Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression

Individual auto market share is always one of the major concerns of any auto manufacturing company. It indicates a lot of things about the company such as profitability, competitiveness, short term and long term development and so on. The focus of this paper is to construct a quantitative model that can precisely formulate the social welfare function of the auto market by relating the auto market share with the utilities of the significant vehicle-purchasing criteria (e.g. reliability, safety, etc.) that concern vehicle buyers. Social welfare function is defined as the additive form of the utility of each criterion considered, it’s a good estimation of the customer preferences. The assessment methods used in this research include random utility theory and B-spline fitted logistic regression model. G-test is applied to select the criteria that is significant to the vehicle market social welfare, pseudo R-squareds are used as the model goodness-of-fit measures and Kendall rank correlation coefficient and Matthews correlation coefficient are applied to validate the assessment model. A case study using the U.S. auto market and vehicles related data collected in years of 2013 and 2014 are conducted to illustrate the assessment process of the social welfare function, and the data from 2015 are used to validate the assessment model.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136069/1/Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.pdfDescription of Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.pdf : Master of Science in Engineering Thesi

Pattern matching encryption, strategic equivalence of range voting and approval voting, and statistical robustness of voting rules

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 119-123).We present new results in the areas of cryptography and voting systems. 1. Pattern matching encryption: We present new, general definitions for queryable encryption schemes - encryption schemes that allow evaluation of private queries on encrypted data without performing full decryption. We construct an efficient queryable encryption scheme supporting pattern matching queries, based on suffix trees. Storage and communication complexity are comparable to those for (unencrypted) suffix trees. The construction is based only on symmetric-key primitives, so it is practical. 2. Strategic equivalence of range voting and approval voting: We study strategic voting in the context of range voting in a formal model. We show that under general conditions, as the number of voters becomes large, strategic range-voting becomes equivalent to approval voting. We propose beta distributions as a new and interesting way to model voter's subjective information about other votes. 3. Statistical robustness of voting rules: We introduce a new notion called "statistical robustness" for voting rules: a voting rule is statistically robust if, for any profile of votes, the most likely winner of a sample of the profile is the winner of the complete profile. We show that plurality is the only interesting voting rule that is statistically robust; approval voting (perhaps surprisingly) and other common voting rules are not statistically robust.by Emily Shen.Ph.D