Military Technology and Sample Selection Bias
نویسندگان
چکیده
منابع مشابه
Sample Selection Bias Correction Theory
This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. This relies on weights derived by various estimation techniques based on finite samples. We analyze the effe...
متن کاملModels for Sample Selection Bias
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive o...
متن کاملCredit scoring and the sample selection bias
For creating or adjusting credit scoring rules, usually only the accepted applicant’s data and default information are available. The missing information for the rejected applicants and the sorting mechanism of the preceding scoring can lead to a sample selection bias. In other words, mostly inferior classification results are achieved if these new rules are applied to the whole population of a...
متن کاملDetecting and Statistically Correcting Sample Selection Bias
Researchers seldom realize 100% participation for any research study. If participants and non-participants are systematically different, substantive results may be biased in unknown ways, and external or internal validity may be compromised. Typically social work reGary Cuddeback, MSW, MPH, is Research Associate, Cecil G. Sheps Center for Health Services Research, University of North Carolina a...
متن کاملRobust Classification Under Sample Selection Bias
In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions. Due to its asymptotic properties, sample reweighted empirical loss minimization is a commonly employed technique to deal with this difference. However, given finite amounts of labeled s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Social Science History
سال: 2020
ISSN: 0145-5532,1527-8034
DOI: 10.1017/ssh.2020.16