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Quantile Regression
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Quantile Regression

Volume: 149
April 2007 | 136 pages | SAGE Publications, Inc
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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.
 
Series Editor's Introduction
 
Acknowledgments
 
1. Introduction
 
2. Quantiles and Quantile Functions
 
3. Quantile-Regression Model and Estimation
 
4. Quantile Regression Inference
 
5. Interpretation of Quantile-Regression Estimates
 
6. Interpretation of Monotone-Transformed QRM
 
7. Application to Income Inequality in 1991 and 2001
 
Appendix: Stata Codes
 
References
 
Index
 
About the Authors

Lingxin Hao

Lingxin Hao is a professor of sociology at Johns Hopkins University. Her specialties include quantitative methodology, social inequality, sociology of education, migration, and family and public policy. She is the lead author of two QASS monographs Quantile Regression and Assessing Inequality. Her research has appeared in the Sociological Methodology, Sociological Methods and Research, American Journal of Sociology, Demography, Social Forces, Sociology of Education, and Child Development, among others. More About Author

Daniel Q. Naiman

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... More About Author

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ISBN: 9781412926287
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