AWS

R package aws: Adaptive Weights Smoothing. The package contains R-functions implementing the Propagation-Separation Approach to adaptive smoothing as described in J. Polzehl and V. Spokoiny (2006), Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields, 135(3):335–362. and J. Polzehl and V. Spokoiny (2004) Spatially adaptive regression estimation: Propagation-separation approach, WIAS-Preprint 998.


References in zbMATH (referenced in 43 articles )

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  1. Luo, S.; Song, R.; Styner, M.; Gilmore, J. H.; Zhu, H.: FSEM: functional structural equation models for twin functional data (2019)
  2. Polzehl, Jörg; Tabelow, Karsten: Magnetic resonance brain imaging. Modeling and data analysis using R (2019)
  3. Ma, Li; Soriano, Jacopo: Efficient functional ANOVA through wavelet-domain Markov groves (2018)
  4. Jin, Qiyu; Grama, Ion; Kervrann, Charles; Liu, Quansheng: Nonlocal means and optimal weights for noise removal (2017)
  5. Tian, MaoZai; Chan, Ngai Hang: Adaptive quantile regression with precise risk bounds (2017)
  6. Geffray, S.; Klutchnikoff, N.; Vimond, M.: Illumination problems in digital images. A statistical point of view (2016)
  7. Ernest, Jan; Bühlmann, Peter: Marginal integration for nonparametric causal inference (2015)
  8. Kolbe, Jens; Schulz, Rainer; Wersing, Martin; Werwatz, Axel: Identifying Berlin’s land value map using adaptive weights smoothing (2015)
  9. Becker, Saskia M. A.; Mathé, Peter: A different perspective on the propagation-separation approach (2013)
  10. Wang, Jiaping; Zhu, Hongtu; Fan, Jianqing; Giovanello, Kelly; Lin, Weili: Multiscale adaptive smoothing models for the hemodynamic response function in fMRI (2013)
  11. Dalalyan, Arnak S.; Salmon, Joseph: Sharp oracle inequalities for aggregation of affine estimators (2012)
  12. Deledalle, Charles-Alban; Duval, Vincent; Salmon, Joseph: Non-local methods with shape-adaptive patches (NLM-SAP) (2012)
  13. Frick, Klaus; Marnitz, Philipp; Munk, Axel: Statistical multiresolution Dantzig estimation in imaging: fundamental concepts and algorithmic framework (2012)
  14. Hotz, Thomas; Marnitz, Philipp; Stichtenoth, Rahel; Davies, Laurie; Kabluchko, Zakhar; Munk, Axel: Locally adaptive image denoising by a statistical multiresolution criterion (2012)
  15. Skup, Martha; Zhu, Hongtu; Zhang, Heping: Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates (2012)
  16. Thon, Kevin; Rue, Håvard; Skrøvseth, Stein Olav; Godtliebsen, Fred: Bayesian multiscale analysis of images modeled as Gaussian Markov random fields (2012)
  17. Cho, Haeran; Fryzlewicz, Piotr: Multiscale interpretation of taut string estimation and its connection to unbalanced Haar wavelets (2011)
  18. Li, Yimei; Zhu, Hongtu; Shen, Dinggang; Lin, Weili; Gilmore, John H.; Ibrahim, Joseph G.: Multiscale adaptive regression models for neuroimaging data (2011)
  19. Su, Yu-Nan; Tian, Mao-Zai: Adaptive local linear quantile regression (2011)
  20. Katkovnik, Vladimir; Foi, Alessandro; Egiazarian, Karen; Astola, Jaakko: From local kernel to nonlocal multiple-model image denoising (2010) ioport

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Further publications can be found at: http://www.wias-berlin.de/research/rts/adasmooth/index.jsp?lang=1#publications