A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data

A 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...

Bayesian Semiparametric Regression Analysis of Multicategorical Time-space Data

SUMMARY We present a uniied semiparametric Bayesian approach based on Markov random eld priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial eeects as well as nonlinear eeects of metrical covariates in exibl...

Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data

A 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...

Approaches for Semiparametric Bayesian Regression

Developing regression relationships is a primary inferential activity. We consider such relationships in the context of hierarchical models incorporating linear structure at each stage. Modern statistical work encourages less presump-tive, i.e., nonparametric speciications for at least a portion of the modeling. That is, we seek to enrich the class of standard parametric hierarchical models by ...