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Articles 1 - 3 of 3
Full-Text Articles in Statistical Models
Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston
Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston
Joseph M Hilbe
SAS Macro to estimate censored Poisson data, using method of Hilbe. See Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed (Cambridge University Press)
Groundwater Model Parameter Estimation Using Response Surface Methodology, Richard M. Cotman
Groundwater Model Parameter Estimation Using Response Surface Methodology, Richard M. Cotman
Theses and Dissertations
This thesis examined the use of response surface methodology (RSM) to estimate the parameters of a finite-element groundwater model. An existing two-dimensional, steady-state flow model of a fractured carbonate groundwater system in southwestern Ohio served as the calibration target data set. A Plackett-Burman screening design showed that only four of the ten hydraulic conductivity zones significantly contributed to the output of the finite-element model. Also, the effective porosity parameter did not significantly affect the model's output. Using only the four significant hydraulic conductivity parameters; four two-level, four-factor designed experiments were conducted to exploit the first-order response surface defined by a …
Comparing Traditional Statistical Models With Neural Network Models: The Case Of The Relation Of Human Performance Factors To The Outcomes Of Military Combat, William Oliver Hedgepeth
Comparing Traditional Statistical Models With Neural Network Models: The Case Of The Relation Of Human Performance Factors To The Outcomes Of Military Combat, William Oliver Hedgepeth
Engineering Management & Systems Engineering Theses & Dissertations
Statistics and neural networks are analytical methods used to learn about observed experience. Both the statistician and neural network researcher develop and analyze data sets, draw relevant conclusions, and validate the conclusions. They also share in the challenge of creating accurate predictions of future events with noisy data.
Both analytical methods are investigated. This is accomplished by examining the veridicality of both with real system data. The real system used in this project is a database of 400 years of historical military combat. The relationships among the variables represented in this database are recognized as being hypercomplex and nonlinear.
The …