Open Access. Powered by Scholars. Published by Universities.®

Operations Research, Systems Engineering and Industrial Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Series

Missouri University of Science and Technology

Complex networks

2015

Articles 1 - 2 of 2

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Analyzing Responses From Likert Surveys And Risk-Adjusted Ranking: A Data Analytics Perspective, Abhijit Gosavi Nov 2015

Analyzing Responses From Likert Surveys And Risk-Adjusted Ranking: A Data Analytics Perspective, Abhijit Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

We broadly consider the topic of ranking entities from surveys/opinions. Often, numerous ranks from different respondents are available for the same entity, e.g., a candidate from a pool, and yet an averaging of those ranks may not serve the purpose of identifying a consensus candidate. We first consider a risk-adjusted paradigm for ranking, where the rank is defined as the average (mean) rank plus a scalar times the risk in the rank; we use standard deviation as a risk metric. In case of a candidate being ranked either on the basis of opinions of a selection committee's members or on …


Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke Nov 2015

Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold …