The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

Experimental data and mathematical models are beginning to take equal billing in systems biology. Experimental observations without a framework in which to link them offer researchers only limited ...

The nine methods of model selection implemented in PROC REG are specified with the SELECTION= option in the MODEL statement. Each method is discussed in this section. This method is the default and ...

Suppose we observe samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and ...

Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of "big ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

To change the model selection criterion, click the button labeled Root Mean Square Error or select Options from the menu bar and then select Model Selection Criterion ...

A statistical model -- now an easy-to-use software tool -- local police can use to identify a series of related crimes and nab a suspect has been unveiled. Crime linkage is the investigative process ...

'Genomic prediction' has been used by researchers to predict the performance of hybrid rice. Genomic prediction is a new technology that could potentially revolutionize hybrid breeding in agriculture.