LEADER 05579nam 2200721 450 001 9910807951903321 005 20200520144314.0 010 $a1-118-75271-6 010 $a1-118-75268-6 010 $a1-118-75319-4 035 $a(CKB)2550000001138476 035 $a(EBL)1489927 035 $a(OCoLC)861559496 035 $a(SSID)ssj0001041328 035 $a(PQKBManifestationID)11601196 035 $a(PQKBTitleCode)TC0001041328 035 $a(PQKBWorkID)11009036 035 $a(PQKB)11526824 035 $a(MiAaPQ)EBC1489927 035 $a(DLC) 2013030463 035 $a(Au-PeEL)EBL1489927 035 $a(CaPaEBR)ebr10788065 035 $a(CaONFJC)MIL538177 035 $a(PPN)191912182 035 $a(EXLCZ)992550000001138476 100 $a20131109d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aQuantile regression $etheory and applications /$fCristina Davino, Marilena Furno, Domenico Vistocco 210 1$aChichester, England :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (290 p.) 225 0$aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a1-119-97528-X 311 $a1-306-06926-2 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aQuantile Regression: Theory and Applications; Copyright; Contents; A.2.2 Summary statistics; Preface; Acknowledgments; Introduction; Nomenclature; 1 A visual introduction to quantile regression; Introduction; 1.1 The essential toolkit; 1.1.1 Unconditional mean, unconditional quantiles and surroundings; 1.1.2 Technical insight: Quantiles as solutions of a minimizationproblem; 1.1.3 Conditional mean, conditional quantiles and surroundings; 1.2 The simplest QR model: The case of the dummy regressor; 1.3 A slightly more complex QR model: The case of a nominal regressor 327 $a1.4 A typical QR model: The case of a quantitative regressor1.5 Summary of key points; References; 2 Quantile regression: Understanding how and why; Introduction; 2.1 How and why quantile regression works; 2.1.1 The general linear programming problem; 2.1.2 The linear programming formulation for the QR problem; 2.1.3 Methods for solving the linear programming problem; 2.2 A set of illustrative artificial data; 2.2.1 Homogeneous error models; 2.2.2 Heterogeneous error models; 2.2.3 Dependent data error models; 2.3 How and why to work with QR; 2.3.1 QR for homogeneous and heterogeneous models 327 $a2.3.2 QR prediction intervals2.3.3 A note on the quantile process; 2.4 Summary of key points; References; 3 Estimated coefficients and inference; Introduction; 3.1 Empirical distribution of the quantile regression estimator; 3.1.1 The case of i.i.d. errors; 3.1.2 The case of i.ni.d. errors; 3.1.3 The case of dependent errors; 3.2 Inference in QR, the i.i.d. case; 3.3 Wald, Lagrange multiplier, and likelihood ratio tests; 3.4 Summary of key points; References; 4 Additional tools for the interpretation and evaluation of thequantile regression model; Introduction; 4.1 Data pre-processing 327 $a4.1.1 Explanatory variable transformations4.1.2 Dependent variable transformations; 4.2 Response conditional density estimations; 4.2.1 The case of different scenario simulations; 4.2.2 The case of the response variable reconstruction; 4.3 Validation of the model; 4.3.1 Goodness of fit; 4.3.2 Resampling methods; 4.4 Summary of key points; References; 5 Models with dependent and with non-identically distributed data; Introduction; 5.1 A closer look at the scale parameter, the independent andidentically distributed case; 5.1.1 Estimating the variance of quantile regressions 327 $a5.1.2 Confidence intervals and hypothesis testing on theestimated coefficients5.1.3 Example for the i.i.d. case; 5.2 The non-identically distributed case; 5.2.1 Example for the non-identically distributed case; 5.2.2 Quick ways to test equality of coefficients across quantilesin Stata; 5.2.3 The wage equation revisited; 5.3 The dependent data model; 5.3.1 Example with dependent data; 5.4 Summary of key points; References; Appendix 5.A Heteroskedasticity tests and weighted quantileregression, Stata and R codes 327 $a5.A.1 Koenker and Basset test for heteroskedasticity comparingtwo quantile regressions 330 $a A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological asp 410 0$aWiley Series in Probability and Statistics 606 $aQuantile regression 606 $aRegression analysis 615 0$aQuantile regression. 615 0$aRegression analysis. 676 $a519.5/36 700 $aDavino$b Cristina$0117150 701 $aFurno$b Marilena$f1957-$0103506 701 $aVistocco$b Domenico$0522623 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910807951903321 996 $aQuantile Regression$91469240 997 $aUNINA