Assessing coupling dynamics from an ensemble of time series

Finding interdependency relations between (possibly multivariate) time series provides valuable knowledge about the processes that generate the signals. Information theory sets a natural framework for non-parametric measures of several classes of statistical dependencies. However, a reliable estimation from information-theoretic functionals is hampered when the dependency to be assessed is brief or evolves in time. Here, we show that these limitations can be overcome when we have access to an ensemble of independent repetitions of the time series. In particular, we gear a data-efficient estimator of probability densities to make use of the full structure of trial-based measures. By doing so, we can obtain time-resolved estimates for a family of entropy combinations (including mutual information, transfer entropy, and their conditional counterparts) which are more accurate than the simple average of individual estimates over trials. We show with simulated and real data that the proposed approach allows to recover the time-resolved dynamics of the coupling between different subsystems

Tackling Transfer: A Guide to Convening Community Colleges and Universities to Improve Transfer Student Outcomes

Research shows that very few community college students, many of whom seek a bachelor’s degree, successfully transfer to and graduate from a four-year institution. In fact, only 15 percent of all community college students complete a four-year degree in less than six years. The numbers are far more daunting for Black, Hispanic, and low-income students—all of whom enroll in community colleges at disproportionately high numbers. In an effort to improve transfer student success, the Aspen Institute, CCRC, Public Agenda, and SOVA have released this implementation guide designed to help state entities organize workshops within which teams from two- and four-year institutions work together to improve transfer and graduation outcomes for their students. Through data analysis and self-reflection of institutional practices, these workshops help institutions develop action plans (individually and among partners) to improve transfer student success. This guide is accompanied by a number of additional resources (see below), including the CCRC’s How to Measure Community College Effectiveness in Serving Transfer Students. The guide was created as part of the larger Tackling Transfer project and serves as a complement to the 2016 Transfer Playbook

Similarity, precedent and argument from analogy

In this paper, it is shown (1) that there are two schemes for argument from analogy that seem to be competitors but are not, (2) how one of them is based on a distinctive type of similarity premise, (3) how to analyze the notion of similarity using story schemes illustrated by some cases, (4) how arguments from precedent are based on arguments from analogy, and in many instances arguments from classification, and (5) that when similarity is defined by means of episode schemes, we can get a clearer idea of how it integrates with the use of argument from classification and argument from precedent in case-based reasoning by using a dialogue structure

An Improved Method for the Quantitative Assessment of Customer Priorities

Companies constantly seek to enhance customer satisfaction by improving product or service features. Two methods are commonly used to assess customer priorities for product or service features from individual customers: ratings and constant-sum allocation. A common problem with the ratings approach is that it does not explicitly capture priorities; it is easy for the respondent to say that every feature is important. The traditional constant-sum approach overcomes this limitation, but with a large number of (ten or more) features, it becomes difficult for the respondent to divide a constant sum among all of them. ASEMAP (pronounced Ace-Map, Adaptive Self-Explication of Multi-Attribute Preferences) is a new web-based interactive method for assessing customer priorities. It consists of the respondent first grouping the features into two or more categories of importance (e.g., more important, less important). The respondent then ranks the features in each of the categories from the most important to least important thereby resulting in an overall rank order of the features. In order to estimate quantitative values for the priorities, the computer-based approach breaks down the feature importance question into a sequence of constant-sum paired comparison questions. The paired comparisons are chosen adaptively for each respondent to maximize the information elicited from each paired comparison question. The respondent needs to be questioned only on a small subset of all possible paired comparisons. Importances for the features are estimated from the constant-sum paired comparisons by log-linear multiple regression. The empirical context was that of assessing research priorities among fifteen topics from managers of Marketing Science Institute's member companies. The ASEMAP method provided a statistically significant and substantially better validity than the traditional constant sum method.