Advances in Contemporary Statistics and Econometrics: Festschrift in Honor of Christine Thomas-Agnan

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 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
	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 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
	4 Estimation Results
	5 Predictions
	6 Conclusion
	References
Lagrangian Spatio-Temporal Nonstationary Covariance Functions
	1 Introduction
	2 Lagrangian Spatio-Temporal Covariances
	3 Estimation
		3.1 Thin Plate Splines
		3.2 Maximum Likelihood Estimation and Likelihood Approximations in the Temporal Domain
		3.3 Two-Step Maximum Likelihood Estimation
	4 Simulation Study: Lagrangian Versus Non-Lagrangian Spatio-Temporal Models
		4.1 Second-Order Stationarity
		4.2 Second-Order Nonstationarity
	5 Application to Particulate Matter Data
		5.1 PM 2.5 Data
		5.2 Models
	6 Conclusion
	References
Compositional Data Analysis
Logratio Approach to Distributional Modeling
	1 Introduction
	2 The Bayes Space Embedding for Compositional Vectors
		2.1 An Introduction to Bayes Spaces
		2.2 Statistical Analysis in Bayes Spaces
	3 Implications for Distributional Modeling
		3.1 Linear Regression with Discrete Distributions as Covariates
		3.2 Multivariate Functional Principal Component Analysis When Data Are Density Functions
	4 Case Studies
		4.1 Effect of GDP Components and Causes of Death on Life Expectancy
		4.2 Dimensionality Reduction of Population Pyramids via mSFPCA in Bayes Spaces
	5 Conclusions
	References
A Spatial Durbin Model for Compositional Data
	1 Introduction
	2 Model Specification
		2.1 Preliminaries of Compositional Data
		2.2 The SAR Model and the SDM Model
		2.3 The Spatial Durbin Model (SDM) for Compositional Data
	3 Estimation Method
		3.1 Orthonormal Log-Ratio (olr) Transformation
		3.2 Maximum Likelihood Estimation (MLE) Method
	4 Simulation Study
	5 Real Data Analysis
	6 Conclusion and Discussion
	References
Compositional Analysis of Exchange Rates
	1 Introduction
	2 Preliminaries
	3 Application to a Group of Countries
		3.1 No-Arbitrage Matrix of Exchange Rates and SDR
		3.2 Exchange Rate Bubbles Using the NAMERs
	4 Conclusions
	References
Log-contrast and Orthonormal Log-ratio Coordinates for Compositional Data with a Total
	1 Compositional Analysis and a Typical Linear Combination of Variables
	2 Log-Contrast and Log-Ratio Variables
	3 Log-contrast and Multiplicative Total
	4 Decomposition of a Linear Discriminant Analysis Model
	5 Final Concluding Remarks
	References
Independent Component Analysis for Compositional Data
	1 Introduction
	2 Independent Component Analysis
		2.1 FOBI
		2.2 JADE
		2.3 FastICA
	3 Compositional Data and Its Real Space Representation
	4 ICA for Compositional Data
	5 A Case Study in Metabolomics
	6 Discussion
	References
Diet Quality and Food Sources in Vietnam: First Evidence Using Compositional Data Analysis
	1 Introduction
	2 Methodology
		2.1 Principal Balances
		2.2 Zeros
	3 Data
		3.1 Data Collection
		3.2 Sample Characteristics
		3.3 Nutrition Knowledge
		3.4 Food Sources
		3.5 Diet Quality
	4 Results
		4.1 Exploratory Analysis
		4.2 Regression Analysis
	5 Concluding Remarks
	References
Tools for Empirical Studies in Economics and Social Sciences
Mobility for Study and Professional Integration: An Empirical Overview of the Situation in France Based on the Céreq generational surveys
	1 Introduction
	2 The Data
	3 The Micro-Economic Approach: Identifying the Factors that Determine Mobility Behaviour
		3.1 Constructing Mobility Variables
		3.2 Factors Determining Mobility
		3.3 Empirical Strategy
	4 Results
		4.1 Educational Mobility
		4.2 Employment Mobility
		4.3 Return Mobility
	5 Conclusion
	6 Appendix
	References
Toward a FAIR Reproducible Research
	1 The Need for Reproducible Research
	2 Reproducible Research in Practice
	3 Implementing FAIR and RR Principles in Practice
	4 Confidential Data
	5 Conclusion
	References
``One Man, One Vote'' Part 2: Measurement of Malapportionment and Disproportionality and the Lorenz Curve A: Introduction and Measurement Tools
	1 Introduction
	2 Descriptive Statistics and Measurement  of Malapportionment/Disproportionality
		2.1 Two Settings
		2.2 Mapping Representation into Public Decisions
		2.3 The Lorenz Order
		2.4 Malapportionment and Disproportionality Indices
	References
``One Man, One Vote'' Part 2: Measurement of Malapportionment and Disproportionality and the Lorenz Curve B: Applications
	1 Introduction
	2 The Evolution of the Geographical Lorenz Curve in the ``Assemblée Nationale'' of the French 5th Republic
		2.1 Analysis of the 2017 Election
		2.2 Lorenz Curve
		2.3 Gini Index
		2.4 DK Index
	3 The Evolution of the Ideological Lorenz Curve in the ``Assemblée Nationale'' of the French 5th Republic
		3.1 Lorenz Curve
		3.2 Gini Index
		3.3 DK Index
	4 The Evolution of the Geographical Lorenz Curve in the ``départements'' Before and After the 2015 Electoral Reform
		4.1 Lorenz Curve
		4.2 Gini Index
		4.3 DK Index
	5 Electoral College
		5.1 Lorenz Curve
		5.2 Gini Index
		5.3 DK Index
	References
Visualizing France with Cartograms
	1 Introduction
	2 Cartograms
	3 Housing and Camping
	4 Unemployment
	5 Immigration
	6 Suicides
	7 Elections
	8 Covid-19
	9 Conclusions
	References
Kernel and Dissimilarity Methods for Exploratory Analysis in a Social Context
	1 Introduction
	2 Kernels and More General Proximity Data
		2.1 Kernels and RKHS
		2.2 From General Similarities to Kernels
	3 Basics of Statistical Learning with Kernels
		3.1 Supervised Setting
		3.2 Unsupervised Setting
	4 Kernel Self-Organizing Maps and Complexity Reduction
		4.1 Kernel Self-Organizing Maps
		4.2 Complexity of Kernel SOM
	5 Combining Kernels
	6 Application
		6.1 Three Sets of Dissimilarities and Relations Between Them
		6.2 Results of the Clusterings
		6.3 Concluding Remarks
	References
Of Particles and Molecules: Application of Particle Filtering to Irrigated Agriculture in Punjab, India
	1 Introduction
	2 Estimation of Hidden Markov Models
		2.1 Particle Filtering for Bayesian Non-Linear Filtering
		2.2 Joint Parameter Estimation
	3 The Convolution Particle Filter
	4 Empirical Application
		4.1 Data and Model Specification
		4.2 Estimation Results
	5 Conclusion
	References