Panning for gold: Enhancing the precision of sensitivity test data

Statistical Analysis and Data Mining

Hamada, Michael S.¹  Collins, David H.¹  Weaver, Brian P.¹ 

[1] Los Alamos National Laboratory Statistical Sciences Group Los Alamos New Mexico

关键词: binary regression;    data mining;    logistic;    penalized maximum likelihood;    precision;    probit;    sensitivity testing analysis;   

DOI  :  10.1002/sam.11345

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Sensitivity tests apply a range of stimulus values to experimental subjects and record binary responses in order to estimate the distribution of threshold values in the subject population, where thresholds delineate responses from nonresponses. In many applications, such as explosives engineering, individual tests are expensive and are conducted in small runs. Scarcity of data results in nonexistence of estimates, or estimates with low precision. We discuss various methods, such as combining test runs, covariate analysis, and penalized maximum likelihood, for enhancing precision and “mining more gold” from expensive test results.

【 授权许可】

Unknown