A new bin size index method for statistical analysis of multimodal datasets from materials characterization.

ABSTRACT: This paper presents a normalized standard error-based statistical data binning method, termed "bin size index" (BSI), which yields an optimized, objective bin size for constructing a rational histogram to facilitate subsequent deconvolution of multimodal datasets from materials characterization and hence the determination of the underlying probability density functions. Totally ten datasets, including four normally-distributed synthetic ones, three normally-distributed ones on the elasticity of rocks obtained by statistical nanoindentation, and three lognormally-distributed ones on the particle size distributions of flocculated clay suspensions, were used to illustrate the BSI's concepts and algorithms. While results from the synthetic datasets prove the method's accuracy and effectiveness

SUBMITTER: Jiang T 

PROVIDER: S-EPMC10322845 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature