Quantile Regression - Hao, Lingxin

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Résumé :
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
...

Biographie:
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
...

Sommaire:

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.

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.