Statistical Methods for Magnetic Resonance Image Analysis with Applications to Multiple Sclerosis.

[1]  T. Taoka,et al.  Early Contrast‐Enhanced Magnetic Resonance Imaging with Fluid‐Attenuated Inversion Recovery in Multiple Sclerosis , 2009, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[2]  Ron Reeder,et al.  Estimation of the mean of functional time series and a two‐sample problem , 2011, 1105.0019.

[3]  Lianfen Qian,et al.  Nonparametric Curve Estimation: Methods, Theory, and Applications , 1999, Technometrics.

[4]  Brian S. Caffo,et al.  Multilevel functional principal component analysis , 2009 .

[5]  S. Cosslett,et al.  Maximum likelihood estimator for choice-based samples , 1981 .

[6]  C. Crainiceanu,et al.  Predicting Breakdown of the Blood-Brain Barrier in Multiple Sclerosis without Contrast Agents , 2012, American Journal of Neuroradiology.

[7]  David H. Miller,et al.  Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain , 2004, Annals of neurology.

[8]  J. Ramsay,et al.  The historical functional linear model , 2003 .

[9]  Gerhard Bohm,et al.  Introduction to Statistics and Data Analysis for Physicists , 2017 .

[10]  B. Silverman,et al.  Estimating the mean and covariance structure nonparametrically when the data are curves , 1991 .

[11]  Frédéric Ferraty,et al.  Factor-based comparison of groups of curves , 2007, Comput. Stat. Data Anal..

[12]  J. Friedman,et al.  Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests , 1979 .

[13]  Ingrid Van Keilegom,et al.  Two-sample tests in functional data analysis starting from discrete data , 2007 .

[14]  Russell T. Shinohara,et al.  Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis , 2011, NeuroImage.

[15]  A. Compston,et al.  Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis , 2001, Annals of neurology.

[16]  P. Hall,et al.  Properties of principal component methods for functional and longitudinal data analysis , 2006, math/0608022.

[17]  Marina Vannucci,et al.  Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis , 2003 .

[18]  G. Kauermann,et al.  A Note on Penalized Spline Smoothing With Correlated Errors , 2007 .

[19]  David H. Miller,et al.  A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. , 2002, The New England journal of medicine.

[20]  Runze Li,et al.  Recent History Functional Linear Models for Sparse Longitudinal Data. , 2011, Journal of statistical planning and inference.

[21]  D. Reich,et al.  Initial investigation of the blood-brain barrier in MS lesions at 7 tesla , 2013, Multiple sclerosis.

[22]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[23]  J. Ramsay,et al.  Some Tools for Functional Data Analysis , 1991 .

[24]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[25]  H. Müller,et al.  Functional Data Analysis for Sparse Longitudinal Data , 2005 .

[26]  Guido W. Imbens,et al.  An efficient method of moments estimator for discrete choice models with choice-based sampling , 1992 .

[27]  José A. Vilar,et al.  Functional ANOVA starting from discrete data: an application to air quality data , 2013, Environmental and Ecological Statistics.

[28]  B. Silverman,et al.  Functional Data Analysis , 1997 .

[29]  Massimo Filippi,et al.  Seven‐tesla phase imaging of acute multiple sclerosis lesions: A new window into the inflammatory process , 2013, Annals of neurology.

[30]  S. Wood,et al.  Generalized Additive Models: An Introduction with R , 2006 .

[31]  S. Wood Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models , 2004 .

[32]  Aaron Carass,et al.  Simple paradigm for extra-cerebral tissue removal: Algorithm and analysis , 2011, NeuroImage.

[33]  Jin-Ting Zhang,et al.  Statistical inferences for functional data , 2007, 0708.2207.

[34]  Jane-ling Wang,et al.  Functional linear regression analysis for longitudinal data , 2005, math/0603132.

[35]  Noel A Cressie,et al.  The asymptotic distribution of REML estimators , 1993 .

[36]  Alois Kneip,et al.  Common Functional Principal Components , 2006, 0901.4252.

[37]  Ana-Maria Staicu,et al.  Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis , 2014 .

[38]  John M. Lachin,et al.  Two-Sample Asymptotically Distribution-Free Tests for Incomplete Multivariate Observations , 1984 .

[39]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[40]  Xavier Lladó,et al.  Automated detection of multiple sclerosis lesions in serial brain MRI , 2012, Neuroradiology.

[41]  Mathew W. McLean,et al.  Journal of Computational and Graphical Statistics Functional Generalized Additive Models Functional Generalized Additive Models Accepted Manuscript Accepted Manuscript , 2022 .

[42]  E. Poch,et al.  Nephrogenic systemic fibrosis: a case series suggesting gadolinium as a possible aetiological factor , 2007, The British journal of dermatology.

[43]  J. Ramsay,et al.  Principal components analysis of sampled functions , 1986 .

[44]  Ana-Maria Staicu,et al.  Fast methods for spatially correlated multilevel functional data. , 2010, Biostatistics.

[45]  Ciprian M. Crainiceanu,et al.  refund: Regression with Functional Data , 2013 .

[46]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[47]  Aaron Carass,et al.  A JOINT REGISTRATION AND SEGMENTATION APPROACH TO SKULL STRIPPING , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[48]  D. N. Landon,et al.  Duration and selectivity of blood-brain barrier breakdown in chronic relapsing experimental allergic encephalomyelitis studied by gadolinium-DTPA and protein markers. , 1990, Brain : a journal of neurology.

[49]  R. Todd Ogden,et al.  Smoothing parameter selection for a class of semiparametric linear models , 2009 .

[50]  Gary King,et al.  Logistic Regression in Rare Events Data , 2001, Political Analysis.

[51]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[52]  Ciprian M Crainiceanu,et al.  Penalized Functional Regression , 2011, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[53]  A. Pettitt A two-sample Anderson-Darling rank statistic , 1976 .

[54]  D. Arnold,et al.  Combining Robust Expectation Maximization and Mean Shift algorithms for Multiple Sclerosis Brain Segmentation , 2008 .

[55]  M. Schilling Multivariate Two-Sample Tests Based on Nearest Neighbors , 1986 .

[56]  R. Pyke,et al.  Logistic disease incidence models and case-control studies , 1979 .

[57]  Piotr Kokoszka,et al.  Testing the Equality of Covariance Operators in Functional Samples , 2011, 1104.4049.

[58]  S. Wood Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .

[59]  J A Frank,et al.  Standardized MR imaging protocol for multiple sclerosis: Consortium of MS Centers consensus guidelines. , 2006, AJNR. American journal of neuroradiology.

[60]  Alex Rovira,et al.  Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches , 2012, Inf. Sci..

[61]  F. Bagnato Uncovering and Characterizing Multiple Sclerosis Lesions: The Aid of Fluid‐Attenuated Inversion Recovery Images in the Presence of Gadolinium Contrast Agent , 2009, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[62]  M. Stephens,et al.  K-Sample Anderson–Darling Tests , 1987 .

[63]  G. Zech,et al.  New test for the multivariate two-sample problem based on the concept of minimum energy , 2003 .

[64]  Xinwei Deng,et al.  Estimation in high-dimensional linear models with deterministic design matrices , 2012, 1206.0847.

[65]  Kathleen F. Kerr,et al.  Testing for improvement in prediction model performance , 2013, Statistics in medicine.

[66]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[67]  Ana-Maria Staicu,et al.  Modeling Functional Data with Spatially Heterogeneous Shape Characteristics , 2012, Biometrics.

[68]  H. Müller Nonparametric regression analysis of longitudinal data , 1988 .

[69]  Hans-Georg Muller,et al.  Functional linear regression via canonical analysis , 2010, 1102.5212.

[70]  C M Crainiceanu,et al.  Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI , 2013, American Journal of Neuroradiology.

[71]  M. Perazella,et al.  Current status of gadolinium toxicity in patients with kidney disease. , 2009, Clinical journal of the American Society of Nephrology : CJASN.

[72]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[73]  Daniel S Reich,et al.  Evolution of the blood–brain barrier in newly forming multiple sclerosis lesions , 2011, Annals of neurology.