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Thompson says America must "unchoose" policies that have led to mass incarceration

Axinn says new data on campus rape will "allow students to see for themselves the full extent of this problem"

Frey says white population is growing in Detroit and other large cities


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 5 at noon, 6050 ISR
Colter Mitchell: Biological consequences of poverty

A Method for Testing Low-Value Spatial Clustering for Rare Diseases

Publication Abstract

Lin, Ge, and T.L. Zhang. 2004. "A Method for Testing Low-Value Spatial Clustering for Rare Diseases." Acta Tropica, 91(3): 279-289.

This paper proposes a method that tests for the existence of low-value spatial clustering while accounting for the influence of high-value clustering. Although the method was developed in reference to the Tango test, it can be extended to other testing methods. The simulation results showed that the proposed method is able to effectively detect low-value clustering with substantially lower rates of type I errors than those of the Tango test, while maintaining comparable statistical power. Applying the method in a case study of leukemia in Minnesota demonstrated an overall tendency toward low-value clustering of leukemia mortality for males but provided inconclusive results for females. (C) 2004 Elsevier B.V. All rights reserved.

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