Spatially Varying Coefficient Model for Neuroimaging Data With Jump Discontinuities
Author
Abstract
Suggested Citation
DOI: 10.1080/01621459.2014.881742
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yimei Li & Hongtu Zhu & Dinggang Shen & Weili Lin & John H. Gilmore & Joseph G. Ibrahim, 2011. "Multiscale adaptive regression models for neuroimaging data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 559-578, September.
- Qiu, Peihua, 2007. "Jump Surface Estimation, Edge Detection, and Image Restoration," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 745-756, June.
- Spence, Jeffrey S. & Carmack, Patrick S. & Gunst, Richard F. & Schucany, William R. & Woodward, Wayne A. & Haley, Robert W., 2007. "Accounting for Spatial Dependence in the Analysis of SPECT Brain Imaging Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 464-473, June.
- J. Polzehl & V. G. Spokoiny, 2000. "Adaptive weights smoothing with applications to image restoration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 335-354.
- J. Fan & J.‐T. Zhang, 2000. "Two‐step estimation of functional linear models with applications to longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 303-322.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Philip T. Reiss & Lei Huang & Pei‐Shien Wu & Huaihou Chen & Stan Colcombe, 2017. "Pointwise influence matrices for functional‐response regression," Biometrics, The International Biometric Society, vol. 73(4), pages 1092-1101, December.
- Yan-Yong Zhao & Jin-Guan Lin & Hong-Xia Wang & Xing-Fang Huang, 2017. "Jump-detection-based estimation in time-varying coefficient models and empirical applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 574-599, September.
- Serfling, Robert & Wijesuriya, Uditha, 2017. "Depth-based nonparametric description of functional data, with emphasis on use of spatial depth," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 24-45.
- Giraldo, Ramón & Dabo-Niang, Sophie & Martínez, Sergio, 2018. "Statistical modeling of spatial big data: An approach from a functional data analysis perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 126-129.
- Baiguo An & Beibei Zhang, 2020. "Logistic regression with image covariates via the combination of L1 and Sobolev regularizations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
- Li, Ting & Shi, Chengchun & Lu, Zhaohua & Li, Yi & Zhu, Hongtu, 2024. "Evaluating dynamic conditional quantile treatment effects with applications in ridesharing," LSE Research Online Documents on Economics 122488, London School of Economics and Political Science, LSE Library.
- Brian J. Reich & Joseph Guinness & Simon N. Vandekar & Russell T. Shinohara & Ana†Maria Staicu, 2018. "Fully Bayesian spectral methods for imaging data," Biometrics, The International Biometric Society, vol. 74(2), pages 645-652, June.
- Arnab Hazra & Brian J. Reich & Daniel S. Reich & Russell T. Shinohara & Ana-Maria Staicu, 2019. "A Spatio-Temporal Model for Longitudinal Image-on-Image Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 22-46, April.
- Zhengjia Wang & John Magnotti & Michael S. Beauchamp & Meng Li, 2023. "Functional group bridge for simultaneous regression and support estimation," Biometrics, The International Biometric Society, vol. 79(2), pages 1226-1238, June.
- Xinchao Luo & Lixing Zhu & Hongtu Zhu, 2016. "Single‐index varying coefficient model for functional responses," Biometrics, The International Biometric Society, vol. 72(4), pages 1275-1284, December.
- Čížek, Pavel & Koo, Chao Hui, 2021.
"Jump-preserving varying-coefficient models for nonlinear time series,"
Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
- Cizek, Pavel & Koo, Chao, 2017. "Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series," Discussion Paper 2017-017, Tilburg University, Center for Economic Research.
- Cizek, Pavel & Koo, Chao, 2017. "Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series," Other publications TiSEM c849e96f-3ad1-461e-96c6-f, Tilburg University, School of Economics and Management.
- Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
- Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
- Xiong Cai & Liugen Xue & Xiaolong Pu & Xingyu Yan, 2021. "Efficient Estimation for Varying-Coefficient Mixed Effects Models with Functional Response Data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(4), pages 467-495, May.
- Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
- Zhao, Yan-Yong & Lin, Jin-Guan, 2019. "Estimation and test of jump discontinuities in varying coefficient models with empirical applications," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 145-163.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Čížek, Pavel & Koo, Chao Hui, 2021.
"Jump-preserving varying-coefficient models for nonlinear time series,"
Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
- Cizek, Pavel & Koo, Chao, 2017. "Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series," Discussion Paper 2017-017, Tilburg University, Center for Economic Research.
- Cizek, Pavel & Koo, Chao, 2017. "Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series," Other publications TiSEM c849e96f-3ad1-461e-96c6-f, Tilburg University, School of Economics and Management.
- Peihua Qiu, 2009. "Jump-preserving surface reconstruction from noisy data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 715-751, September.
- Qiu, Peihua, 2008. "A nonparametric procedure for blind image deblurring," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4828-4841, June.
- Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
- Baiguo An & Beibei Zhang, 2020. "Logistic regression with image covariates via the combination of L1 and Sobolev regularizations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
- Martha Skup & Hongtu Zhu & Heping Zhang, 2012. "Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates," Biometrics, The International Biometric Society, vol. 68(4), pages 1083-1092, December.
- Hotz, Thomas & Marnitz, Philipp & Stichtenoth, Rahel & Davies, Laurie & Kabluchko, Zakhar & Munk, Axel, 2012. "Locally adaptive image denoising by a statistical multiresolution criterion," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 543-558.
- Xueying Zheng & Wing Fung & Zhongyi Zhu, 2013. "Robust estimation in joint mean–covariance regression model for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 617-638, August.
- Şentürk, Damla & Ghosh, Samiran & Nguyen, Danh V., 2014. "Exploratory time varying lagged regression: Modeling association of cognitive and functional trajectories with expected clinic visits in older adults," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 1-15.
- Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2012.
"Location, Location, Location: Extracting Location Value from House Prices,"
Discussion Papers of DIW Berlin
1216, DIW Berlin, German Institute for Economic Research.
- Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2013. "Location, location, location: Extracting location value from house prices," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79732, Verein für Socialpolitik / German Economic Association.
- Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2012. "Location, location, location: Extracting location value from house prices," SFB 649 Discussion Papers 2012-040, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
- Xinchao Luo & Lixing Zhu & Hongtu Zhu, 2016. "Single‐index varying coefficient model for functional responses," Biometrics, The International Biometric Society, vol. 72(4), pages 1275-1284, December.
- Geffray, S. & Klutchnikoff, N. & Vimond, M., 2016. "Illumination problems in digital images. A statistical point of view," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 191-213.
- Wang, Qihua & Zhang, Riquan, 2009. "Statistical estimation in varying coefficient models with surrogate data and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2389-2405, November.
- Billé, AG & Salvioni, C. & Benedetti, R., 2015.
"Spatial Heterogeneity In Production Functions Models,"
150th Seminar, October 22-23, 2015, Edinburgh, Scotland
212662, European Association of Agricultural Economists.
- Sille, AG & Salvioni, C. & Benedetti, R., 2015. "Spatial Heterogeneity in Production Functions Models," 2015 Conference, August 9-14, 2015, Milan, Italy 211343, International Association of Agricultural Economists.
- Konrad Abramowicz & Alessia Pini & Lina Schelin & Sara Sjöstedt de Luna & Aymeric Stamm & Simone Vantini, 2023. "Domain selection and familywise error rate for functional data: A unified framework," Biometrics, The International Biometric Society, vol. 79(2), pages 1119-1132, June.
- Michelle F. Miranda & Hongtu Zhu & Joseph G. Ibrahim, 2013. "Bayesian Spatial Transformation Models with Applications in Neuroimaging Data," Biometrics, The International Biometric Society, vol. 69(4), pages 1074-1083, December.
- Geenens, Gery, 2015. "Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 369-377.
- Tang Qingguo & Cheng Longsheng, 2008. "M-estimation and B-spline approximation for varying coefficient models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(7), pages 611-625.
- Helbing, Georg & Shen, Zhiwei & Odening, Martin & Ritter, Matthias, 2017.
"Estimating Location Values of Agricultural Land,"
German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 66(3), September.
- Ritter, Matthias & Helbing, Georg & Shen, Zhiwei & Odening, Martin, 2017. "Estimating Location Values of Agricultural Land," 57th Annual Conference, Weihenstephan, Germany, September 13-15, 2017 261985, German Association of Agricultural Economists (GEWISOLA).
- Helbing, Georg & Shen, Zhiwei & Odening, Martin & Ritter, Matthias, 2017. "Estimating location values of agricultural land," SFB 649 Discussion Papers 2017-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:109:y:2014:i:507:p:1084-1098. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.