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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Mdc-R-Code 2016 Update, Joseph M. Hilbe
Mdc-R-Code 2016 Update, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data: R code for download and use. Most recent update
Addition To Pglr Chap 6, Joseph M. Hilbe
Addition To Pglr Chap 6, Joseph M. Hilbe
Joseph M Hilbe
Addition to Chapter 6 in Practical Guide to Logistic Regression. Added section on Bayesian logistic regression using Stata.
Testing Homogeneity In Semiparametric Mixture Case-Control Models, C Z. Di, G Kc Chan, C Zheng, Ky Liang
Testing Homogeneity In Semiparametric Mixture Case-Control Models, C Z. Di, G Kc Chan, C Zheng, Ky Liang
Chongzhi Di
Recently, Qin and Liang (Biometrics, 2011) considered a semiparametric mixture case-control model and proposed a score test for homogeneity. The mixture model is semiparametric in the sense that the density ratio of two distributions is assumed to be of exponential form, while the baseline density is unspecified. In a family of parametric admixture models, Di and Liang (Biometrics, 2011) showed that the likelihood ratio test statistics, which is equivalent to a supremum statistics, could improve power over score tests. We generalize the likelihood ratio or supremum statistics to the semiparametric mixture model and demonstrate the power gain over the score …
Hilbe-Pglr-Errata-And-Comments, Joseph M. Hilbe
Hilbe-Pglr-Errata-And-Comments, Joseph M. Hilbe
Joseph M Hilbe
Errata and Comments for Practical Guide to Logistic Regression
Procesy Cieplne I Aparaty (Lab), Wojciech M. Budzianowski
Online Variational Bayes Inference For High-Dimensional Correlated Data, Sylvie T. Kabisa, Jeffrey S. Morris, David Dunson
Online Variational Bayes Inference For High-Dimensional Correlated Data, Sylvie T. Kabisa, Jeffrey S. Morris, David Dunson
Jeffrey S. Morris
High-dimensional data with hundreds of thousands of observations are becoming commonplace in many disciplines. The analysis of such data poses many computational challenges, especially when the observations are correlated over time and/or across space. In this paper we propose exible hierarchical regression models for analyzing such data that accommodate serial and/or spatial correlation. We address the computational challenges involved in fitting these models by adopting an approximate inference framework. We develop an online variational Bayes algorithm that works by incrementally reading the data into memory one portion at a time. The performance of the method is assessed through simulation studies. …
Functional Car Models For Spatially Correlated Functional Datasets, Lin Zhang, Veerabhadran Baladandayuthapani, Hongxiao Zhu, Keith A. Baggerly, Tadeusz Majewski, Bogdan Czerniak, Jeffrey S. Morris
Functional Car Models For Spatially Correlated Functional Datasets, Lin Zhang, Veerabhadran Baladandayuthapani, Hongxiao Zhu, Keith A. Baggerly, Tadeusz Majewski, Bogdan Czerniak, Jeffrey S. Morris
Jeffrey S. Morris
We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on …
Hypothesis Testing For Functional Linear Models, Y-R Su, Cz Di, L Hsu
Hypothesis Testing For Functional Linear Models, Y-R Su, Cz Di, L Hsu
Chongzhi Di
Functional data arise frequently in many biomedical studies, where it is often of interest to investigate the dynamic association between functional predictors and a scalar response variable. While functional linear models (FLM) are widely used to address these questions, hypothesis testing for the functional association in the FLM framework remains challenging. A popular approach to testing the functional effects is through dimension reduction by functional principal component (PC) analysis. However, its power performance depends on the choice of the number of PCs, and is not systematically studied. In this paper, we first investigate the power performance of the Wald-type test …
Inżynieria Chemiczna Lab., Wojciech M. Budzianowski