http://www.rssboard.org/rss-specification 720 https://escholarship.org/uc/ucla_biostatistics_research_report/rss Recent eScholarship items from Research Reports Fri, 15 May 2026 23:27:57 +0000 https://escholarship.org/uc/item/2c88m2h5 A Unifying Bayesian Approach for Sample Size Determination Using Design andAnalysis Priors https://escholarship.org/uc/item/2c88m2h5 Fri, 10 Dec 2021 00:00:00 +0000 Pan, Jane Banerjee, Sudipto https://escholarship.org/uc/item/8pm5v0f8 We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatistics by partitioning the spatial domain and modeling the regions in the partition using a sparsity-inducing directed acyclic graph (DAG). We extend the model over the DAG to a well-defined spatial process, which we call the meshed Gaussian process (MGP). A major contribution is the development of an MGPs on tessellated domains, accompanied by a Gibbs sampler for the efficient recovery of spatial random effects. The source code is available at github.com/mkln/meshgp.&nbsp; https://escholarship.org/uc/item/8pm5v0f8 Fri, 26 Feb 2021 00:00:00 +0000 Peruzzi, Michele Banerjee, Sudipto Finley, Andrew O https://escholarship.org/uc/item/88c7t942 Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatial patterns. Such maps rely upon spatial models for regionally aggregated data, where neighboring regions tend to exhibit similar outcomes than those farther apart. We contribute to the literature on multivariate disease mapping, which deals with measurements on multiple (two or more) diseases in each region. We aim to disentangle associations among the multiple diseases from spatial autocorrelation in each disease. We develop Multivariate Directed Acyclic Graphical Autoregression (MDAGAR) models to accommodate spatial and inter-disease dependence. The hierarchical construction imparts flexibility and richness, interpretability of spatial autocorrelation and inter-disease relationships, and computational ease, but depends upon the order in which the cancers are modeled. To obviate this, we demonstrate... https://escholarship.org/uc/item/88c7t942 Fri, 26 Feb 2021 00:00:00 +0000 Gao, Leiwen Datta, Abhirup Banerjee, Sudipto https://escholarship.org/uc/item/9dw7s0x3 Network modeling in biology: statistical methods for gene and brain networks. https://escholarship.org/uc/item/9dw7s0x3 Tue, 16 Feb 2021 00:00:00 +0000 Wang, Y. X. Rachel Li, Lexin Li, Jingyi Jessica https://escholarship.org/uc/item/2nt2p3dt Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species. https://escholarship.org/uc/item/2nt2p3dt Tue, 16 Feb 2021 00:00:00 +0000 Sun, Yidan Eden Zhou, Heather J. Li, Jingyi Jessica https://escholarship.org/uc/item/1wn7s0xn A bootstrap lasso + partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models. https://escholarship.org/uc/item/1wn7s0xn Tue, 16 Feb 2021 00:00:00 +0000 Liu, Hanzhong Xu, Xin Li, Jingyi Jessica https://escholarship.org/uc/item/16b7929k Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines. https://escholarship.org/uc/item/16b7929k Tue, 16 Feb 2021 00:00:00 +0000 Li, Jingyi Jessica Tong, Xin https://escholarship.org/uc/item/9hw6s7ks Pseudo-Likelihood Based Logistic Regression forEstimating COVID-19 Infection and Case FatalityRates by Gender, Race, and Age in California https://escholarship.org/uc/item/9hw6s7ks Fri, 5 Feb 2021 00:00:00 +0000 Xiong, Di Zhang, Lu Watson, Gregory L Sundin, Phillip Bufford, Teresa Zoller, Joseph A Shamshoian, John Suchard, Marc A Ramirez, Christina M https://escholarship.org/uc/item/58c1r34h Joint modeling of spatially-oriented dependent variables are commonplace in the environmentalsciences, where scientists seek to estimate the relationships among a set of environmental outcomesaccounting for dependence among these outcomes and the spatial dependence for each outcome. Suchmodeling is now sought for very large data sets where the variables have been measured at a very largenumber of locations. Bayesian inference, while attractive for accommodating uncertainties through theirhierarchical structures, can become computationally onerous for modeling massive spatial data sets becauseof their reliance on iterative estimation algorithms. This manuscript develops a conjugate Bayesianframework for analyzing multivariate spatial data using analytically tractable posterior distributions thatdo not require iterative algorithms. We discuss differences between modeling the multivariate responseitself as a spatial process and that of modeling a latent process. We illustrate the... https://escholarship.org/uc/item/58c1r34h Fri, 5 Feb 2021 00:00:00 +0000 Zhang, Lu Banerjee, Sudipto Finley, Andrew O https://escholarship.org/uc/item/216065sz Spatial process models popular in geostatistics often represent the observed data as the sum of a smoothunderlying process and white noise. The variation in the white noise is attributed to measurement error,or micro-scale variability, and is called the “nugget”. We formally establish results on the identifiabilityand consistency of the nugget in spatial models based upon the Gaussian process within the framework ofin-fill asymptotics, i.e. the sample size increases within a sampling domain that is bounded. Our workextends results in fixed domain asymptotics for spatial models without the nugget. More specifically, weestablish the identifiability of parameters in the Matérn covariogram and the consistency of their maximumlikelihood estimators in the presence of discontinuities due to the nugget. We also present simulationstudies to demonstrate the role of the identifiable quantities in spatial interpolation. https://escholarship.org/uc/item/216065sz Fri, 5 Feb 2021 00:00:00 +0000 Tang, Wenpin Zhang, Lu Banerjee, Sudipto https://escholarship.org/uc/item/0s71z8wg Multivariate spatially-oriented data sets are prevalent in the environmental and physical sciences.Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture anyunderlying spatial association for each variable and associations among the different dependent variables.Multivariate latent spatial process models have proved effective in driving statistical inference andrendering better predictive inference at arbitrary locations for the spatial process. High-dimensionalmultivariate spatial data, which is the theme of this article, refers to data sets where the number of spatiallocations and the number of spatially dependent variables is very large. The field has witnessed substantialdevelopments in scalable models for univariate spatial processes, but such methods for multivariate spatialprocesses, especially when the number of outcomes is moderately large, are limited in comparison. Here,we extend scalable modeling strategies for a single... https://escholarship.org/uc/item/0s71z8wg Fri, 5 Feb 2021 00:00:00 +0000 Zhang, Lu Banerjee, Sudipto https://escholarship.org/uc/item/978454rc Spatial process models for analyzing geostatistical data entail computations that become prohibitive asthe number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzinglarge spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesianparadigm. We partition the data into subsets, analyze each subset using a Bayesian spatial process model,and then obtain approximate posterior inference for the entire dataset by combining the individual posteriordistributions from each subset. Importantly, as often desired in spatial analysis, we offer full posteriorpredictive inference at arbitrary locations for the outcome as well as the residual spatial surface afteraccounting for spatially oriented predictors. We call this approach “spatial meta-kriging” (SMK). We do notneed to store the entire data in one processor, and this leads to superior scalability. We demonstrate SMKwith various spatial regression models including... https://escholarship.org/uc/item/978454rc Wed, 7 Nov 2018 00:00:00 +0000 Guhaniyogi, Rajarshi Banerjee, Sudipto https://escholarship.org/uc/item/8s28722k Environmental health exposures to airborne chemicals often originate fromchemical mixtures. Environmental health professionals may be interested inassessing exposure to one or more of the chemicals in these mixtures, but often,exposure measurement data are not available, either because measurementswere not collected/assessed for all exposure scenarios of interest or because someof themeasurementswere below the analytical methods' limits of detection (i.e.,censored). In some cases, based on chemical laws, two or more componentsmay have linear relationships with one another, whether in single or multiplemixtures. Although bivariate analyses can be used if the correlation is high, correlationsare often low. To serve this need, this paper develops a multivariateframework for assessing exposure using relationships of the chemicals presentin these mixtures. This framework accounts for censored measurements in allchemicals, allowing us to develop unbiased exposure estimates.We assessed... https://escholarship.org/uc/item/8s28722k Wed, 7 Nov 2018 00:00:00 +0000 Groth, Caroline P Banerjee, Sudipto Ramachandran, Gurumurthy Stenzel, Mark R Stewart, Patricia A https://escholarship.org/uc/item/8nx6t5xp Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fitusing the restricted likelihood or the closely related Bayesian analysis. This article addresses two problems. First, we proposetools for understanding how data determine estimates in these models, using a spectral basis approximation to the GP underwhich the restricted likelihood is formally identical to the likelihood for a gamma-errors GLM with identity link. Second,to examine the data’s support for a covariate and to understand how adding that covariate moves variation in the outcomey out of the GP and error parts of the fit, we apply a linear-model diagnostic, the added variable plot (AVP), both to theoriginal observations and to projections of the data onto the spectral basis functions. The spectral- and observation-domainAVPs estimate the same coefficient for a covariate but emphasize low- and high-frequency data features respectively and thushighlight the covariate’s... https://escholarship.org/uc/item/8nx6t5xp Wed, 7 Nov 2018 00:00:00 +0000 Bose, Maitreyee Hodges, James S Banerjee, Sudipto https://escholarship.org/uc/item/820036bc Bayesian State Space Modeling of PhysicalProcesses in Industrial Hygiene https://escholarship.org/uc/item/820036bc Wed, 7 Nov 2018 00:00:00 +0000 Abdalla, Nada Banerjee, Sudipto Ramachandran, Gurumurthy Arnold, Susan https://escholarship.org/uc/item/6z09s1xr Species distribution models usually attempt to explain presence-absenceor abundance of a species at a site in terms of the environmental features (socalledabiotic features) present at the site. Historically, such models have consideredspecies individually. However, it is well-established that species interactto influence presence-absence and abundance (envisioned as biotic factors). Asa result, there has been substantial recent interest in joint species distributionmodels with various types of response, e.g., presence-absence, continuous andordinal data. Such models incorporate dependence between species response asa surrogate for interaction.The challenge we address here is how to accommodate such modeling in thecontext of a large number of species (e.g., order 102) across sites numbering on theorder of 102 or 103 when, in practice, only a few species are found at any observedsite. Again, there is some recent literature to address this; we adopt a dimensionreduction approach.... https://escholarship.org/uc/item/6z09s1xr Wed, 7 Nov 2018 00:00:00 +0000 Shirota, Shinichiro Gelfand, Alan E Banerjee, Sudipto https://escholarship.org/uc/item/6mr2986t With continued advances in Geographic Information Systems and related computationaltechnologies, statisticians are often required to analyze very large spatialdatasets. This has generated substantial interest over the last decade, already toovast to be summarized here, in scalable methodologies for analyzing large spatialdatasets. Scalable spatial process models have been found especially attractive dueto their richness and flexibility and, particularly so in the Bayesian paradigm, due totheir presence in hierarchical model settings. However, the vast majority of researcharticles present in this domain have been geared toward innovative theory or morecomplex model development.Very limited attention has been accorded to approachesfor easily implementable scalable hierarchical models for the practicing scientist orspatial analyst. This article devises massively scalable Bayesian approaches that canrapidly deliver inference on spatial process that are practically indistinguishable... https://escholarship.org/uc/item/6mr2986t Wed, 7 Nov 2018 00:00:00 +0000 Zhang, Lu Datta, Abhirup Banerjee, Sudipto https://escholarship.org/uc/item/4s84j9g5 This article presents a simple and easily implementableBayesian approach to model and quantify uncertainty insmall descriptive social networks. While statistical methodsfor analyzing networks have seen burgeoning activity overthe last decade or so, ranging from social sciences to genetics,such methods usually involve sophisticated stochasticmodels whose estimation requires substantial structure andinformation in the networks. At the other end of the analyticspectrum, there are purely descriptive methods based uponquantities and axioms in computational graph theory. In socialnetworks, popular descriptive measures include, but arenot limited to, the so called Krackhardt’s axioms. Anotherapproach, recently gaining attention, is the use of PageRankalgorithms. While these descriptive approaches provide insightinto networks with limited information, including smallnetworks, there is, as yet, little research detailing a statisticalapproach for small networks. This article aims to contributeat... https://escholarship.org/uc/item/4s84j9g5 Wed, 7 Nov 2018 00:00:00 +0000 Nemmers, Thomas Narayan, Anjana Banerjee, Sudipto https://escholarship.org/uc/item/3z75f3dc Statistical interpolation of chemical concentrations at new locations is an important step in assessinga worker’s exposure level. When measurements are available from coastlines, as is the case incoastal clean-up operations in oil spills, one may need a mechanism to carry out spatial interpolationat new locations along the coast. In this article, we present a simple model for analyzing spatial datathat is observed over a coastline. We demonstrate four different models using two different representationsof the coast using curves. The four models were demonstrated on simulated data andone of them was also demonstrated on a dataset from the GuLF STUDY (Gulf Long-term Follow-upStudy). Our contribution here is to offer practicing hygienists and exposure assessors with a simpleand easy method to implement Bayesian hierarchical models for analyzing and interpolating coastalchemical concentrations. https://escholarship.org/uc/item/3z75f3dc Wed, 7 Nov 2018 00:00:00 +0000 Abdalla, Nada Banerjee, Sudipto Ramachandran, Gurumurthy Stenzel, Mark Stewart, Patricia A https://escholarship.org/uc/item/1198466s This work extends earlier work on spatial meta kriging for the analysis of large multivariatespatial datasets as commonly encountered in environmental and climate sciences. Spatialmeta-kriging partitions the data into subsets, analyzes each subset using a Bayesianspatial process model and then obtains approximate posterior inference for the entiredataset by optimally combining the individual posterior distributions from each subset.Importantly, as is often desired in spatial analysis, spatial meta kriging offers posteriorpredictive inference at arbitrary locations for the outcome as well as the residual spatialsurface after accounting for spatially oriented predictors. Our current work explores spatialmeta kriging idea to enhance scalability of multivariate spatial Gaussian process modelthat uses linear model co-regionalization (LMC) to account for the correlation betweenmultiple components. The approach is simple, intuitive and scales multivariate spatialprocess models to big... https://escholarship.org/uc/item/1198466s Wed, 7 Nov 2018 00:00:00 +0000 Guhaniyogi, Rajarshi Banerjee, Sudipto https://escholarship.org/uc/item/0gs54401 Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge https://escholarship.org/uc/item/0gs54401 Wed, 7 Nov 2018 00:00:00 +0000 Kawaguchi, Eric S Suchard, Marc A Liu, Zhenqiu Li, Gang https://escholarship.org/uc/item/0281896n With the growing capabilities of Geographic Information Systems(GIS) and user-friendly software, statisticians today routinely encounter geographicallyreferenced data containing observations from a large number of spatial locationsand time points. Over the last decade, hierarchical spatiotemporal processmodels have become widely deployed statistical tools for researchers to better understandthe complex nature of spatial and temporal variability. However, fittinghierarchical spatiotemporal models often involves expensive matrix computationswith complexity increasing in cubic order for the number of spatial locations andtemporal points. This renders such models unfeasible for large data sets. Thisarticle offers a focused review of two methods for constructing well-defined highlyscalable spatiotemporal stochastic processes. Both these processes can be used as“priors” for spatiotemporal random fields. The first approach constructs a lowrankprocess operating on a lower-dimensional... https://escholarship.org/uc/item/0281896n Wed, 7 Nov 2018 00:00:00 +0000 Banerjee, Sudipto https://escholarship.org/uc/item/9t20g0pr <p>This article considers sample size determination for jointly testing a cause-specific hazard and the any-cause hazard for competing risks data. The cause-specific hazard and the any-cause hazard jointly characterize important study endpoints such as the disease-specific survival and overall survival, which are commonly used as co-primary endpoints in clinical trials. Specifically, we derive sample size calculation methods for two-group comparisons based on an asymptotic chi-square joint test and a maximum joint test of the aforementioned quantities, taking into account of</p><p>censoring due to lost to follow-up as well as staggered entry and administrative censoring.</p><p>Our simulations demonstrate that the proposed methods can produce substantial sample size</p><p>savings over the classical Bonferroni adjustment method and generally have satisfactory finite sample performance.</p><p>We illustrate the application of the proposed methods using the 4-D (Die Deutsche Diabetes... https://escholarship.org/uc/item/9t20g0pr Fri, 12 May 2017 00:00:00 +0000 Yang, Qing Fung, Wing K Li, Gang https://escholarship.org/uc/item/4w60b16n Inferring Brain Signals Synchronicity from a Sample of EEG Readings https://escholarship.org/uc/item/4w60b16n Thu, 29 Sep 2016 00:00:00 +0000 Telesca, Donatello https://escholarship.org/uc/item/9n90r7hq Prediction Summary Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data https://escholarship.org/uc/item/9n90r7hq Thu, 1 Sep 2016 00:00:00 +0000 Li, Gang Wang, Xiaoyan https://escholarship.org/uc/item/9vw0p4pn Minimax optimal designs via particle swarm optimization methods https://escholarship.org/uc/item/9vw0p4pn Wed, 22 Jun 2016 00:00:00 +0000 Chen, Ray-Bing Chang, Shin-Perng Wang, Weichung Tung, Heng-Chih Wong, Weng Kee https://escholarship.org/uc/item/6v89z9ks A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models https://escholarship.org/uc/item/6v89z9ks Wed, 22 Jun 2016 00:00:00 +0000 Wong, Weng Kee Chen, Ray-Bing Huang, Chien-Chih Wang, Weichung Talkachova, Alena https://escholarship.org/uc/item/63q0c96r Cluster-Randomized Trial to Increase Hepatitis B Testing among Koreans in Los Angeles https://escholarship.org/uc/item/63q0c96r Wed, 22 Jun 2016 00:00:00 +0000 Bastani, R. Glenn, B. A Maxwell, A. E Jo, A. M Herrmann, A. K Crespi, C. M Wong, W. K Chang, L. C Stewart, S. L Nguyen, T. T Chen, M. S Taylor, V. M https://escholarship.org/uc/item/5gk1d91d Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach https://escholarship.org/uc/item/5gk1d91d Wed, 22 Jun 2016 00:00:00 +0000 Duarte, Belmiro P. M Wong, Weng Kee https://escholarship.org/uc/item/46z4c8hd Data-driven desirability function to measure patientsâ�� disease progression in a longitudinal study https://escholarship.org/uc/item/46z4c8hd Wed, 22 Jun 2016 00:00:00 +0000 Chen, Hsiu-Wen Wong, Weng Kee Xu, Hongquan https://escholarship.org/uc/item/3qh2c1jp Optimizing Two-Level Supersaturated Designs Using Swarm Intelligence Techniques https://escholarship.org/uc/item/3qh2c1jp Wed, 22 Jun 2016 00:00:00 +0000 Phoa, Frederick Kin Hing Chen, Ray-Bing Wang, Weichung Wong, Weng Kee https://escholarship.org/uc/item/1j63v3q0 Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels https://escholarship.org/uc/item/1j63v3q0 Wed, 22 Jun 2016 00:00:00 +0000 Hyun, Seung Won Wong, Weng Kee https://escholarship.org/uc/item/0bf3t830 A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination https://escholarship.org/uc/item/0bf3t830 Wed, 22 Jun 2016 00:00:00 +0000 Duarte, Belmiro P.M. Wong, Weng Kee Atkinson, Anthony C https://escholarship.org/uc/item/06x6x7cb RARtool: A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes https://escholarship.org/uc/item/06x6x7cb Wed, 22 Jun 2016 00:00:00 +0000 Ryeznik, Yevgen Sverdlov, Oleksandr Wong, Weng Kee https://escholarship.org/uc/item/9sg1r2xj Cluster-Randomized Trial to Increase Hepatitis B Testing among Koreans in Los Angeles https://escholarship.org/uc/item/9sg1r2xj Tue, 21 Jun 2016 00:00:00 +0000 Bastani, Roshan Glenn, Beth A Maxwell, Annette E al, et https://escholarship.org/uc/item/59q7w1k3 Inference for reclassificaiton statistics under nested and non-nested models for biomarker evaluation https://escholarship.org/uc/item/59q7w1k3 Tue, 21 Jun 2016 00:00:00 +0000 Shao, Fanh Li, Jialiang Wong, Weng Kee Pencina, Michael https://escholarship.org/uc/item/41s0g6qn A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models&nbsp; https://escholarship.org/uc/item/41s0g6qn Tue, 21 Jun 2016 00:00:00 +0000 Wong, Weng Kee Chen, Ray-Bing Huang, Chien-Chih Wang, Weichung https://escholarship.org/uc/item/3p82z70s Detection of carotid artery calcification on the panoramic images of post-menopausal females is significantly associated with severe abdominal aortic calcification: a risk indicator of future adverse vascular events https://escholarship.org/uc/item/3p82z70s Tue, 21 Jun 2016 00:00:00 +0000 Friedlander, A H El Saden, S M Hazboun, R C Chang, T I Wong, W K Garrett, N R https://escholarship.org/uc/item/236735z9 Sun Protection Practices and Sun Exposure among Children with a Parental History of Melanoma https://escholarship.org/uc/item/236735z9 Tue, 21 Jun 2016 00:00:00 +0000 Glenn, B. A Lin, T. Chang, L. C Okada, A. Wong, W. K Glanz, K. Bastani, R. https://escholarship.org/uc/item/0z02f0jh Efficient and Ethical Response=Adaptive Randomizaiton Designs for Multi-Arm Clinical Trials With Weibull Time-to-Event Outcomes https://escholarship.org/uc/item/0z02f0jh Tue, 21 Jun 2016 00:00:00 +0000 Sverdlov, Oleksandr Ryeznik, Yevgen Wong, Weng Kee https://escholarship.org/uc/item/55x673td Non-Separable Dynamic Nearest-Neighbor Gaussian Process Models for Large Spatio-Temporal Data With An Application to Particulate Matter Analysis https://escholarship.org/uc/item/55x673td Thu, 24 Mar 2016 00:00:00 +0000 Datta, Abhirup Banerjee, Sudipto Finley, Andrew O Hamm, Nicholas A.S. Schaap, Martijn https://escholarship.org/uc/item/55h4h0w7 <p>In this paper we introduce fuzzy forests, a novel machine learning algorithm for ranking</p><p>the importance of features in high-dimensional classication and regression problems.</p><p>Fuzzy forests is specically designed to provide relatively unbiased rankings of variable</p><p>importance in the presence of highly correlated features, especially when p &gt;&gt; n . We</p><p>introduce our implementation of fuzzy forests in the R  package, fuzzyforest . Fuzzy forests</p><p>works by taking advantage of the network structure between features. First, the features</p><p>are partitioned into separate modules such that the correlation within modules is high</p><p>and the correlation between modules is low. The package fuzzyforest  allows for easy use</p><p>of Weighted Gene Coexpression Network Analysis (WGCNA) to form modules of features</p><p>such that the modules are roughly uncorrelated. Then recursive feature elimination random</p><p>forests (RFE-RFs) are used on each module,... https://escholarship.org/uc/item/55h4h0w7 Thu, 24 Mar 2016 00:00:00 +0000 Conn, Daniel Ngun, Tuck Li, Gang Ramirez, Christina https://escholarship.org/uc/item/9k3819jn Bivariate Left-Censored Bayesian Model for Predicting Exposure: Preliminary Analysis of Worker Exposure during the <em>Deepwater Horizon </em>Oil Spill https://escholarship.org/uc/item/9k3819jn Mon, 23 Nov 2015 00:00:00 +0000 Groth, Caroline Banerjee, Sudipto https://escholarship.org/uc/item/93j573sb Joint Inference for Competing Risks Data https://escholarship.org/uc/item/93j573sb Fri, 4 Sep 2015 00:00:00 +0000 Li, Gang Yang, Qing https://escholarship.org/uc/item/8781x807 This manuscript reviews the use of Bayesian hierarchical curve registration in Biostatistics and Bioinformatics.Several models allowing for unit-specific random time scales are discussed and applied to longitudinal dataarising in biomedicine, pharmacokinetics and time-course genomics. We consider representations of random functionals based on P-spline priors. Under this framework, straightforward posterior simulation strategies are outlined for inference.Beyond curve registration, we discuss jointregression modeling of both random effects and population level functional quantities. Finally, the use of mixture priors is discussed in the setting of differential expression analysis. https://escholarship.org/uc/item/8781x807 Tue, 9 Jun 2015 00:00:00 +0000 Telesca, Donatello https://escholarship.org/uc/item/8848228c Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets https://escholarship.org/uc/item/8848228c Wed, 11 Feb 2015 00:00:00 +0000 Datta, Abhirup Banerjee, Sudipto Finley, Andrew O. Gelfand, Alan E. https://escholarship.org/uc/item/397675wn On Tensor-based Multidimensional Models for Disease Mapping https://escholarship.org/uc/item/397675wn Wed, 11 Feb 2015 00:00:00 +0000 Martinez-Beneito, Miguel A. Botella-Rocamora, Paloma Banerjee, Sudipto https://escholarship.org/uc/item/4rj1t11c Heteroscedastic CAR models for areally referenced temporal processes for analyzing California asthma hospitalization data https://escholarship.org/uc/item/4rj1t11c Wed, 28 Jan 2015 00:00:00 +0000 Quick, Harrison Carlin, Bradley P. Banerjee, Sudipto https://escholarship.org/uc/item/3dw8x1xx Web-based Supplementary Materials for Bayesian Modeling and Analysis for Gradients in Spatiotemporal Processes by Quick et al. https://escholarship.org/uc/item/3dw8x1xx Wed, 28 Jan 2015 00:00:00 +0000 Quick, Harrison Banerjee, Sudipto Carlin, Bradley P. https://escholarship.org/uc/item/9kc7q9pk A Two-step Estimation Approach for Logistic Varying Coefficient Modeling of Longitudinal Data https://escholarship.org/uc/item/9kc7q9pk Tue, 27 Jan 2015 00:00:00 +0000 Li, Gang Senturk, Damla Dong, Jun Estes, Jason P. https://escholarship.org/uc/item/4j94x6cx Identifying Longitudinal Trends within EEGExperiments https://escholarship.org/uc/item/4j94x6cx Tue, 27 Jan 2015 00:00:00 +0000 Hasenstab, Kyle Sugar, Catherine A. Telesca, Donatello McEvoy, Kevin Jeste, Shafali Senturk, Damla https://escholarship.org/uc/item/4b76m8mn Bayesian Modeling and Analysis for Gradients in Spatiotemporal Processes https://escholarship.org/uc/item/4b76m8mn Tue, 27 Jan 2015 00:00:00 +0000 Quick, Harrison Banerjee, Sudipto Carlin, Bradley P. https://escholarship.org/uc/item/1zz0p2d2 Time-Varying Effect Modeling with Longitudinal Data Truncated by Death: &nbsp;Conditional Models, Interpretations and Inference https://escholarship.org/uc/item/1zz0p2d2 Tue, 27 Jan 2015 00:00:00 +0000 Estes, Jason P. Nguyen, Danh V. Dalrymple, Lorien S. Mu, Yi Senturk, Damla https://escholarship.org/uc/item/61n8h2np Non-Local Priors for High Dimensional Estimation https://escholarship.org/uc/item/61n8h2np Wed, 21 Jan 2015 00:00:00 +0000 Rossell, David Telesca, Donatello https://escholarship.org/uc/item/5cr096pt Joint Clustering and Registration of Functional Data https://escholarship.org/uc/item/5cr096pt Wed, 21 Jan 2015 00:00:00 +0000 Zhang, Yafeng Telesca, Donatello