Previous tech reports are available here.
2019
Id | Authors | Title |
19-01 | K.A.L. Valeriano, V.H. Lachos & L.A. Matos | Likelihood Based Inference for Spatio-Temporal Data with Censored and Missing Responses |
19-02 | R.C. Olivari, A.M. Garay, V.H. Lachos & L.A. Matos | Autoregressive Mixed-Effects Models for Censored Data |
19-03 | Y-B. Wang, M-H. Chen, W. Shi, P. Lewis & L. Kuo | Inflated Density Ratio and Its Variation and Generalization for Computing Marginal Likelihoods |
19-04 | Y. Liu, G. Hu, L. Cao, X. Wang & M-H. Chen | Monte Carlo Methods for Computing Marginal Likelihoods with Applications to Item Response Theory Models |
19-05 | G. Hu, F. Huffer & M-H. Chen | New Development of Bayesian Variable Selection Criteria for Spatial Point Process with Applications |
19-06 | Y. Liu, L. Geng, X. Wang, D. Zhang & M-H. Chen | Subgroup Analysis from Bayesian Perspectives |
19-07 | J.L. Pancras, X. Wang & D.K. Dey | Investigating Nested Geographic Structure in Consumer Purchases: A Bayesian Dynamic Multi-scale Spatiotemporal Modeling Approach |
19-08 | V.G. Cancho, J. L. Bazan & D.K. Dey | A New Regression Model for Bounded Response with Application in the Study of Cure Rate of Colorectal Cancer |
19-09 | R. da Paz, J.L. Bazan, V.H. Lachos & D.K. Dey | A Finite Mixture Mixed Proportion Regression Model for Classification Problems in Longitudinal Voting Data |
19-10 | Z. Ma, Y. Xue & G. Hu | Geographically Weighted Regression Analysis for Spatial Economics Data: A Bayesian Recourse |
19-11 | H-C. Yang, L. Geng & G. Hu | Computationally Efficient Bayesian Estimation for Spatial Weibull Regression with Applications in China Earthquake Economic Loss |
19-12 | R. Visina, Y. Bar-Shalom, P. Willett & D.K. Dey | On-Demand Track-to-Track Fusion Using Local IMM Inside Information |
19-13 | J. Geng, W. Shi & G. Hu | Bayesian Nonparametric Nonhomogeneous Poisson Process with Applications |
19-14 | C.E. Galarza, L.A. Matos, D.K. Dey & V.H. Lachos | On Moments of Folded and Truncated Multivariate Extended Skew-Normal Distributions |
19-15 | T.B. Mattos, L.A. Matos & V.H. Lachos | A Semiparametric Mixed-Effects Model for Censored Longitudinal Data |
19-16 | F. Liu, J. Zhang, N. Shi & M-H. Chen | A Generalized Semi-Parametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing |
19-17 | L. Geng, Y. Xue & G. Hu | Subsampled Information Criterion for Bayesian Model Selection in Big Data Setting |
19-18 | J. Jiao, G. Hu & J. Yan | A Bayesian Joint Model for Spatial Point Processes with Application to Basketball Shot Chart |
19-19 | J. Lee, H. Wang & E.D. Schifano | Online Updating for Linear Measurement Error Model in Big Data Stream |
19-20 | J. Wu, M. de Castro & M-H. Chen | Bayesian Survival Analysis in the Presence of Monotone Likelihoods |
19-21 | C.F. Ferreira, V.H. Lachos & A.M. Garay | Inference and Diagnostics for Heteroscedastic Nonlinear Regression Models Under Skew Scale Mixtures of Normal Distributions |
19-22 | T. Ye, V.H. Lachos, X. Wang & D.K. Dey | Comparisons of Zero-Inflated Continuous Regression Models from a Bayesian Perspective |
19-23 | F.H.C. de Alencar, L.A. Matos & V.H. Lachos | Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data |
19-24 | Y. Xue, J. Yan & E.D. Schifano | Simultaneous Monitoring for Regression Coefficients and Baseline Hazard Profile in Cox Modeling of Time-to-Event Data |
19-25 | Z. Ma, Y. Xue & G. Hu | Bayesian Heterogeneity Pursuit Regression Models for Spatially Dependent Data |
19-26 | G. Hu, Y. Xue & F. Huffer | A Comparison of Bayesian Accelerated Failure Time Models with Spatially Varying Coefficients |
19-27 | G. Hu, M-H. Chen & N. Ravishanker | Bayesian Analysis of Spherically Parameterized Dynamic Multivariate Stochastic Volatility Models |
19-28 | L.L. Hernandez-Velasco, C.A. Abanto-Valle & D.K. Dey | Mixed Effects State-Space Models with Student-t errors |
19-29 | C.E. Galarza, L.A. Matos, D.K.Dey & V.H. Lachos | Efficient Computation of Moments of Folded and Doubly Truncated Multivariate Extended Skew-Normal Distributions |
19-30 | V.H. Lachos, J.L. Bazan & L.M. Castro | The skew-t Censored Regression Model: Parameter Estimation Using the EM Algorithm |
19-31 | F.H.C. de Alencar, C.E. Galarza, L.A. Matos & V.H. Lachos | Finite Mixture Modeling of Censored and Missing Data Using the Multivariate Skew-Normal Distribution |
19-32 | F. Zhang, M-H. Chen, X.J. Cong & Q. Chen | A Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks |
19-33 | Md. T. Sheikh, J.G. Ibrahim, J.A. Gelfond, W. Sun & M-H. Chen | Joint Modeling of Longitudinal and Survival Data in the Presence of Competing Risks with Applications to Prostate Cancer Data |
19-34 | H. Jeong & D.K. Dey | Application of Vine Copula for Multi-Line Insurance Reserving |
19-35 | G. Hu, J. Geng, Y. Xue & H. Sang | Bayesian Spatial Homogeneity Pursuit of Functional Data: an Application to the U.S. Income Distribution |
19-36 | P. Zhao, H-C. Yang, D.K. Dey & G. Hu | Bayesian Spatial Homogeneity Pursuit Regression for Count Value Data |