LEADER 10893nam 2200505 450 001 9910555112403321 005 20220317125603.0 010 $a1-119-71518-0 010 $a1-119-71516-4 010 $a1-119-71495-8 035 $a(CKB)4100000011974744 035 $a(MiAaPQ)EBC6647272 035 $a(Au-PeEL)EBL6647272 035 $a(OCoLC)1263872610 035 $a(EXLCZ)994100000011974744 100 $a20220317d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuantile regression $eapplications on experimental and cross section data using EViews /$fI. Gusti Ngurah Agung 210 1$aHoboken, NJ :$cJohn Wiley & Sons, Inc.,$d2021. 215 $a1 online resource (499 pages) 311 $a1-119-71517-2 327 $aCover -- Title Page -- Copyright -- Contents -- Preface -- About the Author -- Chapter 1 Test for the Equality of Medians by Series/Group of Variables -- 1.1 Introduction -- 1.2 Test for Equality of Medians of Y1 by Categorical Variables -- 1.3 Test for Equality of Medians of Y1 by Categorical Variables -- 1.4 Testing the Medians of Y1 Categorized by X1 -- 1.5 Testing the Medians of Y1 Categorized by RX1 & -- equals -- @Ranks(X1,a) -- 1.6 Unexpected Statistical Results -- 1.7 Testing the Medians of Y1 by X1 and Categorical Factors -- 1.8 Testing the Medians of Y by Numerical Variables -- 1.8.1 Findings Based on Data& -- uscore -- Faad.wf1 -- 1.8.2 Findings Based on Mlogit.wf1 -- 1.9 Application of the Function @Mediansby(Y,IV) -- Chapter 2 One? and Two?way ANOVA Quantile Regressions -- 2.1 Introduction -- 2.2 One?way ANOVA Quantile Regression -- 2.3 Alternative Two?way ANOVA Quantile Regressions -- 2.3.1 Applications of the Simplest Equation Specification -- 2.3.2 Application of the Quantile Process -- 2.3.3 Applications of the Models with Intercepts -- 2.4 Forecasting -- 2.5 Additive Two?way ANOVA Quantile Regressions -- 2.6 Testing the Quantiles of Y1 Categorized by X1 -- 2.7 Applications of QR on Population Data -- 2.7.1 One?way?ANOVA?QRs -- 2.7.2 Application of the Forecasting -- 2.7.3 Two?way ANOVA?QRs -- 2.8 Special Notes and Comments on Alternative Options -- Chapter 3 N?Way ANOVA Quantile Regressions -- 3.1 Introduction -- 3.2 The Models Without an Intercept -- 3.3 Models with Intercepts -- 3.4 I × J × K Factorial QRs Based on susenas.wf1 -- 3.4.1 Alternative ESs of CWWH on F1, F2, and F3 -- 3.4.1.1 Applications of the Simplest ES in (3.5a) -- 3.4.1.2 Applications of the ES in (3.5b) -- 3.4.1.3 Applications of the ES in (3.5c) -- 3.5 Applications of the N?Way ANOVA?QRs -- 3.5.1 Four?Way ANOVA?QRs. 327 $aChapter 4 Quantile Regressions Based on (X1,Y1) -- 4.1 Introduction -- 4.2 The Simplest Quantile Regression -- 4.3 Polynomial Quantile Regressions -- 4.3.1 Quadratic Quantile Regression -- 4.3.2 Third Degree Polynomial Quantile Regression -- 4.3.3 Forth Degree Polynomial Quantile Regression -- 4.3.4 Fifth Degree Polynomial Quantile Regression -- 4.4 Logarithmic Quantile Regressions -- 4.4.1 The Simplest Semi?Logarithmic QR -- 4.4.2 The Semi?Logarithmic Polynomial QR -- 4.4.2.1 The Basic Semi?Logarithmic Third Degree Polynomial QR -- 4.4.2.2 The Bounded Semi?Logarithmic Third Degree Polynomial QR -- 4.5 QRs Based on MCYCLE.WF1 -- 4.5.1 Scatter Graphs of (MILL,ACCEL) with Fitted Curves -- 4.5.2 Applications of Piecewise Linear QRs -- 4.5.3 Applications of the Quantile Process -- 4.5.4 Alterative Piecewise Linear QRs -- 4.5.5 Applications of Piecewise Quadratic QRs -- 4.5.6 Alternative Piecewise Polynomial QRs -- 4.5.7 Applications of Continuous Polynomial QRs -- 4.5.8 Special Notes and Comments -- 4.6 Quantile Regressions Based on SUSENAS?2013.wf1 -- 4.6.1 Application of CWWH on AGE -- 4.6.1.1 Quantile Regressions of CWWH on AGE -- 4.6.1.2 Application of Logarithmic QRs -- 4.6.2 An Application of Life?Birth on AGE for Ever Married Women -- 4.6.2.1 QR(Median) of LBIRTH on AGE as a Numerical Predictor -- Chapter 5 Quantile Regressions with Two Numerical Predictors -- 5.1 Introduction -- 5.2 Alternative QRs Based on Data& -- uscore -- Faad.wf1 -- 5.2.1 Alternative QRs Based on (X1,X2,Y1) -- 5.2.1.1 Additive QR -- 5.2.1.2 Semi?Logarithmic QR of log(Y1) on X1 and X2 -- 5.2.1.3 Translog QR of log(Y1) on log(X1) and log(X2) -- 5.2.2 Two?Way Interaction QRs -- 5.2.2.1 Interaction QR of Y1 on X1 and X2 -- 5.2.2.2 Semi?Logarithmic Interaction QR Based on (X1,X2,Y1) -- 5.2.2.3 Translogarithmic Interaction QR Based on (X1,X2,Y1). 327 $a5.3 An Analysis Based on Mlogit.wf1 -- 5.3.1 Alternative QRs of LW -- 5.3.2 Alternative QRs of INC -- 5.3.2.1 Using Z?Scores Variables as Predictors -- 5.3.2.2 Alternative QRs of INC on Other Sets of Numerical Predictors -- 5.3.2.3 Alternative QRs Based on Other Sets of Numerical Variables -- 5.4 Polynomial Two?Way Interaction QRs -- 5.5 Double Polynomial QRs -- 5.5.1 Additive Double Polynomial QRs -- 5.5.2 Interaction Double Polynomial QRs -- Chapter 6 Quantile Regressions with Multiple Numerical Predictors -- 6.1 Introduction -- 6.2 Alternative Path Diagrams Based on (X1,X2,X3,Y1) -- 6.2.1 A QR Based on the Path Diagram in Figure a -- 6.2.2 A QR Based on the Path Diagram in Figure b -- 6.2.3 QR Based on the Path Diagram in Figure c -- 6.2.3.1 A Full Two?Way Interaction QR -- 6.2.3.2 A Full Three?Way Interaction QR -- 6.2.4 QR Based on the Path Diagram in Figure d -- 6.3 Applications of QRs Based on Data& -- uscore -- Faad.wf1 -- 6.4 Applications of QRs Based on Data in Mlogit.wf1 -- 6.5 QRs of PR1 on (DIST1,X1,X2) -- 6.6 Advanced Statistical Analysis -- 6.6.1 Applications of the Quantiles Process -- 6.6.1.1 An Application of the Process Coefficients -- 6.6.1.2 An Application of the Quantile Slope Equality Test -- 6.6.1.3 An Application of the Symmetric Quantiles Test -- 6.6.2 An Application of the Ramsey RESET Test -- 6.6.3 Residual Diagnostics -- 6.7 Forecasting -- 6.7.1 Basic Forecasting -- 6.7.2 Advanced Forecasting -- 6.8 Developing a Complete Data& -- uscore -- LW.wf1 -- 6.9 QRs with Four Numerical Predictors -- 6.9.1 An Additive QR -- 6.9.2 Alternative Two?Way Interaction QRs -- 6.9.2.1 A Two?Way Interaction QR Based on Figure a -- 6.9.2.2 A Two?Way Interaction QR Based on Figure b -- 6.9.2.3 A Two?Way Interaction QR Based on Figure c -- 6.9.2.4 A Two?Way Interaction QR Based on Figure d -- 6.9.3 Alternative Three?Way Interaction QRs. 327 $a6.9.3.1 Alternative Models Based on Figure a -- 6.9.3.2 Alternative Models Based on Figure b -- 6.9.3.3 Alternative Models Based on Figure c -- 6.9.3.4 Alternative Models Based on Figure d -- 6.10 QRs with Multiple Numerical Predictors -- 6.10.1 Developing an Additive QR -- 6.10.2 Developing a Simple Two?Way Interaction QR -- 6.10.3 Developing a Simple Three?Way Interaction QR -- Chapter 7 Quantile Regressions with the Ranks of Numerical Predictors -- 7.1 Introduction -- 7.2 NPQRs Based on a Single Rank Predictor -- 7.2.1 Alternative Piecewise NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.2 Polynomial NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.3 Special Notes and Comments -- 7.3 NPQRs on Group of R& -- uscore -- Milli -- 7.3.1 An Application of the G& -- uscore -- Milli as a Categorical Variable -- 7.3.2 The kth?Degree Polynomial NPQRs of ACCEL on G& -- uscore -- Milli -- 7.4 Multiple NPQRs Based on Data?Faad.wf1 -- 7.4.1 An NPQR Based on a Triple Numerical Variable (X1,X2,Y) -- 7.4.2 NPQRs with Multi?Rank Predictors -- 7.5 Multiple NPQRs Based on MLogit.wf1 -- Chapter 8 Heterogeneous Quantile Regressions Based on Experimental Data -- 8.1 Introduction -- 8.2 HQRs of Y1 on X1 by a Cell?Factor -- 8.2.1 The Simplest HQR -- 8.2.2 A Piecewise Quadratic QR -- 8.2.3 A Piecewise Polynomial Quantile Regression -- 8.3 HLQR of Y1 on (X1,X2) by the Cell?Factor -- 8.3.1 Additive HLQR of Y1 on (X1,X2) by CF -- 8.3.2 A Two?Way Interaction Heterogeneous?QR of Y1 on (X1,X2) by CF -- 8.3.3 An Application of Translog?Linear QR of Y1 on (X1,X2) by CF -- 8.4 The HLQR of Y1 on (X1,X2,X3) by a Cell?Factor -- 8.4.1 An Additive HLQR of Y1 on (X1,X2,X3) by CF -- 8.4.2 A Full Two?Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- 8.4.3 A Full Three?Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- Chapter 9 Quantile Regressions Based on CPS88.wf1. 327 $a9.1 Introduction -- 9.2 Applications of an ANOVA Quantile Regression -- 9.2.1 One?Way ANOVA?QR -- 9.2.2 Two?Way ANOVA Quantile Regression -- 9.2.2.1 The Simplest Equation of Two?Way ANOVA?QR -- 9.2.2.2 A Special Equation of the Two?Way ANOVA?QR -- 9.2.2.3 An Additive Two?Way ANOVA?QR -- 9.2.3 Three?Way ANOVA?QRs -- 9.3 Quantile Regressions with Numerical Predictors -- 9.3.1 QR of LWAGE on GRADE -- 9.3.1.1 A Polynomial QR of LWAGE on GRADE -- 9.3.1.2 The Simplest Linear QR of Y1 on a Numerical X1 -- 9.3.2 Quantile Regressions of Y1 on (X1,X2) -- 9.3.2.1 Hierarchical and Nonhierarchical Two?Way Interaction QRs -- 9.3.2.2 A Special Polynomial Interaction QR -- 9.3.2.3 A Double Polynomial Interaction QR of Y1 on (X1,X2) -- 9.3.3 QRs of Y1 on Numerical Variables (X1,X2,X3) -- 9.3.3.1 A Full Two?Way Interaction QR -- 9.3.3.2 A Full?Three?Way?Interaction QR -- 9.4 Heterogeneous Quantile?Regressions -- 9.4.1 Heterogeneous Quantile Regressions by a Factor -- 9.4.1.1 A Heterogeneous Linear QR of LWAGE on POTEXP by IND1 -- 9.4.1.2 A Heterogeneous Third?Degree Polynomial QR of LWAGE on GRADE -- 9.4.1.3 An Application of QR for a Large Number of Groups -- 9.4.1.4 Comparison Between Selected Heterogeneous QR(Median) -- Chapter 10 Quantile Regressions of a Latent Variable -- 10.1 Introduction -- 10.2 Spearman?rank Correlation -- 10.3 Applications of ANOVA?QR(?) -- 10.3.1 One?way ANOVA?QR of BLV -- 10.3.2 A Two?Way ANOVA?QR of BLV -- 10.3.2.1 The Simplest Equation of a Two?Way ANOVA?QR of BLV -- 10.3.2.2 A Two?way ANOVA?QR of BLV with an Intercept -- 10.3.2.3 A Special Equation of Two?Way ANOVA?QR of BLV -- 10.4 Three?way ANOVA?QR of BLV -- 10.5 QRs of BLV on Numerical Predictors -- 10.5.1 QRs of BLV on MW -- 10.5.1.1 The Simplest Linear Regression of BLV on MW -- 10.5.1.2 Polynomial Regression of BLV on MW -- 10.5.2 QRs of BLV on Two Numerical Predictors. 327 $a10.5.2.1 An Additive QR of BLV. 606 $aQuantile regression 606 $aMathematical statistics 608 $aElectronic books. 615 0$aQuantile regression. 615 0$aMathematical statistics. 676 $a519.536 700 $aAgung$b I Gusti Ngurah$0614603 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910555112403321 996 $aQuantile regression$91884494 997 $aUNINA