In a high-throughput experiment one performs measurements on thousands of variables (e.g. genes or proteins) across two or more experimental conditions. In bioinformatics, we come across such data generated using technologies like Microarrays, Next generation sequencing, Mass spec etc. 1,969 more words

## Tags » Random Effects

#### High Dimensional Data & Hierarchical Regression

In a high-throughput experiment one performs measurements on thousands of variables (e.g. genes or proteins) across two or more experimental conditions. In bioinformatics, we come across such data generated using technologies like Microarrays, Next generation sequencing, Mass spec etc. 1,969 more words

#### Hierarchical Models: A Binomial Model with Shrinkage

The material in this post comes from various sources, some of which can be found in

[1] Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition. 2,046 more words

#### Hierarchical Models: A Binomial Model with Shrinkage

The material in this post comes from various sources, some of which can be found in

[1] Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition. 2,048 more words

#### Hierarchical Linear Regression - 2 Level Random Effects Model

Regression is a popular approach to modelling where a response variable is modelled as a function of certain predictors – to understand the relations between variables. 1,663 more words

#### Hierarchical Linear Regression - 2 Level Random Effects Model

Regression is a popular approach to modelling where a response variable is modelled as a function of certain predictors – to understand the relations between variables. 1,663 more words