Directional Decision Lists

In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples of this family are decision lists with monotonically decreasing (or increasing) probabilities. On simulated data we empirically confirm that the proposed model family is easier to train than general decision lists. We exemplify the practical usability of our approach by identifying problem symptoms in a manufacturing process.Comment: IEEE Big Data for Advanced Manufacturin

Intention‐to‐treat analysis with treatment discontinuation and missing data in clinical trials

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112012/1/sim6352.pd

Developing the Evaluation Framework of Technology Foresight Program: Lesson Learned from European Countries

Atlanta Conference on Science and Innovation Policy 2009This presentation was part of the session : Roundtables on Methods, Measures, and DataThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Foresight activities are valued in many countries since 1990s due to their long term strategic planning. These governments consequently allocate most resources in these foresight activities. As a result, the paper mainly develops the evaluation framework of technology foresight program, by integrating the concepts of evaluation and logic framework with the experience of foresight evaluation from developed countries, for instance European Union, Britain etc., to realize the outcomes of implementing foresight activities. Taking Sweden as a case study, the paper is also proposed to show the effectiveness of this new framework

A New Powerful Nonparametric Rank Test for Ordered Alternative Problem

We propose a new nonparametric test for ordered alternative problem based on the rank difference between two observations from different groups. These groups are assumed to be independent from each other. The exact mean and variance of the test statistic under the null distribution are derived, and its asymptotic distribution is proven to be normal. Furthermore, an extensive power comparison between the new test and other commonly used tests shows that the new test is generally more powerful than others under various conditions, including the same type of distribution, and mixed distributions. A real example from an anti-hypertensive drug trial is provided to illustrate the application of the tests. The new test is therefore recommended for use in practice due to easy calculation and substantial power gain