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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 - Sales Rank: #1470944 in Books
- Published on: 2007-04-18
- Original language: English
- Number of items: 1
- Dimensions: 8.64" h x .30" w x 5.67" l, .36 pounds
- Binding: Paperback
- 136 pages
About the Author
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.
Most helpful customer reviews
6 of 7 people found the following review helpful.
Robust regression
By wiredweird
The non-specialist (e.g. me) who thinks of regression thinks immediately of linear (i.e least-squares) regression - and stops there. This bakes in more hidden assumptions about the data than I care to name. For one, it assumes outliers won't skew the result, or that they can be dropped without [much] changing the result. When the extrema are the subjects of greatest interest, this assumption clearly falls on its face. Another is that the central tendency is the statistic of interest and residuals have zero mean. In many cases, wrong and wrong. Asymmetric and fat-tailed distributions pop up like mushrooms after a rain. Likewise, changes in the tenth- or ninetieth-percentile mark could be the subject of interest, as in "How has the distribution of wealth changed over time?" Without ending the list of least-squares assumptions, another big one comes to mind: what I can compute easily with closed-form expressions. Given that you have more computing power in your cell phone than existed on earth sixty years ago, that borders on the sin of sloth.
Quantile regressions pull the statistical workhorse of regression well in the non-parametric direction. Although somewhat dense, this provides a reasonably clear introduction to quantile regression, not "How does the mean value change with the input value?" but "How does the median change?" Then, from the median (50th percentile) it generalizes readily to the N-th percentile. Although the algorithm performs a startling geometric transformation before turning into linear programming, a bit of thought exposes the significance of the transformation and the logic of the minimization algorithm. Starting there, it moves smoothly forward into discussion of confidence intervals, a case study, and some executable code. The little-known language of the latter might block many readers from deriving its full value, and I never convinced myself of the non-parametric purity of the confidence intervals. (That's probably just me, though.)
Unlike least-squares regression, quantile regression gives essentially unchanged results under any positively monotonic data transformation. For example, if regression of raw data gives absolute differences, then log-scaling gives percentage differences, with no qualms about what the regression did to the transformed data. Still, this comes down do a linear regression at bottom, y=mx+b with m and b to be inferred from the data. Like least-squares, it can not directly produce nonlinear relationships, and can't handle non-monoticity at all. No one should let the fact of a tool's limitations stop them from using it, though. Every tool has constraints, and the successful practitioner knows the assumptions that bound the useful range of each one. Quantile regression's range of value might overlap least-squares' in many areas (like, where mean, median, and mode coincide), but adds significant area of inquiry as well. When I compare the cost of this book to the price I would have paid in classroom time getting the same knowledge, this brief but informative text is a rock-bottom bargain. And, in truth, I might never have found the classroom where this would have been presented. A prepared and diligent reader will find huge value in this book - not just in its presentation of new ideas, but in its critique of what you might have thought familiar.
-- wiredweird
0 of 0 people found the following review helpful.
Five Stars
By andres
By far the most accessible and well written introduction to quantile regressions that i have come across. Highly recomended.
0 of 11 people found the following review helpful.
Very quick service
By Joe
Very pleased with the both the speed of the order and the subject presentation, I would highly recommend the seller
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