Year: 2013
Type: article
Abstract: Measuring individuals' preferences for goods and services has recently obtained considerable attention in both public and private contexts. Individuals' judgments are used for many different purposes, including setting social policies and forecating the acceptance of a new product in the market. While preference modeling is a long-studying problem, modern applications, related to the web, make it an actual topic. Respondents are called to express their preferences among a set of alternatives and collected data can be represented in various kinds of matrices. This thesis is focused on some popular methods to estimate either scores or ranks of a set of alternatives by analyzing a generalized tournament matrix. The proposed methods are compared via simulation and some special situations are investigated to detect their reliability. Our aim is to compare methods that assume parametric hypotheses on data distribution with methods that do not require such hypotheses. When respondents do not compare directly two alternatives, the matrix representing their preferences may show one or more missing values. We propose a method to estimate the missing entries of a generalized tournament matrix based on the minimization of the sum of its singular values, i.e. the nuclear norm. We perform some simulation studies to investigate the nuclear norm minimization effectiveness.
Author: Manuela Scioni
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
Funders: National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Neurological Disorders and Stroke, National Center for Research Resources, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Developmental Adaptation: Child Health Research Center, G2645212210, Predoctoral Program in Cellular and Molecular Biology, STUDIES OF EPSTEIN-BARR VIRUS, Yale Microarray Center for Research on the Nervous System, Studies of Epstein-Barr Virus, Yale University Clinical and Translational Science Award Program, Early cellular immune responses to EBV, Molecular Basis of Cancer Virus Replication, Transformation, and Innate Defense
OpenAlex ID: W19889776