AIJun 8, 2014

Introduction to Neutrosophic Statistics

arXiv:1406.2000v1278 citations
AI Analysis

This work addresses the challenge of handling uncertain or incomplete data in statistics, which is incremental as it builds on an existing notion.

The paper tackles the problem of statistical analysis with indeterminate data, such as imprecise or ambiguous population samples, by developing the concept of neutrosophic statistics from a 1995 notion and presenting practical examples.

Neutrosophic Statistics means statistical analysis of population or sample that has indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data. For example, the population or sample size might not be exactly determinate because of some individuals that partially belong to the population or sample, and partially they do not belong, or individuals whose appurtenance is completely unknown. Also, there are population or sample individuals whose data could be indeterminate. In this book, we develop the 1995 notion of neutrosophic statistics. We present various practical examples. It is possible to define the neutrosophic statistics in many ways, because there are various types of indeterminacies, depending on the problem to solve.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes