Sampling distributions and the bootstrap

The bootstrap can be used to assess uncertainty of sample estimates

To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance

Self-adaptive system (SAS) can adapt itself to optimize various key performance indicators in response to the dynamics and uncertainty in environment. In this paper, we present Debt Learning Driven Adaptation (DLDA), an framework that dynamically determines when and whether to adapt the SAS at runtime. DLDA leverages the temporal adaptation debt, a notion derived from the technical debt metaphor, to quantify the time-varying money that the SAS carries in relation to its performance and Service Level Agreements. We designed a temporal net debt driven labeling to label whether it is economically healthier to adapt the SAS (or not) in a circumstance, based on which an online machine learning classifier learns the correlation, and then predicts whether to adapt under the future circumstances. We conducted comprehensive experiments to evaluate DLDA with two different planners, using 5 online machine learning classifiers, and in comparison to 4 state-of-the-art debt- oblivious triggering approaches. The results reveal the effectiveness and superiority of DLDA according to different metrics

Points of Significance: Statistics versus Machine Learning

International audienc

The IMS New Researchers\u27 Survival Guide

Statistics is a wonderfully diverse profession and graduate students making career choices have many options — especially in light of the dearth of students moving into the statistical sciences today. The three main career paths at the PhD level are in academics, industry/business and government. Each of these job types offers its own mix of intellectual challenges, financial reward, pressure and security. How a new researcher selects (or is selected by) a specific occupation in the statistical sciences sometimes seems more a function of luck than of conscious decision making. This consideration was one of the first concerns addressed by the New Researchers Committee (NRC) of the Institute of Mathematical Statistics in 1988, and this guide is the product of that (and later) thinking. We believe that if students were better informed about their choices, they would be less apprehensive, pursue their goals more effectively and, ultimately, be far more likely to find positions for which they are well suited. Similarly, if doctoral students were generally more familiar with various aspects of professional life, the entire statistical community would benefit. Among the transitional facts of life with which we believe new researchers should be acquainted are: 1. mechanisms for applying for jobs, 2. expectations associated with different types of jobs, 3. techniques for initiating an active research program, and 4. methods of becoming more involved with the broader statistical community. The Survival Guide addresses these issues, but it also offers advice on a variety of other topics which new researchers may wish to consider as they prepare to leave graduate school. This guide is based on the Statistical Science article by the New Researchers Committee of IMS (1991). See Kruse (2002) on inspiration for statistics as a career path and Stasny (2001) on the big picture with respect to academic jobs. DeMets et al (1998) and Shettle and Gaddy (1998) provide job outlooks for statisticians

Cross-validation, the Bootstrap, and Related Methods for Tuning Parameter Selection

23 pages, 1 article*Cross-validation, the Bootstrap, and Related Methods for Tuning Parameter Selection* (Altman, Naomi; Leger, Christian) 23 page

Local Polynomial Regression for Binary Response

24 pages, 1 article*Local Polynomial Regression for Binary Response* (Aragaki, Aaron; Altman, Naomi) 24 page

Self-Modeling Regression with Random Effects Using Penalized Splines

20 pages, 1 article*Self-Modeling Regression with Random Effects Using Penalized Splines* (Altman, Naomi S.; Villarreal, Julio C.) 20 page