Computational Methods in Statistics

Experimental Mathematics and Computational Statistics

The field of statistics has long been noted for techniques to detect patterns and regularities in numerical data. In this article we explore connections between statistics and the emerging field of “experimental mathematics.” These includes both applications of experimental mathematics in statistics, as well as statistical methods applied to computational mathematics.

FUZZY FRACTIONAL PARTIAL DIFFERENTIAL EQUATIONS IN PARTIALLY ORDERED METRIC SPACES

In this paper, we consider fuzzy fractional partial differential equations under Caputo generalized Hukuhara differentiability. Some new results on the existence and uniqueness of two types of fuzzy solutions are studied via  weakly contractive mapping in the partially ordered metric space. Some application examples are presented to illustrate our main results.

Statistical Methods in Medical Research Book Review: Computational Statistics

The Third Special Issue on Computational Econometrics

The journal Computational Statistics and Data Analysis aims to have regular issues on computational econometrics. Of particular interest are papers in important areas of econometric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources of information about the most recent developments in computational econometrics that are cur...

Second Special issue on Computational Econometrics

The journal Computational Statistics and Data Analysis aims to have regular issues in Computational Econometrics. Of particular interest are papers in important areas of econo-metric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources of information about the most recent developments in computational econometrics that are cu...

Camparison of Numerically Stability of Two Algorithms for the Calculation of Variance

In descriptive statistics, there are two computational algorithms for determining the variance S2, of a set of observations : Algorithm 1: S2= - , Algorithm 2: S2= , where . It is interesting to discuss, which of the above formulas is numerically more trustworthy in machine numbers sets. I this paper, based on total effect of rounding error, we prove that the second Algorithm is better...