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Full-Text Articles in Engineering
Identification Of Critical Roles Of Program Managers And Contracting Officers, Daniel R. Shingledecker
Identification Of Critical Roles Of Program Managers And Contracting Officers, Daniel R. Shingledecker
Theses and Dissertations
The purpose of this research was to identify current disagreement among acquisition professionals in regards to their role and authority; and to identify benefits and risks associated with combining program management and contracting officer roles into a single position. The research questions were answered through a literature review and use of the Delphi technique. The literature review indicated that the DoD has been operating with multiple conflicting perceptions of authority among its key acquisition professionals. The literature also identified the need for complex organizations, such as DoD acquisition offices, to pair high levels of responsibility with equally high levels of …
Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin
Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin
Theses and Dissertations
Minimum distance estimate is a statistical parameter estimate technique that selects model parameters that minimize a good-of-fit statistic. Minimum distance estimation has been demonstrated better standard approaches, including maximum likelihood estimators and least squares, in estimating statistical distribution parameters with very small data sets. This research applies minimum distance estimation to the task of making time series predictions with very few historical observations. In a Monte Carlo analysis, we test a variety of distance measures and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when …
Multiple Model Adaptive Estimation For Time Series Analysis, Ibrahim Dulger
Multiple Model Adaptive Estimation For Time Series Analysis, Ibrahim Dulger
Theses and Dissertations
Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions. Our Kalman filters have a dynamics model based on a Box-Jenkins Auto-Regressive Moving Average (ARMA) model and a measure model with additive noise. The time-series prediction is based …