Our team can also assist with internal model validation for algorithm testing across large datasets and tailor our approach to datasets that are tall (thousands or millions of samples), wide (thousands or millions of variables), or both. Our extensive work with sampling and validity means we can also provide a great deal of guidance and problem-solving for contaminated data.

While it’s become standard across industries to complete statistical analysis for these large-scale projects using R, our statisticians can also work with SPSS, SAS, STATA, Python, and MATLAB, depending on your firm’s software preferences and specific dataset development and data analysis needs.

Whether your dataset requires a subsampling-based method, a divide-and-conquer design, or online updating, our highly experienced statistical consulting team can help you to develop and perform analysis for any of the following methods:

• Bootstrapping
• Leveraging
• Regression tree
• LASSO
• Mean-log likelihood
• Subsampling Markov chain Monte Carlo (MCMC) and parallel MCMC
• Aggregated estimating equations
• Majority voting
• Random forest
• Ensemble
• Screening for ultrahigh dimensions
• Online updating for data streams