Empirical likelihood for single-index varying-coefficient models

In this paper, we develop statistical inference techniques for the unknown coefficient functions and single-index parameters in single-index varying-coefficient models. We first estimate the nonparametric component via the local linear fitting, then construct an estimated empirical likelihood ratio function and hence obtain a maximum empirical likelihood estimator for the parametric component. Our estimator for parametric component is asymptotically efficient, and the estimator of nonparametric component has an optimal convergence rate. Our results provide ways to construct the confidence region for the involved unknown parameter. We also develop an adjusted empirical likelihood ratio for constructing the confidence regions of parameters of interest. A simulation study is conducted to evaluate the finite sample behaviors of the proposed methods.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ365 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Looking Out and Looking In: Promoting Academic Success through Peer Review and Self-Reflection in Online and Face-to-Face Courses

Poster presentation. Presented at the 34th Annual International Lilly Conference on Teaching, Miami University, Oxford, Ohio, November 20-23, 2014.This presentation will illuminate why peer review and self-reflection are important in promoting academic success and student engagement in both online and face-to-face courses. It will showcase the effective and easy-to-implement techniques that the presenters use to provide students with opportunities to look outward and inward and how the results contribute to course grades and the overall assessment of student learning. Attendees will be able to incorporate these techniques into any course at any level

Impact of SoTL on Online and Face-to-Face Courses

Presented at The Seventeen Annual Midwest Conference on the Scholarship of Teaching and Learning, Indiana University South Bend. April 1, 2016.Drawn from the literature and the experiences of two faculty members, this presentation will highlight the many opportunities to engage in SoTL and demonstrate the impact that SoTL activities have had on the teaching approaches and student learning outcomes that the presenters have seen in the diverse array of face-to-face and online courses that they teach

Using Technology to Provide Multimodal Learning Opportunities and Enhance Student Engagement in Face-to-Face and Online Courses

The presenters will showcase how web-based technologies and media formats, such as text, audio, video, images, games and simulations, can be effectively designed and delivered to encourage student-to-student interaction and to motivate students to be active participants in the learning process in both face-to-face and online courses. As part of the poster, the attendees will provide practical suggestions about integrating technology and media into courses.This poster showcases how the presenters incorporate a variety of technologies, media and interactive elements to provide multimodal learning opportunities and enhance student engagement in both face-to-face and online courses. It highlights the pedagogical, logistical and evaluative considerations of creating a multimodal environment that will address the needs of a wide range of students with diverse backgrounds and learning styles, including whether student learning outcomes are achieved

GREAT Expectations: Promoting Active and Collaborative Learning in Online and Face-to-face Courses

PowerPoint slides from poster and handouts (bibliography and examples of assignments from courses). Included demonstration of applications developed in courses.GREAT Expectations stands for Group work, Reflection, Evaluation of self and peers, Application of course content to real-world problems and Testing (pre- and post-testing). It encompasses all of the techniques and technologies that the presenters use to promote active and collaborative learning in their online and face-to-face courses and to encourage students to take more responsibility for their own learning.Peer-reviewed presentation at the 2014 E.C. Moore Symposium, IUPUI, April 4, 2014

A Carrot or a Stick: Enhancing Student Motivation through Accountability in Online and Face-to-Face Courses

This engaging presentation features a variety of innovative and practical teaching strategies that are intended to increase student accountability in online and face-to-face courses. Based on their assessment methods and drawing from the literature, the presenters demonstrate how student accountability has a direct impact on extrinsic and intrinsic student motivation that translates into improved student learning and academic success

Big and Small: Active Learning in Online and Face-to-Face Courses

Drawn from the literature and the experiences of two faculty members, this presentation will highlight a variety of opportunities to promote active learning in online and face-to-face courses. Although some options may require substantial adjustment in pedagogical and logistical approaches, they will demonstrate how even small changes in a course can result in big improvements in student engagement and success

Impact of SoTL on Online and Face-to-Face Courses

Drawn from the literature and the experiences of two faculty members, this presentation will highlight the many opportunities to engage in SoTL and demonstrate the impact that SoTL activities have had on the teaching approaches and student learning outcomes that the presenters have seen in the diverse array of face-to-face and online courses that they teach

Revising Program-Level Learning Outcomes: Methodology, Results and Lessons Learned

During the session, the presenters will: • introduce their methodology for revising the Program-Level Learning Outcomes (PLOs) • present their preliminary findings from the project • identify what worked well versus what was less effective with their methodology • provide suggestions for how their methodology could be improved upon in the future • offer insights into how their methodology could be adapted by other disciplines to revise student learning outcomes for a wide variety of degrees and programsThe presenters developed a methodology for revising program-level learning outcomes that is efficient, effective and readily adaptable for other degrees. They will introduce their methodology, present preliminary findings, identify what worked well versus how the methodology could be improved upon in the future and offer insights into how the methodology could be used to revise learning outcomes in other disciplines

Estimation for a Partial-Linear Single-Index Model

In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimation procedure is proposed to estimate the link function for the single index and the parameters in the single index, as well as the parameters in the linear component of the model. Asymptotic normality is established for both parametric components. For the index, a constrained estimating equation leads to an asymptotically more efficient estimator than existing estimators in the sense that it is of a smaller limiting variance. The estimator of the nonparametric link function achieves optimal convergence rates; and the structural error variance is obtained. In addition, the results facilitate the construction of confidence regions and hypothesis testing for the unknown parameters. A simulation study is performed and an application to a real dataset is illustrated. The extension to multiple indices is briefly sketched.Comment: 43 pages and 2 figure