Tapabrata (Taps) Maiti
Department of Statistics and Probability
Michigan State University
Selected Statistics Publications
Dass, S.C. Lim, C and Maiti, T. (2014). “A Generalized Mixed Model Framework for Assessing Fingerprint Individuality in presence of Varying Image Quality". Annals of Applied Statistics, 8, 1314-1340.
Maiti, T., Ren, H. and Sinha, S. (2014). “Prediction Error of Small Area Predictors Shrinking both Mean and Variances". Scandinavian Journal of Statistics, 41, 775-790
Dass, S.C. Lim, C and Maiti, T. (2011). “Default Bayesian Analysis for Hierarchical Spatial Multivariate Generalized Linear Mixed Models''. Statistica Sinica, 22, 231-248.
Hall, P., and Maiti, T. (2012). “Choosing Trajectory and Data Type when Classifying Functional Data". Biometrika, 99, 799-811.
Maiti, T., and Pradhan, V. (2009). “Bias reduction and a solution of separation for logistic regression with missing covariates''. Biometrics, 65, 1262-1269.
Hall, P., and Maiti, T. (2008). “Nonparametric inference for clustered binary and count data when only summary information is available". Journal of Royal Statistical Society, Series B, 70, 725-739.
Hall, P., and Maiti, T (2006. “Nonparametric estimation of mean squared prediction error in nested-error regression models''. Annals of Statistics, 34, 1733-1750.
Ghosh, M., Maiti, T., Kim, D., Chakraborty, S. and Tewari, A. (2004). “Bayesian neural network modeling in prostate cancer detection''. Journal of the American Statistical Association, 99, 601-608.
Selected Collaborative Publications
Xu, Y., Choi, J., Dass, S., and Maiti, T. (2013). “Efficient Bayesian Spatial Prediction with Mobile Sensor Networks Using Gaussian Markov Random Fields". Automatica, 49, pp 3520-3530.
Xu, Y., Choi, J., Dass, S. and Maiti, T. (2011). “Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks". EEE Transactions on Automatic Control, 99, pp.1, 0 \ doi: 10.1109/TAC.2011.2179430.
Hu, P., and Maiti, T. (2011). “A Nonparametric Mean-Variance Smoothing Method to Assess Arabidopsis Cold Stress Transcriptional Regulator CBF2 Overexpression Microarray Data", PLoS ONE, 6, e19640. \doi:10.1371/journal.pone.0019640.
Martin, A.M., Gutierrez-Pabello, J., Igor, K., Maiti, T. and Quanckenbush, J. (2009). “An improved empirical Bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). BMC Bioinformatics, 10:409.