Brown mathematicians prove new way to build a better estimate

PROVIDENCE, R.I. [Brown University] — How do you sift through hundreds of billions of bits of information and make accurate inferences from such gargantuan sets of data" Brown University mathematician Charles “Chip” Lawrence and graduate student Luis Carvalho have arrived at a fresh answer with broad applications in science, technology and business.

In new work published in the Proceedings of the National Academy of Sciences, Lawrence and Carvalho describe a new class of statistical estimators and prove four theorems concerning their properties. Their work shows that these “centroid” estimators allow for better statistical predictions – and, as a result, better ways to extract information from the immense data sets used in computational biology, information technology, banking and finance, medicine and engineering.

“What’s exciting about this work – what makes it every scientist’s dream – is that it’s so fundamental,” Lawrence said. “These new estimators have applications in biology and beyond and they advance a statistical method that’s been around for decades.”

For more than 80 years, one of the most common methods of statistical prediction has been maximum likelihood estimation (MLE). This method is used to find the single most probable solution, or estimate, from a set of data.

But new technologies that capture enormous amounts of data – human genome sequencing, Internet transaction tracking, instruments that beam high-resolution images from outer space – have opened opportunities to predict discrete “high dimensional” or “high-D” unknowns. The huge number of combinations of these “high-D” unknowns produces enormous statistical uncertainty. Data has outgrown data analysis.