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Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung
Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung
Autore Agung I Gusti Ngurah
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
Descrizione fisica 1 online resource (499 pages)
Disciplina 519.536
Soggetto topico Quantile regression
Mathematical statistics
Soggetto genere / forma Electronic books.
ISBN 1-119-71518-0
1-119-71516-4
1-119-71495-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- 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.
Chapter 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).
5.3 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.
6.9.3.1 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.
9.1 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(τ) -- 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.
10.5.2.1 An Additive QR of BLV.
Record Nr. UNINA-9910555112403321
Agung I Gusti Ngurah  
Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung
Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung
Autore Agung I Gusti Ngurah
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
Descrizione fisica 1 online resource (499 pages)
Disciplina 519.536
Soggetto topico Quantile regression
Mathematical statistics
ISBN 1-119-71518-0
1-119-71516-4
1-119-71495-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- 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.
Chapter 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).
5.3 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.
6.9.3.1 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.
9.1 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(τ) -- 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.
10.5.2.1 An Additive QR of BLV.
Record Nr. UNINA-9910829888103321
Agung I Gusti Ngurah  
Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco
Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco
Autore Davino Cristina
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2014
Descrizione fisica 1 online resource (290 p.)
Disciplina 519.5/36
Altri autori (Persone) FurnoMarilena <1957->
VistoccoDomenico
Collana Wiley series in probability and statistics
Soggetto topico Quantile regression
Regression analysis
ISBN 1-118-75271-6
1-118-75268-6
1-118-75319-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Quantile 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
1.4 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
2.3.2 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
4.1.1 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
5.1.2 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
5.A.1 Koenker and Basset test for heteroskedasticity comparingtwo quantile regressions
Record Nr. UNINA-9910138993403321
Davino Cristina  
Chichester, England : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco
Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco
Autore Davino Cristina
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2014
Descrizione fisica 1 online resource (290 p.)
Disciplina 519.5/36
Altri autori (Persone) FurnoMarilena <1957->
VistoccoDomenico
Collana Wiley series in probability and statistics
Soggetto topico Quantile regression
Regression analysis
ISBN 1-118-75271-6
1-118-75268-6
1-118-75319-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Quantile 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
1.4 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
2.3.2 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
4.1.1 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
5.1.2 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
5.A.1 Koenker and Basset test for heteroskedasticity comparingtwo quantile regressions
Record Nr. UNINA-9910807951903321
Davino Cristina  
Chichester, England : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantile regression : theory and applications, volume 2 / / Marilena Furno, Domenico Vistocco
Quantile regression : theory and applications, volume 2 / / Marilena Furno, Domenico Vistocco
Autore Furno Marilena
Pubbl/distr/stampa Chichester, West Sussex, UK : , : Wiley, , 2018
Descrizione fisica 1 online resource (310 pages) : illustrations
Disciplina 519.5/36
Collana Wiley series in probability and statistics
Soggetto topico Regression analysis
Quantile regression
ISBN 1-118-86364-X
1-118-86360-7
1-118-86371-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto [volume 1]. Theory and applications -- volume 2. Estimation and simulation.
Record Nr. UNINA-9910555014703321
Furno Marilena  
Chichester, West Sussex, UK : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantile regression : theory and applications, volume 2 / / Marilena Furno, Domenico Vistocco
Quantile regression : theory and applications, volume 2 / / Marilena Furno, Domenico Vistocco
Autore Furno Marilena
Pubbl/distr/stampa Chichester, West Sussex, UK : , : Wiley, , 2018
Descrizione fisica 1 online resource (310 pages) : illustrations
Disciplina 519.5/36
Collana Wiley series in probability and statistics
Soggetto topico Regression analysis
Quantile regression
ISBN 1-118-86364-X
1-118-86360-7
1-118-86371-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto [volume 1]. Theory and applications -- volume 2. Estimation and simulation.
Record Nr. UNINA-9910824473903321
Furno Marilena  
Chichester, West Sussex, UK : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantile regression in clinical research : complete analysis for data at a loss of homogeneity / / Ton J. Cleophas, Aeilko H. Zwinderman
Quantile regression in clinical research : complete analysis for data at a loss of homogeneity / / Ton J. Cleophas, Aeilko H. Zwinderman
Autore Cleophas Ton J.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (283 pages)
Disciplina 610.727
Soggetto topico Clinical medicine - Research
Quantile regression
Medicina clínica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-82840-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520075303321
Cleophas Ton J.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui