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Summary We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted for transfer learning problems in various domains including epidemiology, genetics and biomedicine. As concrete applications, we illustrate the power of our approach (i) through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data, and (ii) in overcoming a spurious correlation present in the source domain of the Waterbirds dataset.
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Building Strength in Chicago: Setting the Local and National Computer Science Agendas,
We used Manna’s theory on borrowing strength to examine the influence of local and national idea champions seeking to broaden the participation of K-12 students in computer science. Concepts from Manna’s model were applied to analyze interview data gathered from idea champions at the national and local levels. We identified examples of borrowing strength that not only highlighted the importance of individual policy entrepreneurs but also elevated the importance of community building. We introduce the concept of building strength to highlight how idea champions strategically supported capacity-building activities at a different level in the federal system prior to borrowing strength.
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- Award ID(s):
- 1738776
- PAR ID:
- 10472707
- Publisher / Repository:
- Southern Connecticut State University
- Date Published:
- Journal Name:
- Journal of Educational Leadership and Policy Studies
- ISSN:
- 2473-2826
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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