Dr. R. Todd Ogden has interests in a wide variety of topics in both statistical methodology and various application areas. He is currently collaborating with researchers at the New York State Psychiatric Institute on various statistical modeling issues with the analysis of data from brain imaging studies. Other ongoing interests include functional data analysis, nonparametric regression, wavelet methods, statistical modeling, statistical computing, and statistical education.
Honors & Awards
Chang, C, Chen, Y, and Ogden, RT. Functional data classification: A wavelet approach, in press, Computational Statistics.
Zhao, Y, Chen, H, and Ogden, RT. Wavelet-based weighted LASSO and screening approaches in functional linear regression, in press, Journal of Computational and Graphical Statistics.
Reiss, PT and Ogden, RT (2010). Functional generalized linear models with images as predictors. Biometrics 66: 61-69.
Ogden, RT and Greene, E. (2010). Wavelet modeling of functional random effects with application to human vision data. Journal of Statistical Planning and Inference 140: 3797-3808.
Ogden, RT and Jiang, H (2010). Nonparametric evaluation of heterogeneity of brain regions in neuroreceptor mapping applications. Statistics and Its Interface 3: 59-67.
Reiss, PT and Ogden, RT (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society, Series B 71:505-523.
Jiang, H and Ogden, RT (2008). Mixture modeling for dynamic PET data. Statistica Sinica 18 1341-1356.
Reiss, PT and Ogden, RT (2007). Functional principal component regression and functional partial least squares. Journal of the American Statistical Association 102:984-996.
Ogden, RT and Tarpey, T (2006). Estimation in regression models with externally estimated parameters. Biostatistics 7:115-129.
Ogden, RT (1997). Essential Wavelets for Statistical Applications and Data Analysis. Birkhauser: Boston.