Quantile Regression establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literatures exist for each subject matter, the authors explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.
Lingxin Hao (PhD, Sociology, 1990, University of Chicago) is Professor of Sociology at the Johns Hopkins University. She was a 2002-2003 Visiting Scholar at Russell Sage Foundation and a 2007 Resident Fellow at Spencer Foundation. Her areas of specialization include the family and public policy, social inequality, immigration, quantitative methods, and advanced statistics. The focus of her research is on social inequality, emphasizing the effects of structural, institutional, and contextual forces in addition to individual and family factors. Her research tests hypotheses derived from sociological and economic theories using advanced statistical methods and large national survey datasets. Her articles have appeared in various journals including Sociological Methodology, Sociological Methods and Research, Quality and Quantity, American Journal of Sociology, Social Forces, Sociology of Education, Social Science Research, and International Migration Review. Daniel Q. Naiman (PhD, Mathematics, 1982, University of Illinois at Urbana-Champaign) is Professor and Chair of the Applied Mathematics and Statistics at the Johns Hopkins University. He was elected as a Fellow of the Institute of Mathematical Statistics in 1997, and was an Erskine Fellow at the University of Canterbury in 2005. Much of his mathematical research has been focused on geometric and computational methods for multiple testing. He has collaborated on papers applying statistics in a variety of areas: bioinformatics, econometrics, environmental health, genetics, hydrology, and microbiology. His articles have appeared in various journals including Annals of Statistics, Bioinformatics, Biometrika, Human Heredity, Journal of Multivariate Analysis, Journal of the American Statistical Association, and Science.