| Quantile Regression Contributor(s): Hao, Lingxin (Author), Naiman, Daniel Q. (Author) |
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ISBN: 1412926289 ISBN-13: 9781412926287 Publisher: Sage Publications, Inc OUR PRICE: $39.90 Product Type: Paperback - Other Formats Published: April 2007 Annotation: Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines.Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to??empirical research |
| Additional Information |
| BISAC Categories: - Social Science | Statistics - Social Science | Research - Social Science | Methodology |
| Dewey: 519.536 |
| Series: Quantitative Applications in the Social Sciences |
| Physical Information: 0.3" H x 5.67" W x 8.64" (0.36 lbs) 136 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Quantile Regression, the first book of Hao and Naiman′s two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: |
Contributor Bio(s): Hao, Lingxin: - 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.Naiman, Daniel Q.: - 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. |
