Ana-Maria Staicu

Battagliola, M. L., Sorensen, H., Tolver, A., & Staicu, A.-M. (2022). A bias-adjusted estimator in quantile regression for clustered data. ECONOMETRICS AND STATISTICS, 23, 165–186. https://doi.org/10.1016/j.ecosta.2021.07.003

Li, M., Wang, K., Maity, A., & Staicu, A.-M. (2022). Inference in functional linear quantile regression. JOURNAL OF MULTIVARIATE ANALYSIS, 190. https://doi.org/10.1016/j.jmva.2022.104985

Cui, C., Singh, S. P., Staicu, A.-M., & Reich, B. J. (2021, May 24). Bayesian variable selection for high-dimensional rank data. ENVIRONMETRICS, Vol. 5. https://doi.org/10.1002/env.2682

Xu, Z., Laber, E., Staicu, A.-M., & Lascelles, B. D. X. (2021). Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions. SCIENTIFIC REPORTS, 11(1). https://doi.org/10.1038/s41598-021-87304-w

Roy, A., Reich, B. J., Guinness, J., Shinohara, R. T., & Staicu, A.-M. (2021, June 16). Spatial Shrinkage Via the Product Independent Gaussian Process Prior. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 6. https://doi.org/10.1080/10618600.2021.1923512

Xu, Z., Laber, E. B., & Staicu, A. (2020). Hierarchical continuous time hidden Markov model, with application in zero-inflated accelerometer data. In Y. Zhao & D. G. Chen (Eds.), Statistical Modeling for Biomedical Research: Contemporary Topics and Voices in the Field (pp. 125–142). https://doi.org/10.1007/978-3-030-33416-1_7

Stallrich, J., Islam, M. N., Staicu, A.-M., Crouch, D., Pan, L., & Huang, H. (2020). OPTIMAL EMG PLACEMENT FOR A ROBOTIC PROSTHESIS CONTROLLER WITH SEQUENTIAL, ADAPTIVE FUNCTIONAL ESTIMATION (SAFE). ANNALS OF APPLIED STATISTICS, 14(3), 1164–1181. https://doi.org/10.1214/20-AOAS1324

Hazra, A., Reich, B. J., & Staicu, A.-M. (2019). A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes. ENVIRONMETRICS. https://doi.org/10.1002/env.2602

Singh, S. P., Staicu, A.-M., Dunn, R. R., Fierer, N., & Reich, B. J. (2019). A nonparametric spatial test to identify factors that shape a microbiome. The Annals of Applied Statistics, 13(4), 2341–2362. https://doi.org/10.1214/19-aoas1262

Chen, S. T., Xiao, L., & Staicu, A.-M. (2019). A smoothing-based goodness-of-fit test of covariance for functional data. BIOMETRICS, 75(2), 562–571. https://doi.org/10.1111/biom.13005

Hazra, A., Reich, B. J., Reich, D. S., Shinohara, R. T., & Staicu, A. M. (2019). A spatio-temporal model for longitudinal image-on-image regression. Statistics in Biosciences, 11(1), 22–46. https://doi.org/10.1007/s12561-017-9206-z

Park, S. Y., Li, C., Benavides, S. M. M., Heugten, E., & Staicu, A. M. (2019). Conditional Analysis for Mixed Covariates, with Application to Feed Intake of Lactating Sows. JOURNAL OF PROBABILITY AND STATISTICS, 2019. https://doi.org/10.1155/2019/3743762

Staicu, A. M., Islam, M. N., Dumitru, R., & Heugten, E. van. (2019). Longitudinal dynamic functional regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(1), 25–46. https://doi.org/10.1111/rssc.12376

Tekbudak, M. Y., Alfaro-Córdoba, M., Maity, A., & Staicu, A.-M. (2018). A comparison of testing methods in scalar-on-function regression. AStA Advances in Statistical Analysis, 103(3), 411–436. https://doi.org/10.1007/S10182-018-00337-X

King, M. C., Staicu, A.-M., Davis, J. M., Reich, B. J., & Eder, B. (2018). A functional data analysis of spatiotemporal trends and variation in fine particulate matter. Atmospheric Environment, 184, 233–243. https://doi.org/10.1016/j.atmosenv.2018.04.001

Park, S. Y., Xiao, L., Willbur, J. D., Staicu, A.-M., & Jumbe, N. L. (2018). A joint design for functional data with application to scheduling ultrasound scans. Computational Statistics & Data Analysis, 122, 101–114. https://doi.org/10.1016/j.csda.2018.01.009

Kim, J. S., Staicu, A.-M., MAITY, A. R. N. A. B., Carroll, R. J., & Ruppert, D. (2018). Additive function-on-function regression. Journal of Computational and Graphical Statistics, 27(1), 234–244. https://doi.org/10.1080/10618600.2017.1356730

Kim, J. S., Maity, A., & Staicu, A.-M. (2018). Additive nonlinear functional concurrent model. Statistics and Its Interface, 11(4), 669–685. https://doi.org/10.4310/sii.2018.v11.n4.a11

Reich, B. J., Guinness, J., Vandekar, S. N., Shinohara, R. T., & Staicu, A.-M. (2018). Fully Bayesian spectral methods for imaging data. BIOMETRICS, 74(2), 645–652. https://doi.org/10.1111/biom.12782

Laber, E. B., & Staicu, A.-M. (2018). Functional Feature Construction for Individualized Treatment Regimes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 113(523), 1219–1227. https://doi.org/10.1080/01621459.2017.1321545

Geden, M., Staicu, A.-M., & Feng, J. (2018). Reduced Target Facilitation and Increased Distractor Suppression During Mind Wandering. EXPERIMENTAL PSYCHOLOGY, 65(6), 345–352. https://doi.org/10.1027/1618-3169/a000417

Kang, J., Reich, B. J., & Staicu, A.-M. (2018). Scalar-on-image regression via the soft-thresholded Gaussian process. Biometrika, 105(1), 165–184. https://doi.org/10.1093/biomet/asx075

Geden, M., Staicu, A.-M., & Feng, J. (2018). The impacts of perceptual load and driving duration on mind wandering in driving. Transportation Research Part F: Traffic Psychology and Behaviour, 57, 75–83. https://doi.org/10.1016/J.TRF.2017.07.004

Zhang, G. Z., Roell, K. R., Truong, L., Tanguay, R. L., & Reif, D. M. (2017). A Data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades. Toxicology and Applied Pharmacology, 314, 109–117. https://doi.org/10.1016/j.taap.2016.11.010

Gertheiss, J., Goldsmith, J., & Staicu, A.-M. (2017). A note on modeling sparse exponential-family functional response curves. Computational Statistics & Data Analysis, 105, 46–52. https://doi.org/10.1016/j.csda.2016.07.010

Staicu, A. M., & Reid, N. (2017). Interview with Nancy Reid. International Statistical Review, 85(3), 381–403.

Park, S. Y., Staicu, A.-M., Xiao, L., & Crainiceanu, C. M. (2017). Simple fixed-effects inference for complex functional models. Biostatistics, 19(2), 137–152. https://doi.org/10.1093/biostatistics/kxx026

Gruen, M. E., Alfaro-Córdoba, M., Thomson, A. E., Worth, A. C., Staicu, A.-M., & Lascelles, B. D. X. (2017). The Use of Functional Data Analysis to Evaluate Activity in a Spontaneous Model of Degenerative Joint Disease Associated Pain in Cats. PLOS ONE, 12(1), e0169576. https://doi.org/10.1371/journal.pone.0169576

Pomann, G. M., Staicu, A.-M., Lobaton, E. J., Mejia, A. F., Dewey, B. E., Reich, D. S., … Shinohara, R. T. (2016). A Lag functional linear model for prediction of magnetization transfer ratio in multiple sclerosis lesions. Annals of Applied Statistics, 10(4), 2325–2348. https://doi.org/10.1214/16-aoas981

Pomann, G. M., Staicu, A.-M., & Ghosh, S. (2016). A two-sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis. Journal of the Royal Statistical Society. Series C, Applied Statistics, 65(3), 395–414. https://doi.org/10.1111/rssc.12130

Kong, D. H., Staicu, A.-M., & MAITY, A. R. N. A. B. (2016). Classical testing in functional linear models. Journal of Nonparametric Statistics, 28(4), 813–838. https://doi.org/10.1080/10485252.2016.1231806

Usset, J., Staicu, A.-M., & Maity, A. (2016). Interaction models for functional regression. Computational Statistics & Data Analysis, 94, 317–329. https://doi.org/10.1016/J.CSDA.2015.08.020

Wrobel, J., Park, S. Y., Staicu, A.-M., & Goldsmith, J. (2016). Interactive graphics for functional data analyses. Stat, 5(1), 108–118. https://doi.org/10.1002/sta4.109

Zhang, Y. C., Staicu, A.-M., & MAITY, A. R. N. A. B. (2016). Testing for additivity in non-parametric regression. Canadian Journal of Statistics, 44(4), 445–462. https://doi.org/10.1002/cjs.11295

Scheipl, F., Staicu, A.-M., & Greven, S. (2015). Functional additive mixed models. Journal of Computational and Graphical Statistics, 24(2), 477–501. https://doi.org/10.1080/10618600.2014.901914

Usset, J., Maity, A., Staicu, A.-M., & Schwartzman, A. (2015). Glacier Terminus Estimation from Landsat Image Intensity Profiles. Journal of Agricultural, Biological, and Environmental Statistics, 20(2), 279–298. https://doi.org/10.1007/S13253-015-0207-4

Zhao, N., Bell, D. A., MAITY, A. R. N. A. B., Staicu, A.-M., Joubert, B. R., London, S. J., & Wu, M. C. (2015). Global analysis of methylation profiles from high resolution CpG data. Genetic Epidemiology, 39(2), 53–64. https://doi.org/10.1002/gepi.21874

Li, M., Staicu, A.-M., & Bondell, H. D. (2015). Incorporating covariates in skewed functional data models. Biostatistics (Oxford, England), 16(3), 413–426. https://doi.org/10.1093/biostatistics/kxu055

Park, S. Y., & Staicu, A.-M. (2015). Longitudinal functional data analysis. Stat, 4(1), 212–226. https://doi.org/10.1002/STA4.89

Gertheiss, J., Maier, V., Hessel, E. F., & Staicu, A.-M. (2015). Marginal functional regression models for analyzing the feeding behavior of pigs. Journal of Agricultural Biological and Environmental Statistics, 20(3), 353–370. https://doi.org/10.1007/s13253-015-0212-7

Ivanescu, A. E., Staicu, A.-M., Scheipl, F., & Greven, S. (2015). Penalized function-on-function regression. Computational Statistics, 30(2), 539–568. https://doi.org/10.1007/s00180-014-0548-4

Pomann, G. M., Sweeney, E. M., Reich, D. S., Staicu, A.-M., & Shinohara, R. T. (2015). Scan-stratified case-control sampling for modeling blood-brain barrier integrity in multiple sclerosis. Statistics in Medicine, 34(20), 2872–2880. https://doi.org/10.1002/sim.6520

Staicu, A.-M., Lahiri, S. N., & Carroll, R. J. (2015). Significance tests for functional data with complex dependence structure. Journal of Statistical Planning and Inference, 156, 1–13. https://doi.org/10.1016/j.jspi.2014.08.006

Staicu, A.-M., & Lu, X. S. (2014). Analysis of AneuRisk65 data: Classification and curve registration. Electronic Journal of Statistics, 8, 1914–1919. https://doi.org/10.1214/14-ejs938c

McLean, M. W., Hooker, G., Staicu, A.-M., Scheipl, F., & Ruppert, D. (2014). Functional generalized additive models. Journal of Computational and Graphical Statistics, 23(1), 249–269. https://doi.org/10.1080/10618600.2012.729985

Staicu, A.-M., Li, Y. X., Crainiceanu, C. M., & Ruppert, D. (2014). Likelihood ratio tests for dependent data with applications to longitudinal and functional data analysis. Scandinavian Journal of Statistics: Theory and Applications, 41(4), 932–949. https://doi.org/10.1111/sjos.12075

Serban, N., Staicu, A.-M., & Carroll, R. J. (2013). Multilevel cross-dependent binary longitudinal data. Biometrics, 69(4), 903–913. https://doi.org/10.1111/biom.12083

Gertheiss, J., Maity, A., & Staicu, A.-M. (2013). Variable selection in generalized functional linear models. Stat, 2(1), 86–101. https://doi.org/10.1002/sta4.20

Crainiceanu, C. M., Staicu, A.-M., Ray, S., & Punjabi, N. (2012). Bootstrap-based inference on the difference in the means of two correlated functional processes. Statistics in Medicine, 31(26), 3223–3240. https://doi.org/10.1002/sim.5439

Jiang, H., & Serban, N. (2012). Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility. Technometrics, 54(2), 108–119. https://doi.org/10.1080/00401706.2012.657106

Crainiceanu, C. M., & Staicu, A.-M. (2012). Comment. Technometrics, 54(2), 120–122. https://doi.org/10.1080/00401706.2011.649821

Staicu, A.-M., Crainiceanu, C. M., Reich, D. S., & Ruppert, D. (2011). Modeling Functional Data with Spatially Heterogeneous Shape Characteristics. Biometrics, 68(2), 331–343. https://doi.org/10.1111/j.1541-0420.2011.01669.x

Staicu, A.-M., Crainiceanu, C. M., & Carroll, R. J. (2010). Fast methods for spatially correlated multilevel functional data. Biostatistics (Oxford, England), 11(2), 177–194. https://doi.org/10.1093/biostatistics/kxp058

Staicu, A.-M. (2010). On the equivalence of prospective and retrospective likelihood methods in case-control studies. Biometrika, 97(4), 990–996. https://doi.org/10.1093/biomet/asq054

Fraser, A. M., Fraser, D. A. S., & Staicu, A.-M. (2010). Second order ancillary: A differential view from continuity. Bernoulli, 16(4), 1208–1223. https://doi.org/10.3150/10-bej248

Staicu, A.-M., & Fraser, D. A. S. (2010). The second order ancillary is rotation based. Journal of Statistical Planning and Inference, 140(3), 831–836. https://doi.org/10.1016/j.jspi.2009.09.011

Crainiceanu, C. M., Staicu, A.-M., & Di, C. Z. (2009). Generalized multilevel functional regression. Journal of the American Statistical Association, 104(488), 1550–1561. https://doi.org/10.1198/jasa.2009.tm08564

Staicu, A.-M. (2009). Higher-order approximations for interval estimation in binomial settings. Journal of Statistical Planning and Inference, 139(10), 3393–3404. https://doi.org/10.1016/j.jspi.2009.03.021

Staicu, A.-M., & Reid, N. M. (2008). On probability matching priors. Canadian Journal of Statistics, 36(4), 613–622. https://doi.org/10.1002/cjs.5550360408