Antoine, J-P, Metens, S (Eds.), Group 24: Physical and Mathematical Aspects of Symmetries: Proceedings of the 24th International Colloquium on Group Theoretical (pp. 955-958). Institute of Physics Publishing.
    • Conference or Workshop Item
  • Cho, Haeran, Fryzlewicz, Piotr (2008-12-05 - 2008-12-08) Multiscale breakpoint detection in piecewise stationary AR models [Paper]. IASC2008, Yokohama, Japan, JPN.
  • Sanderson, Jean, Fryzlewicz, Piotr (2007-08-22 - 2007-08-29) Locally stationary wavelet coherence with application to neuroscience [Paper]. Proceedings of the 56th session of the International Statistical Institute, Lisbon, Portugal, PRT.
    • Report
  • Fryzlewicz, Piotr, Nason, Guy P. (2004). Smoothing the wavelet periodogram using the Haar-Fisz transform. (Technical report 03:08). Department of Mathematics, University of Bristol.
    • Thesis
  • Fryzlewicz, Piotr (2003). Wavelet techniques for time series and poisson data. [Doctoral thesis]. University of Bristol.
  • Fryzlewicz, Piotr (2000). The application of linear programming to American option valuation in the jump-diffusion model [Doctoral thesis]. University of Wrocław.
    • Working paper
  • Christodoulaki, Olga, Cho, Haeran, Fryzlewicz, Piotr (2011). A reflection of history: fluctuations in Greek sovereign risk between 1914 and 1929. (GreeSE 50). Hellenic Observatory, London School of Economics and Political Science.