Piotr Fryzlewicz

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Guy NasonChair in Statistics, Imperial College, LondonVerified email at imperial.ac.uk
Haeran ChoUniversity of BristolVerified email at bristol.ac.uk
Suhasini Subba RaoTexas A&MVerified email at tamu.edu
Rafał BaranowskiIndependent Data Science and Quantitative Finance ConsultantVerified email at rbaranowski.com
Rainer von SachsUniversité catholique de Louvain, BelgiumVerified email at uclouvain.be
Yining ChenLondon School of EconomicsVerified email at lse.ac.uk
Andreas AnastasiouAssistant Professor of Statistics, University of CyprusVerified email at ucy.ac.cy
Jean Hamilton (Sanderson)Research Associate in Statistics, Sheffield UniversityVerified email at sheffield.ac.uk
Theofanis SapatinasProfessor of StatistcsVerified email at ucy.ac.cy
Efthymios MotakisComputational Scientist, The Jackson Laboratory, Farmington, CT, USAVerified email at jax.org
Sébastien Van BellegemCORE, Université catholique de LouvainVerified email at uclouvain.be
Karolos KorkasUniversity of GreenwichVerified email at gre.ac.uk
Matteo BarigozziFull Professor - Alma Mater Studiorum Università di BolognaVerified email at unibo.it
Hyeyoung MaengDurham UniversityVerified email at durham.ac.uk
Ilknur ZerFederal Reserve BoardVerified email at frb.gov
Paul FearnheadProfessor of Statistics; Lancaster UniversityVerified email at lancs.ac.uk
Veronique DelouilleRoyal Observatory of BelgiumVerified email at oma.be
Zhenke WuAssociate Professor of Biostatistics, University of MichiganVerified email at umich.edu
Mengbing LiUniversity of MichiganVerified email at umich.edu
Chengchun ShiLondon School of Economics and Political ScienceVerified email at lse.ac.uk

Piotr Fryzlewicz

Professor of Statistics, London School of Economics

Verified email at lse.ac.uk - Homepage

statistics time series data science machine learning econometrics


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Wild binary segmentation for multiple change-point detection P Fryzlewicz The Annals of Statistics 42 (6), 2243-2281, 2014	799	2014
Multiple-change-point detection for high dimensional time series via sparsified binary segmentation H Cho, P Fryzlewicz Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015	403	2015
A Haar-Fisz algorithm for Poisson intensity estimation P Fryzlewicz, GP Nason Journal of computational and graphical statistics 13 (3), 621-638, 2004	231	2004
Forecasting non-stationary time series by wavelet process modelling P Fryzlewicz, S Van Bellegem, R Von Sachs Annals of the Institute of Statistical Mathematics 55 (4), 737-764, 2003	171	2003
Narrowest‐over‐threshold detection of multiple change points and change‐point‐like features R Baranowski, Y Chen, P Fryzlewicz Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2019	170	2019
Random rotation ensembles R Blaser, P Fryzlewicz The Journal of Machine Learning Research 17 (1), 126-151, 2016	155	2016
Simultaneous multiple change-point and factor analysis for high-dimensional time series M Barigozzi, H Cho, P Fryzlewicz Journal of Econometrics 206 (1), 187-225, 2018	125	2018
High dimensional variable selection via tilting H Cho, P Fryzlewicz Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2012	101	2012
Multiscale and multilevel technique for consistent segmentation of nonstationary time series H Cho, P Fryzlewicz Statistica Sinica, 207-229, 2012	101	2012
Unbalanced Haar technique for nonparametric function estimation P Fryzlewicz Journal of the American Statistical Association 102 (480), 1318-1327, 2007	99	2007
Multiple-change-point detection for auto-regressive conditional heteroscedastic processes P Fryzlewicz, S Subba Rao Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2014	97*	2014
The Dantzig selector in Cox's proportional hazards model A Antoniadis, P Fryzlewicz, F Letué Scandinavian Journal of Statistics 37 (4), 531-552, 2010	95	2010
Normalized least-squares estimation in time-varying ARCH models P Fryzlewicz, T Sapatinas, S Subba Rao	94	2008
Haar–Fisz estimation of evolutionary wavelet spectra P Fryzlewicz, GP Nason Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2006	85*	2006
Multiple change-point detection for non-stationary time series using wild binary segmentation KK Korkas, P Fryzlewicz Statistica Sinica, 287-311, 2017	81	2017
A Haar–Fisz technique for locally stationary volatility estimation P Fryzlewicz, T Sapatinas, SS Rao Biometrika 93 (3), 687-704, 2006	76	2006
Mixing properties of ARCH and time-varying ARCH processes P Fryzlewicz, S Subba Rao	73	2011
Tail-greedy bottom-up data decompositions and fast multiple change-point detection P Fryzlewicz Annals of Statistics 46 (6B), 3390-3421, 2018	69	2018
Estimating linear dependence between nonstationary time series using the locally stationary wavelet model J Sanderson, P Fryzlewicz, MW Jones Biometrika 97 (2), 435-446, 2010	68	2010
Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection P Fryzlewicz Journal of the Korean Statistical Society 49 (4), 1027-1070, 2020	64	2020

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