Research Reports
Parent: Department of Biostatistics
eScholarship stats: History by Item for August through November, 2024
| Item | Title | Total requests | 2024-11 | 2024-10 | 2024-09 | 2024-08 |
|---|---|---|---|---|---|---|
| 6v89z9ks | A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models | 84 | 13 | 19 | 17 | 35 |
| 2nt2p3dt | Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species. | 71 | 13 | 23 | 15 | 20 |
| 2c88m2h5 | A Unifying Bayesian Approach for Sample Size Determination Using Design andAnalysis Priors | 69 | 10 | 38 | 6 | 15 |
| 55h4h0w7 | Fuzzy Forests: Extending Random Forests for Correlated, High-Dimensional Data | 49 | 12 | 11 | 11 | 15 |
| 8848228c | Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets | 46 | 6 | 10 | 3 | 27 |
| 8781x807 | Bayesian Analysis of Curves Shape Variation through Registration and Regression | 43 | 5 | 5 | 33 | |
| 58c1r34h | High-dimensional MultivariateGeostatistics: A Conjugate BayesianMatrix-Normal Approach | 42 | 5 | 6 | 2 | 29 |
| 0s71z8wg | Spatial Factor Modeling: A BayesianMatrix-Normal Approach for Misaligned Data | 36 | 7 | 3 | 26 | |
| 06x6x7cb | RARtool: A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes | 31 | 9 | 7 | 15 | |
| 1198466s | Multivariate spatial meta kriging | 31 | 3 | 6 | 5 | 17 |
| 88c7t942 | Multivariate Directed Acyclic Graph Auto-Regressive (MDAGAR) models for spatial diseases mapping | 31 | 3 | 9 | 1 | 18 |
| 9dw7s0x3 | Network modeling in biology: statistical methods for gene and brain networks. | 31 | 9 | 10 | 4 | 8 |
| 9vw0p4pn | Minimax optimal designs via particle swarm optimization methods | 31 | 6 | 7 | 3 | 15 |
| 1zz0p2d2 | Time-Varying Effect Modeling with Longitudinal Data Truncated by Death: Conditional Models, Interpretations and Inference | 30 | 3 | 6 | 3 | 18 |
| 0bf3t830 | A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination | 29 | 6 | 3 | 20 | |
| 3qh2c1jp | Optimizing Two-Level Supersaturated Designs Using Swarm Intelligence Techniques | 29 | 5 | 7 | 17 | |
| 8pm5v0f8 | Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains | 28 | 4 | 12 | 3 | 9 |
| 0gs54401 | Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge | 26 | 2 | 9 | 4 | 11 |
| 3dw8x1xx | Web-based Supplementary Materials for Bayesian Modeling and Analysis for Gradients in Spatiotemporal Processes by Quick et al. | 26 | 2 | 7 | 17 | |
| 4w60b16n | Inferring Brain Signals Synchronicity from a Sample of EEG Readings | 26 | 3 | 10 | 2 | 11 |
| 5gk1d91d | Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach | 26 | 2 | 6 | 4 | 14 |
| 0z02f0jh | Efficient and Ethical Response=Adaptive Randomizaiton Designs for Multi-Arm Clinical Trials With Weibull Time-to-Event Outcomes | 25 | 5 | 1 | 19 | |
| 16b7929k | Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines. | 25 | 6 | 9 | 4 | 6 |
| 93j573sb | Joint Inference for Competing Risks Data | 24 | 1 | 6 | 17 | |
| 978454rc | Meta-Kriging: Scalable Bayesian Modeling andInference for Massive Spatial Datasets | 24 | 3 | 5 | 16 | |
| 1j63v3q0 | Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels | 23 | 1 | 5 | 1 | 16 |
| 4b76m8mn | Bayesian Modeling and Analysis for Gradients in Spatiotemporal Processes | 23 | 3 | 5 | 1 | 14 |
| 6z09s1xr | Spatial Joint Species Distribution Modeling usingDirichlet Processes | 23 | 5 | 3 | 15 | |
| 41s0g6qn | A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models | 22 | 7 | 1 | 14 | |
| 55x673td | Non-Separable Dynamic Nearest-Neighbor Gaussian Process Models for Large Spatio-Temporal Data With An Application to Particulate Matter Analysis | 22 | 1 | 4 | 2 | 15 |
| 216065sz | On identifiability and consistency of the nugget in Gaussian spatial process models | 21 | 4 | 8 | 4 | 5 |
| 9hw6s7ks | Pseudo-Likelihood Based Logistic Regression forEstimating COVID-19 Infection and Case FatalityRates by Gender, Race, and Age in California | 21 | 1 | 6 | 1 | 13 |
| 4j94x6cx | Identifying Longitudinal Trends within EEGExperiments | 20 | 1 | 9 | 4 | 6 |
| 820036bc | Bayesian State Space Modeling of PhysicalProcesses in Industrial Hygiene | 20 | 1 | 5 | 14 | |
| 8s28722k | Multivariate left‐censored Bayesian modeling for predicting exposure using multiple chemical predictors | 20 | 1 | 7 | 4 | 8 |
| 9k3819jn | Bivariate Left-Censored Bayesian Model for Predicting Exposure: Preliminary Analysis of Worker Exposure during the <em>Deepwater Horizon </em>Oil Spill | 20 | 1 | 11 | 3 | 5 |
| 6mr2986t | Practical Bayesian Modeling and Inference for Massive SpatialDatasets On Modest Computing Environments | 19 | 2 | 9 | 3 | 5 |
| 1wn7s0xn | A bootstrap lasso + partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models. | 18 | 1 | 8 | 2 | 7 |
| 61n8h2np | Non-Local Priors for High Dimensional Estimation | 18 | 1 | 6 | 5 | 6 |
| 9t20g0pr | Professor | 18 | 1 | 7 | 3 | 7 |
| 5cr096pt | Joint Clustering and Registration of Functional Data | 17 | 4 | 6 | 2 | 5 |
| 9kc7q9pk | A Two-step Estimation Approach for Logistic Varying Coefficient Modeling of Longitudinal Data | 17 | 3 | 6 | 8 | |
| 0281896n | High-Dimensional Bayesian Geostatistics | 16 | 4 | 5 | 1 | 6 |
| 9n90r7hq | Prediction Summary Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data | 15 | 2 | 4 | 2 | 7 |
| 9sg1r2xj | Cluster-Randomized Trial to Increase Hepatitis B Testing among Koreans in Los Angeles | 15 | 4 | 4 | 7 | |
| 4s84j9g5 | Bayesian modeling and uncertainty quantificationfor descriptive social networks | 14 | 2 | 4 | 1 | 7 |
| 4rj1t11c | Heteroscedastic CAR models for areally referenced temporal processes for analyzing California asthma hospitalization data | 13 | 4 | 1 | 8 | |
| 63q0c96r | Cluster-Randomized Trial to Increase Hepatitis B Testing among Koreans in Los Angeles | 13 | 5 | 2 | 6 | |
| 3z75f3dc | Coastline Kriging: A Bayesian Approach | 11 | 6 | 5 | ||
| 46z4c8hd | Data-driven desirability function to measure patients� disease progression in a longitudinal study | 10 | 4 | 1 | 5 |
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