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[null,null,"Titolo",null,"Advances in Contemporary Statistics and Econometrics : Festschrift in Honor of Christine Thomas-Agnan / / edited by Abdelaati Daouia, Anne Ruiz-Gazen"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-73249-5"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (713 pages)"]
[null,null,"Disciplina",null,"330.015195"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Econometrics"]
[{"$nulls":4},"Social sciences - Statistical methods"]
[{"$nulls":4},"Statistical Theory and Methods"]
[{"$nulls":4},"Statistics in Business, Management, Economics, Finance, Insurance"]
[{"$nulls":4},"Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- Contributors -- Nonparametric Statistics and Econometrics -- Profile Least Squares Estimators in the Monotone Single Index Model -- 1 Introduction -- 2 General Conditions and the Functions n, and ψ -- 3 The Limit Theory for the SSE -- 4 The Limit Theory for ESE and Cubic Spline Estimator -- 5 Simulation and Comparisons with Other Estimators -- 6 Concluding Remarks -- References -- Optimization by Gradient Boosting -- 1 Introduction -- 2 Gradient Boosting -- 2.1 Mathematical Context -- 2.2 Some Examples -- 2.3 Two Algorithms -- 3 Convergence of the Algorithms -- 3.1 Algorithm 1 -- 3.2 Algorithm 2 -- 4 Large Sample Properties -- References -- Nonparametric Model-Based Estimators for the Cumulative Distribution Function of a Right Censored Variable in a Small Area -- 1 Introduction -- 2 Estimation of the Cdf of a Censored Variable in a Small Area -- 2.1 Framework -- 2.2 Direct Estimators -- 2.3 The New Small Area Estimator -- 3 Model-Based Simulations -- 3.1 Description -- 3.2 Results -- 4 Example -- 5 Concluding Remarks -- References -- Relaxing Monotonicity in Endogenous Selection Models and Application to Surveys -- 1 "]
[{"$nulls":4},"Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Baseline Setup -- 2.3 NMAR Missing Data -- 3 Models with One Unobservable in the 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."]
[{"$nulls":4},"3 B-Spline Model-Assisted Estimator for Complex Parameters -- 4 B-Spline Imputation for Handling Item Nonresponse -- References -- Computational Outlier Detection Methods in Sliced Inverse Regression -- 1 Introduction -- 2 A Brief Review on Usual SIR -- 3 Outlier Detection Methods in SIR -- 3.1 A Naive Method -- 3.2 TTR Method -- 3.3 BOOT Method -- 4 A Numerical Example -- 4.1 Description of the Simulated Dataset -- 4.2 Numerical Results -- 5 Simulation Results -- 6 A Real Data Application -- 7 Concluding Remarks and Extensions -- References -- Uncoupled Isotonic Regression with Discrete Errors -- 1 Introduction -- 2 Estimation in Uncoupled Regression with Discrete Errors -- 3 Comparison with Coupled Isotonic Regression -- 4 Additional Proofs -- References -- Quantiles and Expectiles -- Partially Linear Expectile Regression Using Local Polynomial Fitting -- 1 Introduction -- 2 Partially Linear Expectile Regression -- 3 Statistical Estimation Methodology -- 3.1 Estimation of the Vector of Regression Coefficients -- 3.2 Estimation of the Nonparametric Part -- 4 Asymptotic Properties and Bandwidth Selection -- 4.1 Optimal Theoretical Bandwidth (Matrix) -- 4.2 Rule-of-Thumb (ROT) Bandwidth Selector -- 5 Simulation Study -- 5.1 Simulation Results for Model 1 -- 5.2 Simulation Results for Model 2 -- 6 Real Data Application -- 7 Further Reading -- References -- Piecewise Linear Continuous Estimators of the Quantile Function -- 1 Introduction -- 2 The Piecewise Quantile Estimators -- 2.1 Definition -- 2.2 First Properties -- 2.3 Mean Integrated Squared Error -- 3 Discussion -- Appendix -- References -- Single-Index Quantile Regression Models for Censored Data -- 1 Introduction -- 2 Model and Estimation -- 3 Asymptotic Results -- 4 Bandwidth Selection -- 5 Numerical Results -- 6 Case Study -- References -- Extreme Lp-quantile Kernel Regression."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Analysis of Maximum Likelihood Estimation of Covariance Parameters for Gaussian Processes: An Introduction with Proofs -- 1 Introduction -- 2 Framework and Notations -- 2.1 Gaussian Processes and Covariance Functions -- 2.2 Classical Families of Covariance Functions -- 2.3 Maximum Likelihood -- 3 Increasing-Domain Asymptotics -- 3.1 Consistency -- 3.2 Asymptotic Normality -- 4 Fixed-Domain Asymptotics -- 4.1 What Changes -- 4.2 Microergodic and Non-microergodic Parameters."]
[{"$nulls":4},"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."]
[{"$nulls":4},"Modeling Dependence in Spatio-Temporal Econometrics -- 1 Introduction -- 2 Spatio-Temporal Statistics -- 2.1 Uncertainty and Data -- 2.2 Uncertainty and Models -- 2.3 Conditional Probabilities in a Hierarchical Statistical Model (HM) -- 2.4 ``Classical'' Statistical Modeling -- 3 Spatio-Temporal-Econometric Modeling -- 3.1 Spatial Description and Temporal Dynamics: A Simple Example -- 3.2 Time Series of Spatial Processes -- 3.3 Space-Time Autoregressive Moving Average (STARMA) Models -- 4 Spatial-Econometric Modeling -- 5 Modern Spatio-Temporal-Econometric Hierarchical Models -- 6 Concluding Remarks -- References -- Guidelines on Areal Interpolation Methods -- 1 Introduction -- 1.1 Motivation -- 1.2 Context -- 2 Notations -- 3 Data -- 3.1 Target Zones -- 3.2 First Source Scale: The Cells -- 3.3 Second Source Scale: The Iris -- 3.4 Variables to Estimate -- 4 Point-in-Polygon Method -- 4.1 Extensive Variables -- 4.2 Intensive Variables -- 4.3 Limitation of the Point-in Polygon Method -- 5 Areal Weighting Interpolation Method -- 5.1 Extensive Variable -- 5.2 Intensive Variable -- 6 Dasymetric Method with Auxiliary Variable X -- 6.1 Extensive Variables -- 6.2 Intensive Variables -- 7 Dasymetric Method with Control Zones -- 7.1 Presentation of the Method -- 7.2 Comparison Between DAC and DAX -- 8 Regression Modelling -- 8.1 Covariates and Exploratory Analysis -- 8.2 Linear Modelling -- 8.3 Regression Tree -- References -- Predictions in Spatial Econometric "]
[{"$nulls":4},"Models: Application to Unemployment Data -- 1 Introduction -- 1.1 Related Literature -- 2 Notation, Models, and Prediction Formula -- 2.1 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."]
[{"$nulls":4},"3.2 Data and Definition of Neighborhood Structure."]
[null,null,null,"Sommario/riassunto","This book presents a unique collection of contributions on modern topics in statistics and econometrics, written by leading experts in the respective disciplines and their intersections. It addresses nonparametric statistics and econometrics, quantiles and expectiles, and advanced methods for complex data, including spatial and compositional data, as well as tools for empirical studies in economics and the social sciences. The book was written in honor of Christine Thomas-Agnan on the occasion of her 65th birthday. Given its scope, it will appeal to researchers and PhD students in statistics and econometrics alike who are interested in the latest developments in their field."]