Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advances in contemporary statistics and econometrics : Festschrift in honor of Christine Thomas-Agnan / / Abdelaati Daouia, Anne Ruiz-Gazen, editors
| Advances in contemporary statistics and econometrics : Festschrift in honor of Christine Thomas-Agnan / / Abdelaati Daouia, Anne Ruiz-Gazen, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (713 pages) |
| Disciplina | 330.015195 |
| Soggetto topico |
Econometrics
Econometria |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73249-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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. |
| Record Nr. | UNINA-9910485587103321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in contemporary statistics and econometrics : Festschrift in honor of Christine Thomas-Agnan / / Abdelaati Daouia, Anne Ruiz-Gazen, editors
| Advances in contemporary statistics and econometrics : Festschrift in honor of Christine Thomas-Agnan / / Abdelaati Daouia, Anne Ruiz-Gazen, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (713 pages) |
| Disciplina | 330.015195 |
| Soggetto topico |
Econometrics
Econometria |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73249-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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. |
| Record Nr. | UNISA-996466398703316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Studies in Theoretical and Applied Statistics : SIS 2016, Salerno, Italy, June 8-10 / / edited by Cira Perna, Monica Pratesi, Anne Ruiz-Gazen
| Studies in Theoretical and Applied Statistics : SIS 2016, Salerno, Italy, June 8-10 / / edited by Cira Perna, Monica Pratesi, Anne Ruiz-Gazen |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (335 pages) |
| Disciplina | 519.5 |
| Collana | Springer Proceedings in Mathematics & Statistics |
| Soggetto topico |
Statistics
Statistical Theory and Methods Statistics for Business, Management, Economics, Finance, Insurance Statistics and Computing/Statistics Programs Statistics for Social Sciences, Humanities, Law |
| ISBN | 3-319-73906-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 C. Favre-Martinoz et al., Robustness in survey sampling using the conditional bias approach with R implementation -- 2 F. Andreis et al., Methodological perspectives for surveying rare and clustered population: towards a sequentially adaptive approach -- 3 M. L. Aversa et al., Age management in Italian companies. Findings from two INAPP surveys -- 4 M. Calzaroni et al., Generating high quality administrative data: new technologies in a national statistical reuse perspective -- 5 T. Tuoto et al., Exploring solutions for linking Big Data in Official Statistics -- 6 C. Franceschini and N. Loperfido, An Algorithm for Finding Projections with Extreme Kurtosis -- 7 L. Egidi et al., Maxima Units Search (MUS) algorithm: methodology and applications -- 8 D. Passaretti and D. Vistocco, DESPOTA: an algorithm to detect the partition in the extended hierarchy of a dendrogram -- 9 F. Pauli, The p-value case, a review of the debate: issues and plausible remedies -- 10 J. Koskinen et al., A dynamic discrete-choice model for movement flows -- 11 G. Ragozini et al., On the Analysis of Time-Varying Affiliation Networks: the Case of Stage Co-productions -- 12 M. Ichino and K. Umbleja, Similarity and Dissimilarity Measures for Mixed Feature-type Symbolic Data -- 13 L. D'Ambra et al., Dimensionality reduction methods for contingency tables with ordinal variables -- 14 R. Gerlach and G. Storti, Extended Realized GARCH models -- 15 L. Crosato and B. Zavanella, Updating CPI weights through compositional VAR forecasts: an application to the Italian index -- 16 P. Chirico, Prediction intervals for heteroscedastic series by Holt-Winters methods -- 17 F. Benassi et al., Measuring residential segregation of selected foreign groups with aspatial and spatial evenness indices. A case study -- 18 G. Adelfio et al., Space-time FPCA clustering of multidimensional curves -- 19 D. Rocchini et al., The power of generalized entropy for biodiversity assessment by remote sensing: an open source approach -- 20 A. Lepore et al., An empirical approach to monitoring ship CO^2 emissions via Partial Least-Squares regression -- 21 A. Valentini et al., Promoting statistical literacy to university students: a new approach adopted by Istat -- 22 M. Enea, From South to North? Mobility of Southern Italian students at the transition from the first to the second level university degree -- 23 G. Leckie and H. Goldstein, Monitoring school performance using value-added and value-table models: Lessons from the UK -- 24 G. D'Epifanio, Indexing the Normalized Worthiness of Social Agents -- 25 E. Baldacci, Financial Crises and their Impacts: Data Gaps and Innovation in Statistical Production -- 26 A. Coli and B. Pacini, European welfare systems in official statistics: the national and local levels -- 27 M. Costa, Financial variables analysis by inequality decomposition -- 28 E. Grimaccia and T. Rondinella, A novel perspective in the analysis of sustainability, inclusion and smartness of growth through Europe 2020 indicators -- 29 I. Mingo et al., The Italian population behaviours toward environmental sustainability: a study from Istat surveys -- 30 C. Giusti and S. Marchetti, Estimating the at risk of poverty rate before and after social transfers at provincial level in Italy. |
| Record Nr. | UNINA-9910300122903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||