Bayesian Graphical Compositional Regression for Microbiome Data
نویسندگان
چکیده
منابع مشابه
Multiple linear regression modeling for compositional data
Compositional data, containing relative information, occur regularly inmany disciplines and practical situations. Multivariate statistics methods including regression analysis have been adopted to model compositional data, but the existing research is still scattered and fragmented. This paper contributes to modeling the linear regression relationship for compositional data as both dependent an...
متن کاملA Bayesian Nominal Regression Model with Random Effects for Analysing Tehran Labor Force Survey Data
Large survey data are often accompanied by sampling weights that reflect the inequality probabilities for selecting samples in complex sampling. Sampling weights act as an expansion factor that, by scaling the subjects, turns the sample into a representative of the community. The quasi-maximum likelihood method is one of the approaches for considering sampling weights in the frequentist framewo...
متن کاملA Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data
The successional dynamics of microbial communities are influenced by the synergistic interactions of physical and biological factors. In our motivating data, ocean microbiome samples were collected from the Santa Cruz Municipal Wharf, Monterey Bay at multiple time points and then 16S ribosomal RNA (rRNA) sequenced. We develop a Bayesian semiparametric regression model to investigate how microbi...
متن کاملBayesian Quantile Regression with Adaptive Lasso Penalty for Dynamic Panel Data
Dynamic panel data models include the important part of medicine, social and economic studies. Existence of the lagged dependent variable as an explanatory variable is a sensible trait of these models. The estimation problem of these models arises from the correlation between the lagged depended variable and the current disturbance. Recently, quantile regression to analyze dynamic pa...
متن کاملBayesian Graphical Models for Multivariate Functional Data
Graphical models express conditional independence relationships among variables. Although methods for vector-valued data are well established, functional data graphical models remain underdeveloped. By functional data, we refer to data that are realizations of random functions varying over a continuum (e.g., images, signals). We introduce a notion of conditional independence between random func...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2019
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2019.1647212