Engineering Commons^™

Specification Of Mixed Logit Models Using An Optimization Approach, Cristian David Arteaga Sanchez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Mixed logit models are a widely-used tool for studying discrete outcome problems. Modeling development entails answering three important questions that highly affect the quality of the specification: (i) what variables are considered in the analysis? (ii) what are going to be the coefficients for these variables? and (iii) what density function these coefficients will follow? The literature provides guidance; however, a strong statistical background and an ad hoc search process are required to obtain the best model specification. Knowledge of the problem context and data is required. Given a dataset including discrete outcomes and associated characteristics the problem to be …

Optimization Of Prosthetic Hands: Utilizing Modularity To Improve Grip Force, Grasp, And Versatility, Jordan William Harris

UNLV Theses, Dissertations, Professional Papers, and Capstones

It has been demonstrated that although many varieties of upper limb prosthetics exist, commercially available prosthetics are outdated and unsatisfactory. Ineffectiveness and limitations have led to some prosthesis wearers having to own multiple devices, whereas others have given up on them entirely. Even though ample research has been conducted to design and test new hand designs, the industry appears to rest in an overall stagnated state.

It was proposed here, that one problem with prosthetic research is an excess of variables involved in testing, and therefore the improper application of the scientific method. It seems that each time a research …

Generalized Clusterwise Regression For Simultaneous Estimation Of Optimal Pavement Clusters And Performance Models, Mukesh Khadka

UNLV Theses, Dissertations, Professional Papers, and Capstones

The existing state-of-the-art approach of Clusterwise Regression (CR) to estimate pavement performance models (PPMs) pre-specifies explanatory variables without testing their significance; as an input, this approach requires the number of clusters for a given data set. Time-consuming ‘trial and error’ methods are required to determine the optimal number of clusters. A common objective function is the minimization of the total sum of squared errors (SSE). Given that SSE decreases monotonically as a function of the number of clusters, the optimal number of clusters with minimum SSE always is the total number of data points. Hence, the minimization of SSE is …