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Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
Mathematical statistics --- Regression analysis. --- Mathematical statistics. --- Analyse de régression --- Statistique mathématique --- Regression analysis --- 330.115 --- 519.536 --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Econometrie --- Statistical methods --- 330.115 Econometrie --- Analyse de régression --- Statistique mathématique --- econometrie --- Quantitative methods (economics) --- Business, Economy and Management --- Economics
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Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
Mathematical statistics --- Multilevel models (Statistics) --- 519.536 --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Multilevel models (Statistics). --- Regression analysis. --- Methoden en technieken --- statistiek. --- wiskundige statistiek --- regressie-analyse --- #SBIB:303H520 --- 519.2 --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Mathematical models --- Multivariate analysis --- Structural equation modeling
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Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. This analysis provides a comprehensive account of models and methods to interpret such data. The authors have conducted research in the field for nearly fifteen years and in this work combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. The treatment will be useful to researchers in areas such as applied statistics, econometrics, operations research, actuarial studies, demography, biostatistics, quantitatively-oriented sociology and political science. The book may be used as a reference work on count models or by students seeking an authoritative overview. The analysis is complemented by template programs available on the Internet through the authors' homepages.
Regression Analysis --- Econometrics --- Business, Economy and Management --- Economics --- Mathematical statistics --- Regression analysis. --- Econometrics. --- Analyse de régression --- Econométrie --- Economics, Mathematical --- Statistics --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Regression analysis --- 519.536 --- 303.5 --- AA / International- internationaal --- 330.115 --- 519.235 --- 330.115 Econometrie --- Econometrie --- 519.235 Statistics of dependent variables. Contingency tables --- Statistics of dependent variables. Contingency tables --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Wiskundige statistiek --- #SBIB:303H520 --- #SBIB:303H522 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Methoden sociale wetenschappen: handboeken statistische analyse
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