Papers of David Ruppert (since 1999)

1999   2000   2001   2002   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   2013   2014   2015   2016   2017   2018   2019   2020   2021   2022   2023   2024  


Thurston, S., Ruppert, R., and Korrick, S. (2024) A Novel Approach to Assessing the Joint Effects of Mercury and Fish Consumption on Neurodevelopment in the New Bedford Cohort American Journal of Epidemiology, to appear.

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Kent, David, Budavari, Tamas, Loredo, Thomas, and Ruppert, David (2023) Splines 'n Lines: Rest-frame galaxy spectral energy distributions via Bayesian functional data analysis (arXiv)

Zhang, Tao, Kato, Kengo, and Ruppert, David (2023) Bootstrap Inference for Quantile-Based Modal Regression, JASA, 118, 122-134.

Zhu, Rong, Liang, Hua, and Ruppert, David (2023) Ensemble Subset Regression (ENSURE): Efficient High-dimensional Prediction, Statistica Sinica, 33, 1411-1434.

Li, Xinmin, Chen, Feifei, Liang, Hua, and Ruppert, David (2023) Model Checking for Logistic Models When the Number of Parameters Tends to Infinity, J. of Computational and Graphical Statistics, 32, 241-251.

Giloteaux, Ludo, Li, Jiayin, Hornig, Mady, Lipkin, W. Ian, Ruppert, David, and Hanson, Maureen (2023) Proteomics and cytokine analyses distinguish Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cases from controls, J. of Translation Medicine, to appear.

Hattab, Mohammed and Ruppert, David (2023) Measurement errors in semiparametric generalized linear models, Australian and New Zealand Journal of Statistics, 65, 344-363.

Li, Jiayin, Kaltenegger, L., Pham, D., and Ruppert, D. (2023) Characterization of extrasolar giant planets with machine learning, Monthly Notices of the Royal Astronomical Society, to appear

Nolan, Tui, Goldsmith, Jeff, and Ruppert, David (2023) Bayesian Functional Principal Components Analysis via Variational Message Passing, Bayesian Statistics, to appear.

Kent, David, and Ruppert, David (2023) Smoothness-Penalized Deconvolution (SPeD) of a Density Estimate, JASA, to appear. (arXiv)

Pinelis, M. and Ruppert, D. (2023) Maximizing Portfolio Predictability with Machine Learning (SSRN)

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Pinelis, M. and Ruppert, D. (2022) Machine Learning Portfolio Allocation. The Journal of Finance and Data Science, 8, 35-54. (SSRN)

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Cui, E., Thompson, E.C., Carroll, R.J., and Ruppert, D. (2021) A semiparametric risk score for physical activity, Statistics in Medicine, 41, 1191-1204.

Tang, Q., Kong, L., Ruppert,D., and Karunamuni, R. (2021) Partial Functional Partially Linear Single-Index Model, Statistica Sinica, 31, 107--132, (arXiv)

Hattab, M. and Ruppert, D. (2021) A Mixed Model Approach to Measurement Error in Semiparametric Regression, Statistics & Computing, 31, 3 (pdf) .

Wang, Haiying, Zhang, Dixin, Liang, Hua and Ruppert, David (2021) Iterative Likelihood: A unified inference tool, JCGS, 920--933.

Yang, Ran, Kent, David, Apley, Daniel, Staum, Jeremy, Ruppert, David (2021) Bias-corrected Estimation of the Density of a Conditional Expectation in Nested Simulation Problems, ACM Transactions on Modeling and Computer Simulation, 31, 1-36.

Sun, Zhihua, Chen, Feifei, Liang, Hua, and Ruppert, David (2021) A projection-based consistent test incorporating dimension-reduction in partial linear models, Statistica Sinica, 31, 1489--1508.

Zhang, Tao, Ning, Yang, and Ruppert, David (2021) Optimal Sampling for Generalized Linear Models under Measurement Constraints, J. of Computational and Graphical Statistics, 30, 106--114. (arXiv)

Huang, W. and Ruppert, D. (2021) Copula-based Functional Bayes Classification with Principal Components and Partial Least Squares, Statistics Sinica, 33, 55--84. (arXiv)

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Yang, Ran, Apley, Daniel, Staum, Jeremy, Ruppert, David (2020) Density Deconvolution with Additive Measurement Errors using Quadratic Programming, JCGS, 29, 580-591. arXiv

Liu, Yang and Ruppert, David (2020) Density estimation on a network, Computational Statistics and Data Analysis, to appear. (arXiv) (arXiv)

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Kowal, D., Matteson, D., and Ruppert, D. (2019) Functional Autoregression for Sparsely Sampled Data, JBES, 37, 97-109. (arXiv), (2017 Student Best Paper Award, Section on Nonparametric Statistics of the American Statistical Association)

Risk, B., Matteson, D., Ruppert, D. (2019) Linear Non-Gaussian Component Analysis via Maximum Likelihood, JASA, 12, 733-744, (arXiv)

Cao, J., Soiaporn, K., Carroll, R., and Ruppert, D. (2019) Modeling and Prediction of Multiple Correlated Functional Outcomes, J. of Agricultural, Biological, and Environmental Statistics, 24, 112-129.

Kowal, D., Matteson, D., and Ruppert, D. (2019) Dynamic Shrinkage Processes, JRSS-B, 81, 781-804. (pdf)

Kravitz, E., Carroll, R., and Ruppert, D. (2019) Sample Splitting as an M-Estimator with Application to Physical Activity Scoring, submitted (arXiv)

Kravitz, E., Carroll, R., and Ruppert, D. (2019) Finite Sample Hypothesis Tests for Stacked Estimating Equations (arXiv)

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Germain, A., Ruppert, D., Levine, S., and Hanson, M. (2018) Prospective biomarkers from plasma metabolomics of myalgic encephalomyelitis/chronic fatigue syndrome implicate redox imbalance in disease symptomology, Metabolites, 8, online.

Kim, J., Staicu, A-M., Maity, A., Carroll, R., and Ruppert, D. (2018) Additive function-on-function regression, JCGS, 27, 234-244. arXiv

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Kowal, D., Matteson, D., and Ruppert, D. (2017) A Bayesian Multivariate Functional Dynamic Linear Model, JASA, 112, 733-744. (arXiv) (2016 Student Best Paper Award, Section on Bayesian Statistical Science of the American Statistical Association)

Germain, A., Ruppert, D., Levine, S., and Hanson, M. (2017) Metabolic profiling of a myalgic encephalomyelitis/chronic fatigue syndrome discovery cohort reveals disturbances in fatty acid and lipid metabolism, Molecular BioSystems, 13, 371-379.

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Xiao, L., Ruppert, D., Zipunnikov, V., and Crainicenau, C. (2016) Fast Covariance Estimation for High-Dimensional Functional Data. Statistics & Computing, 26, 409-421.

Steckley, S., Henderson, S., Ruppert, D., Yang, R., Apley, D., and Staum, J. (2016) Estimating the Density of a Conditional Expectation, Electronic Journal of Statistics, 10, 736-760.

Zhang, X., Liang, H., Liu, A., Ruppert, D. and Zou, G. (2016) Selection Strategy for Covariance Structure of Random Effects in Linear Mixed-effects Models, Scandinavian Journal of Statistics, 43, 275-291.

Srivastava, R., Li, P., and Ruppert, D. (2016) RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections, JCGS, 25, 954-970. arXiv

Shetty, R. et al (2016) Simultaneously modelling far-infrared dust emission and its relation to CO emission in star forming galaxies. Monthly Notices of the Royal Astronomical Society, 460, 67-81. arXiv

Risk, B., Matteson, D., Spreng, R. N., and Ruppert, D. (2016) Spatiotemporal Mixed Modeling of Multi-subject Task fMRI via Method of Moments, NeuroImage, 142, 280-292.

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McLean, M., Hooker, G., and Ruppert, D. (2015) Restricted likelihood ratio tests for linearity in scalar-on-function regression, Statistics & Computing 25, 997-1008.

Wang, Xiao, and Ruppert, D. (2015) Optimal Prediction in an Additive Functional Model Statistica Sinica, 15, 567-589.

Lian, H., Liang, H. and Ruppert, D. (2015) Separation of Covariates into Nonparametric and Parametric Parts in High-Dimensional Partially Linear Additive Models. Statistica Sinica, 25, 591-607.

Fang, Y., Lian, H., Liang, H., and Ruppert, D , (2015) Variance Function Additive Partial Linear Models, Electronic Journal of Statistics, 9, 2793-2827.

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Xiao, L., Thurston, S., Ruppert, D., Love, T., and Davidson, P. (2014) Bayesian Models for Multiple Outcomes in Domains with Application to the Seychelles Child Development Study, JASA, 109, 1-10.

McLean, M., Hooker, G., Staicu, A-M, Scheipl, F., and Ruppert, D. (2014) Functional Generalized Additive Models, JCGS, 23, 249-269.

Risk, B., Matteson, D., Ruppert, D., Eloyan, A., and Caffo, B. (2014) An Evaluation of Independent Component Analyses with an Application to Resting State fMRI, Biometrics , 70, 224-236. (pdf) . (web supplement) .

Staicu, A-M, Li, Y., Ruppert, D., and Crainiceanu, C. M. (2014) Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis, Scandinavian Journal of Statistics, 41, 932-949 (pdf) .

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Xiao, Luo., Li, Y., and Ruppert, D. (2013) Fast Bivariate P-splines: the Sandwich Smoother, JRSS-B, 75, 577-599.

  • Xiao, Luo, Li, Y., Apanasovich, T., and Ruppert, D. (2012), Local asymptotics of P-splines, (pdf) . (reference paper for Fast Bivariate P-splines: the Sandwich Smoother)

Woodard, D., Love, T., Thurston, S., and Ruppert, D. (2013) Latent Factor Regression Models for Grouped Outcomes, Biometrics, 69, 785-794.

Woodard, D., Crainiceanu, C, and Ruppert, D. (2013) Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors, JCGS , 22, 777-800.

Soiaporn, K., Chernoff, D., Loredo, T., Ruppert, D., and Wasserman, I., (2013) Multilevel Bayesian Framework for Modeling the Production, Propagation and Detection of Ultra-High Energy Cosmic Rays, Annals of Applied Statistics, 7, 1249-1285.

Kauermann, G., Schellhase, C., and Ruppert, D. (2013) Flexible Copula Density Estimation with Penalized Hierarchical B-Splines, Scandinavian Journal of Statistics , 40, 685-705. (pdf) .

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Staicu, A.-M., Crainiceanu, C., Reich, D., and Ruppert, D. (2012) Modeling Functional Data With Spatially Heterogeneous Shape Characteristics, Biometrics , 68, 331-343.

Bliznyuk, N., Ruppert, D., and Shoemaker, C. (2012) Local Derivative-Free Approximation of Computationally Expensive Posterior Densities, JCGS, 21, 476-495.

Shaby, B., and Ruppert, D. (2012) Taper covariance: Bayesian estimation, asymptotics, and applications, JCGS, 21, 433-452.

Ruppert, D., Shoemaker, C., Wang, Y., Li, Y., and Bliznyuk, N. (2012) Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs, J. of Agricultural, Biological, and Agricultural Statistics, 17, 623-640.

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Wang, Xiao, Shen, Jinglai, and Ruppert, D. (2011) Local Asymptotics of P-Spline Smoothing, EJS, 4, 1-17. (pdf) .

Matteson, D., and Ruppert, D. (2011) Time-Series Models of Dynamic Volatility and Correlation, IEEE Signal Processing Magazine, 28, 72--82.

Bliznyuk, N., Ruppert, D., and Shoemaker, C. (2011) Bayesian Inference Using Efficient Interpolation of Computationally Expensive Densities with Variable Parameter Costs, JCGS, 20, 636--655.

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Sharef, E., Strawderman, R., and Ruppert, D. (2010), Bayesian Adaptive B-spline Estimation in Proportional Hazards Frailty Models, Electronic Journal of Statistics, 4, 606-642.

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Thurston, S., Ruppert, D., and Davidson, P. (2009) Bayesian models for multiple outcomes nested in domains, Biometrics , 65, 1078-1086.

Ruppert, D., Wand, M.P., and Carroll, R.J. (2009) Semiparametric regression during 2003-2007, Electronic Journal of Statistics, 3, 1193-1256.

Liang, H., Qin, Y., Zhang, X., and Ruppert, D. (2009) Empirical-likelihood-based inferences for generalized partially linear models, Scandinavian Journal of Statistics, 36, 433-443.

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Apanasovich, T., Ruppert, D., Lupton, J., Popovic, N., Turner, N., Chapkin, R., and Carroll, R. (2008). Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data, Biometrics , 64, 490-500.

Li, Yingxing, and Ruppert, D. (2008). On The Asymptotics Of Penalized Splines, Biometrika , 95, 415-436.

Bliznyuk, N., Ruppert, D., Shoemaker, C., Regis, R., Wild, S., and Mugunthan, P. (2008). Bayesian Calibration of Computationally Expensive Models Using Optimization and Radial Basis Function Approximation. JCGS, 17, 270-294.

Staudenmayer, J., Ruppert, D., and Buonaccorsi, J. (2008). Density estimation in the presence of heteroskedastic measurement error, JASA, 103, 726-736.

Madsen, L., Ruppert, D., and Altman, N.S. (2008). Regression with Spatially Misaligned Data, Environmetrics, 19, 453-467.

Liang, Hua, Thurston, S., Ruppert, D., Apanosovich, T., and Hauser, R. (2008) Additive partial linear models with measurement errors, Biometrika, 95, 667-678.

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Ruppert, D., Nettleton, D., and Hwang, J. T. Gene (2007). Exploring the Information in P-values for the Analysis and Planning of Multiple-Test Experiments, Biometrics , 63, 483-495.

Ruppert, D., (2007). Comments on "Model-assisted Estimation of Forest Resources with Generalized Additive Models," by J. D. Opsomer, F. J. Breidt, G. G. Moisen, and G. Kauermann, JASA, 102, 409-411.

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Briggs, W. M., and Ruppert, D. (2006) Assessing the skill of yes/no forecasts for Markov observations, Monthly Weather Review, 134, 2601-2611.

Carroll, R., and Ruppert, D. (2006). Comment on "Conditional Growth Charts" by Ying Wei and Xuming He, The Annals of Statistics, 34, 2098-2104.

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Crainiceanu, C., Ruppert, D. Claeskens, G., and Wand, M. (2003) Exact Likelihood ratio tests for penalized splines, Biometrika, 92, 91-103.

Crainiceanu, C., Ruppert, D., and Wand, M. (2005). Bayesian Analysis for Penalized Spline Regression Using Win BUGS, Journal of Statistical Software Volume 14, 2005, Issue 14

Ruppert, D., and Carroll, R. (2005). Comments on "Does the Effect of Micronutrient Supplementation on Neonatal Survival Vary with Respect to the Percentiles of the Birth Weight Distribution?'' by Francesca Dominici, Scott L. Zeger, Giovanni Parmigiani, Joanne Katz, and Parul Christian

Ruppert, D., (2005). Discussion of "Maximization by Parts in Likelihood Inference," by Song, Fan, and Kalbfleish, JASA, 100, 1161-1163

Briggs, W., Pocernich, M, and Ruppert, D. (2005), Incorporating misclassification error in skill assessment, Monthly Weather Review , 133 (11): 3382-3392 NOV 2005

Briggs, W., and Ruppert, D. (2005), Assessing the Skill of Yes/No Predictions, Biometrics, 61, 799-807.

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Ruppert, D. (2004). Review of "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Hastie, Tibshirani, and Friedman, JASA , 99, 567.

Crainiceanu, C. and Ruppert, D. (2004). Likelihood ratio tests in linear mixed models with one variance component, JRSS-B, 66, 165-185.

Staudenmayer, J., and Ruppert, D. (2004). Local Polynomial regression and SIMEX, JRSS-B, 66, 17-30.

Jarrow, R., Ruppert, D., and Yu, Yan. (2004). Estimating the term structure of corporate debt with a semiparametric penalized spline model, JASA, 99, 57-66.

Yu, Y., and Ruppert, D. (2004), Root-n Consistency of Penalized Spline Estimator for Partially Linear Single-Index Models under General Euclidean Space, Statistica Sinica, 14, 449-456.

Carroll, R., Ruppert, D., Crainiceanu, C., Tosteson, T., and Karagas, M. (2004), Nonlinear and Nonparametric Regression and Instrumental Variables, JASA, 99, 736--750.

Crainiceanu, C. and Ruppert, D. (2004). Restricted Likelihood Ratio Tests for Longitudinal Models, Statistica Sinica, 14, 713-729.

Crainiceanu, C. and Ruppert, D. (2004). Likelihood Ratio Tests for Goodness-of-Fit of a Nonlinear Regression Model. (pdf), Journal of Multivariate Analysis , 91(1), 35-42.

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Crainiceanu, C., Stedinger, J., Ruppert, D., and Behr, C. (2003). Modeling the U. S. National Distribution of Waterborne Pathogen Concentrations with Application to Cryptosporidium parvum Water Resources Research, 39, no. 9, 1235-1249.

Thurston, S. W., Spiegelman, D., and Ruppert, D. (2003), Equivalence of regression calibration methods in main study/external validation study designs, J. of Statistical Planning and Inference, 113, 527-539.

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Yu, Yan, and Ruppert, D. (2002). Penalized Spline Estimation for Partially Linear Single Index Models, JASA, 97, 1042-1054.

Ruppert, D., (2002). Discussion of "Spline adaptation in extended linear models" by Mark Hansen and Charles Kooperberg," Statistical Science, 17, 37-40.

Ruppert, D. (2002). Selecting the number of knots for penalized splines, JCGS , 11, 735-757.

Crainiceanu, C., Ruppert, D., Stedinger, J. R., and Behr, C. T. (2002). Improving MCMC Mixing for a GLMM Describing Pathogen Concentrations in Water Supplies, In Case Studies in Bayesian Statistics, VI, , Gatsonis, C., et al (editors), Lecture Notes in Statistics 167, Springer, pp. 207-222.

Berry, S., Carroll, R., and Ruppert, D. (2002). Bayesian Smoothing and Regression Splines for Measurement Error Problems, JASA, 97, 160--169.

Ruppert, D. (2002). Discussion of ``Inconsistency of resampling algorithms for high breakdown regression estimatiors and a new algorithm'' by Douglas Hawkins and David Olive, JASA, 97, 148--149.

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Ruppert, D. (2001). Review of ``Nonparametric regression and Spline Smoothing'' by Randall Eubank, JASA, 96, 1523--1524.

Parise, H., Ruppert, D., Ryan, L., and Wand, M. (2001). Incorporation of Historical Controls Using Semiparametric Mixed Models, Applied Statistics , 50, 31-42.

Ruppert, D. (2001). Transformations of data. (ps) International Encyclopedia of Social and Behavioral Sciencees .

Ruppert, D. (2001) Multivariate Transformations (pdf) Encyclopedia of Environmetrics

Coull, B., Ruppert, D., and Wand, M. (2001), Simple incorporation of interactions into additive models. Biometrics, 57, 539--545.

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Ruppert, D., Schruben, L, and Freimer, M. (2000). Meta-modeling of a cluster tool simulator, MASM2000 Proceedings. (pdf)

Ruppert, D., and Carroll, R.J. (2000). Spatially-adaptive penalties for spline fitting, Australian and New Zealand Journal of Statistics, 42, 205-223. Correction.

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Marron, J.S., Ruppert, D., Smith, E.K., and Conley, G. (1999), Motion Picture Analysis of Smoothing.

Opsomer, J., and Ruppert, D. (1999). A root-n consistent backfitting estimator for semiparametric additive modelling. JCGS, 8, 715--732. (Invited paper in the ``Best of JCGS Session'' at Interface '99)

Brumback, B., Ruppert, D., and Wand, M.P. (1999). Comment on "Variable selection and function estimation in additive nonparametric regression using a data-based prior" by Shively, Kohn, and Wood. JASA, 94, 794--797.

Carroll, R.J., Ruppert, D., and Stefanski, L.A. (1999), Comments on ``Regression Depth'' by Rousseeuw and Hulbert, JASA, 94, 410-411.

Carroll, R.J., Maca, J.D., and Ruppert, D. (1999). Nonparametric Estimation in the Presence of Measurement Error. Biometrika, 86, 541-554.

Opsomer, J, Ruppert, D., Wand, M.P., Holst, U., and Hossjer, O., (1999), Kriging with nonparametric variance function estimation, Biometrics, 55, 704-710.

Chen, V. C. P., Ruppert, D., and Shoemaker, C. A., (1999), Applying experimental design and regression splines to high-dimensional continuous-state stochastic dynamic programming, Operations Research, 47, 38-53.

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Ruppert's home page at ORIE

Last update: Oct 13, 2023