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Stats, Volume 2, Issue 3

September 2019 - 4 articles

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Cover Story: Linear correlation is a common traditional statistic quantitative analysts study. Its classical version quantifies two attributes’ value pairing trends for a set of observations, whose nature can be negative, none, or positive. Researchers rarely focus on the few advantageous qualities negative correlation displays. Extending this statistic to geographic data quantifies the relationship between neighboring value pairs of a single attribute (i.e., spatial autocorrelation). Researchers rarely treat negative spatial autocorrelation, sometimes arguing that it seldom characterizes georeferenced data, making it one of the most neglected spatial statistics/econometrics concepts. This paper redresses this situation, furnishing concrete examples of it, and demonstrating its frequent coexistence as a mixture with positive spatial autocorrelation. View this paper.

Articles (4)

  • Feature Paper
  • Article
  • Open Access
29 Citations
11,475 Views
28 Pages

15 August 2019

Negative spatial autocorrelation is one of the most neglected concepts in quantitative geography, regional science, and spatial statistics/econometrics in general. This paper focuses on and contributes to the literature in terms of the following thre...

  • Article
  • Open Access
5 Citations
3,585 Views
17 Pages

19 July 2019

The popular concept of slash distribution is generalized by considering the quotient Z = X/Y of independent random variables X and Y, where X is any continuous random variable and Y has a general beta distribution. The density of Z can usually be exp...

  • Article
  • Open Access
3 Citations
3,590 Views
15 Pages

Confidence Sets for Statistical Classification

  • Wei Liu,
  • Frank Bretz,
  • Natchalee Srimaneekarn,
  • Jianan Peng and
  • Anthony J. Hayter

30 June 2019

Classification has applications in a wide range of fields including medicine, engineering, computer science and social sciences among others. In statistical terms, classification is inference about the unknown parameters, i.e., the true classes of fu...