Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Alzheimer Disease (1)
- Bayes Theorem (1)
- Bias (Epidemiology) (1)
- Binomial Distribution (1)
- Bioinformatics (1)
-
- Biomedical signal processing (1)
- Biostatistics (1)
- Computer Simulation (1)
- Crossover (1)
- Disease Progression (1)
- HSV (1)
- High dimensional data (1)
- High-performance computing (1)
- High-throughput genomics (1)
- Humans (1)
- Large-scale biological data analysis (1)
- Likelihood Functions (1)
- Message-passing interface (1)
- Mixed (1)
- Models (1)
- Models, Statistical (1)
- Non-negative matrix factorization (1)
- Poisson (1)
- Regression Analysis (1)
- Shedding (1)
- Statistical (1)
- Text mining (1)
Articles 1 - 3 of 3
Full-Text Articles in Statistical Models
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
COBRA Preprint Series
Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …
Binomial Regression With A Misclassified Covariate And Outcome., Sheng Luo, Wenyaw Chan, Michelle A Detry, Paul J Massman, R S. Doody
Binomial Regression With A Misclassified Covariate And Outcome., Sheng Luo, Wenyaw Chan, Michelle A Detry, Paul J Massman, R S. Doody
Faculty Publications
Misclassification occurring in either outcome variables or categorical covariates or both is a common issue in medical science. It leads to biased results and distorted disease-exposure relationships. Moreover, it is often of clinical interest to obtain the estimates of sensitivity and specificity of some diagnostic methods even when neither gold standard nor prior knowledge about the parameters exists. We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach …
Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret
Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret
UW Biostatistics Working Paper Series
We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the …