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Record Nr. |
UNINA9910485587103321 |
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Titolo |
Advances in contemporary statistics and econometrics : Festschrift in honor of Christine Thomas-Agnan / / Abdelaati Daouia, Anne Ruiz-Gazen, editors |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (713 pages) |
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Disciplina |
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Soggetti |
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Econometrics |
Econometria |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- Contributors -- Nonparametric Statistics and Econometrics -- Profile Least Squares Estimators in the Monotone Single Index Model -- 1 Introduction -- 2 General Conditions and the Functions n, and ψ -- 3 The Limit Theory for the SSE -- 4 The Limit Theory for ESE and Cubic Spline Estimator -- 5 Simulation and Comparisons with Other Estimators -- 6 Concluding Remarks -- References -- Optimization by Gradient Boosting -- 1 Introduction -- 2 Gradient Boosting -- 2.1 Mathematical Context -- 2.2 Some Examples -- 2.3 Two Algorithms -- 3 Convergence of the Algorithms -- 3.1 Algorithm 1 -- 3.2 Algorithm 2 -- 4 Large Sample Properties -- References -- Nonparametric Model-Based Estimators for the Cumulative Distribution Function of a Right Censored Variable in a Small Area -- 1 Introduction -- 2 Estimation of the Cdf of a Censored Variable in a Small Area -- 2.1 Framework -- 2.2 Direct Estimators -- 2.3 The New Small Area Estimator -- 3 Model-Based Simulations -- 3.1 Description -- 3.2 Results -- 4 Example -- 5 Concluding Remarks -- References -- Relaxing Monotonicity in Endogenous Selection Models and Application to Surveys -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Baseline Setup -- 2.3 NMAR Missing Data -- 3 Models with One Unobservable in the |
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Endogenous Selection -- 4 Monotonicity -- 5 A Random Coefficients Model for the Selection Equation -- 5.1 Scaling to Handle Genuine Non Instrument Monotonicity -- 5.2 Alternative Scaling Under a Weak Version of Monotonicity -- 6 Application to Missing Data in Surveys -- References -- B-Spline Estimation in a Survey Sampling Framework -- 1 Introduction -- 2 B-Spline Model-Assisted Estimator for Finite Population Totals -- 2.1 B-Spline Model-Assisted Estimation -- 2.2 B-Spline Calibration Estimator. |
3 B-Spline Model-Assisted Estimator for Complex Parameters -- 4 B-Spline Imputation for Handling Item Nonresponse -- References -- Computational Outlier Detection Methods in Sliced Inverse Regression -- 1 Introduction -- 2 A Brief Review on Usual SIR -- 3 Outlier Detection Methods in SIR -- 3.1 A Naive Method -- 3.2 TTR Method -- 3.3 BOOT Method -- 4 A Numerical Example -- 4.1 Description of the Simulated Dataset -- 4.2 Numerical Results -- 5 Simulation Results -- 6 A Real Data Application -- 7 Concluding Remarks and Extensions -- References -- Uncoupled Isotonic Regression with Discrete Errors -- 1 Introduction -- 2 Estimation in Uncoupled Regression with Discrete Errors -- 3 Comparison with Coupled Isotonic Regression -- 4 Additional Proofs -- References -- Quantiles and Expectiles -- Partially Linear Expectile Regression Using Local Polynomial Fitting -- 1 Introduction -- 2 Partially Linear Expectile Regression -- 3 Statistical Estimation Methodology -- 3.1 Estimation of the Vector of Regression Coefficients -- 3.2 Estimation of the Nonparametric Part -- 4 Asymptotic Properties and Bandwidth Selection -- 4.1 Optimal Theoretical Bandwidth (Matrix) -- 4.2 Rule-of-Thumb (ROT) Bandwidth Selector -- 5 Simulation Study -- 5.1 Simulation Results for Model 1 -- 5.2 Simulation Results for Model 2 -- 6 Real Data Application -- 7 Further Reading -- References -- Piecewise Linear Continuous Estimators of the Quantile Function -- 1 Introduction -- 2 The Piecewise Quantile Estimators -- 2.1 Definition -- 2.2 First Properties -- 2.3 Mean Integrated Squared Error -- 3 Discussion -- Appendix -- References -- Single-Index Quantile Regression Models for Censored Data -- 1 Introduction -- 2 Model and Estimation -- 3 Asymptotic Results -- 4 Bandwidth Selection -- 5 Numerical Results -- 6 Case Study -- References -- Extreme Lp-quantile Kernel Regression. |
1 Introduction -- 2 Lp-quantile Kernel Regression -- 3 Main Results -- 3.1 Intermediate Lp-quantile Regression -- 3.2 Extreme Lp-quantile Regression -- 3.3 Lp-quantile-Based Estimation of the Conditional Tail Index -- 4 Simulation Study -- 5 Real Data Example -- 6 Appendix -- 6.1 Preliminary Results -- 6.2 Proofs of Main Results -- References -- Robust Efficiency Analysis of Public Hospitals in Queensland, Australia -- 1 Introduction -- 2 Methodology -- 2.1 Theoretical Concepts -- 2.2 Nonparametric Estimators -- 3 Variables and Data -- 4 Results and Discussions -- 4.1 Univariate Input-Output Illustration -- 4.2 Main Analysis: Multiple Inputs Case -- 5 Concluding Remarks -- References -- On the Behavior of Extreme d-dimensional Spatial Quantiles Under Minimal Assumptions -- 1 Introduction -- 2 Results -- 3 Proofs -- References -- Modelling Flow in Gas Transmission Networks Using Shape-Constrained Expectile Regression -- 1 Introduction -- 2 Description of Data and Motivation -- 2.1 Data -- 2.2 Previous Models and Advantages of the New Approach -- 3 Methods -- 3.1 Geoadditive Regression Models -- 3.2 Shape-Constrained P-splines -- 3.3 Semiparametric Expectile Regression -- 4 Estimating and Forecasting Gas Flow -- 4.1 Results -- 4.2 Risk Analysis -- 5 Conclusion -- References -- Spatial Statistics and Econometrics -- Asymptotic Analysis of Maximum Likelihood Estimation of Covariance Parameters for Gaussian Processes: An Introduction with Proofs -- 1 Introduction |
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-- 2 Framework and Notations -- 2.1 Gaussian Processes and Covariance Functions -- 2.2 Classical Families of Covariance Functions -- 2.3 Maximum Likelihood -- 3 Increasing-Domain Asymptotics -- 3.1 Consistency -- 3.2 Asymptotic Normality -- 4 Fixed-Domain Asymptotics -- 4.1 What Changes -- 4.2 Microergodic and Non-microergodic Parameters. |
4.3 Consistent Estimation of the Microergodic Parameter of the Isotropic Matérn Model -- 5 Conclusion -- References -- Global Scan Methods for Comparing Two Spatial Point Processes -- 1 Introduction -- 2 Methodology -- 2.1 Spatial Scan Statistics for Bivariate Data -- 2.2 Significance Issues -- 3 Applications -- 3.1 Simulation Study -- 3.2 Forest Fire Occurrences -- 4 Discussion -- References -- Assessing Spillover Effects of Spatial Policies with Semiparametric Zero-Inflated Models and Random Forests -- 1 Introduction -- 2 Conditional Average Treatment Effect, Identification and Model Specification -- 2.1 Identification Issues and Conditional Independence Assumption -- 2.2 Zero Inflation and Conditional Mixtures -- 3 Econometric Modeling and Estimation Procedures -- 3.1 A Flexible Semi-parametric Modeling Approach Based on Additive Models and Conditional Mixtures -- 3.2 Estimation of the Conditional Treatment Effect with Random Forests -- 4 An Illustration on the Estimation of the Effect of Local Development Policies in France -- 4.1 Description of the Policy and Data -- 4.2 Estimation Results and Counterfactual Analysis at the Municipality Level -- 5 Conclusion -- References -- Spatial Autocorrelation in Econometric Land Use Models: An Overview -- 1 Introduction -- 2 Econometric Land Use Models -- 3 Linear Land Use Models -- 3.1 Land Use Share Models -- 3.2 Spatial Autocorrelation in Linear Models -- 3.3 Example of Spatial Land Studies with Linear Models -- 4 Discrete Choice Land Use Models -- 4.1 Individual Choice Land Use Model -- 4.2 Spatial Autocorrelation in Discrete Choice Models -- 4.3 Examples of Spatial Land Use Studies with Discrete Choice Models -- 5 Land Use and Its Impacts on the Environment -- 5.1 Land Use and ES -- 5.2 Land Use and Water Quality -- 5.3 Land Use and Climate Change -- 6 Conclusion -- References. |
Modeling Dependence in Spatio-Temporal Econometrics -- 1 Introduction -- 2 Spatio-Temporal Statistics -- 2.1 Uncertainty and Data -- 2.2 Uncertainty and Models -- 2.3 Conditional Probabilities in a Hierarchical Statistical Model (HM) -- 2.4 ``Classical'' Statistical Modeling -- 3 Spatio-Temporal-Econometric Modeling -- 3.1 Spatial Description and Temporal Dynamics: A Simple Example -- 3.2 Time Series of Spatial Processes -- 3.3 Space-Time Autoregressive Moving Average (STARMA) Models -- 4 Spatial-Econometric Modeling -- 5 Modern Spatio-Temporal-Econometric Hierarchical Models -- 6 Concluding Remarks -- References -- Guidelines on Areal Interpolation Methods -- 1 Introduction -- 1.1 Motivation -- 1.2 Context -- 2 Notations -- 3 Data -- 3.1 Target Zones -- 3.2 First Source Scale: The Cells -- 3.3 Second Source Scale: The Iris -- 3.4 Variables to Estimate -- 4 Point-in-Polygon Method -- 4.1 Extensive Variables -- 4.2 Intensive Variables -- 4.3 Limitation of the Point-in Polygon Method -- 5 Areal Weighting Interpolation Method -- 5.1 Extensive Variable -- 5.2 Intensive Variable -- 6 Dasymetric Method with Auxiliary Variable X -- 6.1 Extensive Variables -- 6.2 Intensive Variables -- 7 Dasymetric Method with Control Zones -- 7.1 Presentation of the Method -- 7.2 Comparison Between DAC and DAX -- 8 Regression Modelling -- 8.1 Covariates and Exploratory Analysis -- 8.2 Linear Modelling -- 8.3 Regression Tree -- References -- Predictions in Spatial Econometric Models: Application to Unemployment Data -- 1 Introduction -- 1.1 Related Literature -- 2 Notation, Models, and Prediction Formula -- 2.1 |
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Notation and the Spatial Autoregressive Durbin Model -- 2.2 In-Sample and Out-of-Sample Units -- 2.3 In-Sample Prediction Formulas -- 2.4 Out-of-Sample Prediction Formulas -- 3 Application -- 3.1 Theoretical Explanations for Regional Unemployment Differentials. |
3.2 Data and Definition of Neighborhood Structure. |
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