Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure.

ABSTRACT: Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. There are parametric and non-parametric approaches for studying the data or sampling distributions, yet few resources are available to provide integrated views of data (observed or simulated), theoretical concepts, computational mechanisms and hands-on utilization via flexible graphical user interfaces. We designed, implemented and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-pr

SUBMITTER: Dinov ID 

PROVIDER: S-EPMC6155997 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature