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Learn the distinctions between simple and stratified random sampling. Understand how researchers use these methods to accurately represent data populations.

Model-based clustering provides a principled way of developing clustering methods. We develop a new model-based clustering methods for count data. The method combines clustering and variable selection ...

Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels, and even comments scraped from the internet can offer valuable ...

Introduction: Variable selection methods can provide an unbiased means of identifying informative predictors but have rarely been applied to CVD risk prediction. Hypothesis: Additional variables ...

Abstract: The majority of fault diagnosis techniques for root cause identification often overlook a critical aspect of complex industrial operations, which is reflected in the neutralization and ...

ABSTRACT: Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships, controlling redundant information, and improving ...

Cancer machine learning research is often limited by overparameterization and overfitting, which arise because cancer ‘omic’ variables significantly outnumber patient samples. Traditional feature ...

ABSTRACT: Function-on-scalar regression is a type of function response regression used to analyze the relationship between function response and a set of scalar predictor factors. The variable ...

Abstract: Variable selection is the process of selecting a subset of relevant variables for use in model construction. It is a critical step in forecasting but has not yet played a major role in the ...