Results
Articles
Amor Keziou, Aida Toma (2021) A Robust Version of the Empirical Likelihood Estimator, Mathematics, 9(8), 829. https://doi.org/10.3390/math9080829
Amor Keziou, Aida Toma (2021) Robust Empirical Likelihood. In: Nielsen F., Barbaresco F. (eds) Geometric Science of Information. Lecture Notes in Computer Science, vol 12829, 841-848. Springer. https://doi.org/10.1007/978-3-030-80209-7_90
Marie du Roy de Chaumaray, Matthieu Marbac, Valentin Patilea, (2021) Wilks’ Theorem for Semiparametric Regressions with Weakly Dependent Data, The Annals of Statistics, 42, 6, 3228-3254.
Muhammad Sheraz, Imran Nasir, Silvia Dedu (2021) Extreme value analysis and risk assessment: a case of Pakistan Stock Market, Economic Computation and Economic Studies and Research, 55(3), 5-20.
Yves Berger, Valentin Patilea, (2022) A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection, Econometrics and Statistics, 24, 151-163.
Amor Keziou, Aida Toma, (2022) Robust minimum empirical divergence estimators for moment condition models, under revision.
Daniel Becker, Alois Kneip, Valentin Patilea, (2022) Semiparametric Inference for Partially Linear Regressions with Box-Cox Transformation, https://arxiv.org/pdf/2106.10723.pdf, submitted.
Muhammad Sheraz, Silvia Dedu, Vasile Preda, (2022) Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data, Entropy, 24, 10, 1410.
Vasile Preda, Silvia Dedu, Iuliana Iatan, Ioana Dănilă Cernat, Muhammad Sheraz, (2022) Tsallis entropy for loss models and survival models involving truncated and censored random variables, Entropy, 24, 11, 1654.
Imran Nasir, Muhammad Sheraz, Silvia Dedu, (2022) Mixture models and modelling volatility of returns – A study on gaussian and heterogeneous heavy tail mixtures, Journal Economic Computation and Economic Cybernetics Studies and Research, 56, 4, 5-20.
Aida Toma, (2023) Robust Z-estimators for semiparametric moment condition models, Entropy, 25, 7, 1013.
Valentin Patilea, Hamdi Raïssi, (2023) Powers correlation analysis of returns with a non-stationary zero-process, Journal of Financial Econometrics, Oxford University Press (https://doi.org/10.1093/jjfinec/nbad030).
Aida Toma, Amor Keziou, Luiza Badin, Silvia Dedu, (2023) Equivariant robust estimators for moment condition models, accepted for publication in ISTE Book 2023 Data Analysis and Related Applications: Recent Findings, Christos H. Skiadas (Ed.), ISTE-Wiley, 2024, in press.
Valentin Patilea, H. Raïssi, (2023) Orthogonal impulse response analysis in presence of time-varying covariance, chapter in Research Papers in Statistical Inference for Time Series and Related Models, Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa (Eds), Springer, 419-443.
Luiza Bădin, Camilla Mastromarco, (2023) A nonparametric journey through conditional frontier models, in The Cambridge Handbook of Productivity, Efficiency & Effectiveness in Health Care, Eds. Shawna Grosskopf, Vivian Valdmanis, Valentin Zelenyuk – in press, 43 pg.
Sunny Wang, Valentin Patilea, Nicolas Klutchnikoff, (2023) Adaptive functional principal components analysis, Journal of the Royal Statistical Society Series B, under revision.
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea, (2023) Adaptive estimation of irregular mean and covariance functions, Bernoulli, under revision.
Aida Toma, Amor Keziou, Luiza Badin, Silvia Dedu, (2023) Robust Pitman type estimators for moment condition models, to be submitted.
Aida Toma, (2023) Robustness of minimum dual divergence estimators for two sample density ratio models, to be submitted.
Valentin Patilea, François Portier, (2023) Density model checks via the lack-of-fitness , to be submitted.
Samuel Maistre, Valentin Patilea, (2023) Nonparametric conditional independence testing for functional data, to be submitted.
Papers presented at International Conferences
Aida Toma, Alex Karagrigoriou, Paschalini Trentou, (2021) Robust Model Selection Criteria, The 19th Conference of Applied Stochastic Models and Data Analysis International Society ASMDA 2021, 1 – 4 June 2021. Invited Presentation in the framework of the special session „Models & Methods in Stochastic and Multivariate Data Analysis”.
Amor Keziou, Aida Toma, (2021) Robust Empirical Likelihood, 5th Conference on Geometric Science of Information in Paris, Sorbonne University, France 21-23 July 2021.
Luiza Badin, (2021) Recent Developments in Conditional Frontier Analysis, 19th Conference of the Applied Stochastic Models and Data Analysis International Society (ASMDA2021) Athens, Greece, 1-4 June 2021.
Aida Toma, Amor Keziou, (2022), Robust Minimum Empirical Divergence Estimators for Moment Condition Models, 7th Stochastic Modeling Techniques and Data Analysis International Conference and Demographics 2022 Workshop, Athens, Greece, 7-10 June 2022.
Aida Toma, Amor Keziou, Luiza Badin, Silvia Dedu, (2022) Robust Pitman type estimators for moment condition models, 24th International Conference on Computational Statistics (COMPSTAT 2022), Bologna, Italy, 23-26 August 2022.
Aida Toma, Amor Keziou, Luiza Badin, Silvia Dedu, (2022) Robust Estimators for Moment Condition Models, StatMod 2022, Statistical Modeling with Applications, Bucharest, Romania, 14-15 October 2022.
Marie du Roy de Chaumaray, Matthieu Marbac, Valentin Patilea, (2022) On Wilks’ Theorem for Conditional Moment Equations with Weakly Dependent Data, International Symposium on Nonparametric Statistics (ISNPS), Paphos, Cyprus, June 20-24.
Valentin Patilea, François Portier, (2022) Density model checks via the lack-of-fitness, 5th International Workshop on Goodness-Of-Fit (GOF) and Change-Point (CP) Problems, Ensai Rennes (France), 2-4 September 2022.
Valentin Patilea, François Portier, (2022) Density model checks via the lack-of-fitness, New Advances in Statistical Modeling and Applications, 17 November 2022, Departamentul de Matematici Aplicate (ASE), București.
Valentin Patilea, François Portier, (2022) Density model checks via the lack-of-fitness, 2022 IMS International Conference on Statistics and Data Science (ICSDS), Florence, Italy, December 13-16.
Luiza Bădin, C. Mastromarco, R. Lagravinese, (2022) Performance Assessment of the Italian Healthcare System Using Conditional Nonparametric Efficiency Models, The 17th European Workshop of Efficiency and Productivity Analysis (EWEPA XVII)/Portugalia, June 27-30.
Luiza Bădin, C. Mastromarco, R. Lagravinese, (2022) Performance Assessment of the Italian Healthcare System Using Conditional Nonparametric Efficiency Models, Statistical Modeling with Applications StatMod2022/România, October 14-15.
Silvia Dedu, Iuliana Iatan, Muhammad Sheraz (2022) Loss models involving truncated and censored random variables (2022) The 23rd Conference of The Romanian Probability and Statistics Society, Bucharest, November 18-19, 2022.
Silvia Dedu, Vasile Preda, Iuliana Iatan, Muhammad Sheraz, (2022) Loss models and survival models involving truncated and censored random variables, The 9th International Conference Economic Scientific Research - Theoretical, Empirical and Practical Approaches ESPERA 2022, November 24-25, 2022.
Vasile Preda, Iuliana Iatan, Silvia Dedu, Muhammad Sheraz, (2022) Loss models using the Power Nakagami distribution, The 9th International Conference Economic Scientific Research - Theoretical, Empirical and Practical Approaches ESPERA 2022, November 24-25, 2022.
Aida Toma, (2023) Robust Estimators for Semiparametric Moment Condition Models, the 24th Conference of the Romanian Society of Probability and Statistics, Bucharest, April 21-22, 2023.
Aida Toma, (2023) Robust Estimators for Semiparametric Moment Condition Models, International Conference on Robust Statistics - ICORS 2023, Toulouse, France, May 23-26, 2023.
Aida Toma, Amor Keziou, Luiza Badin, Silvia Dedu, (2023) Robust Pitman type Estimators for Moment Condition Models, Applied Stochastic Models and Data Analysis International Conference - ASMDA 2023, Heraklion, Crete, Greece, June 6-9, 2023.
Valentin Patilea, (2023) Adaptive functional principal components analysis, the 24th Conference of the Romanian Society of Probability and Statistics, Bucharest, April 21-22, 2023.
Valentin Patilea, (2023) Adaptive Functional Data Analysis, 43rd Conference on Applied Statistics in Ireland – CASI 2023, Killarney, Ireland, May 15-17, 2023.
Valentin Patilea, (2023) Regularity estimation in multivariate functional data analysis, 10th Congress of Romanian Mathematicians, Pitesti, June 30 – July 5, 2023.
Valentin Patilea, François Portier, (2023) Density model checks via the lack-of-fitness, 6th International Workshop on Goodness-Of-Fit (GOF) and Change-Point (CP) Problems, Skukuza, South Africa, 25-29 August 2023.
Luiza Badin, C. Mastromarco, R. Lagravinese, (2023) Performance Assessment of the Italian Healthcare System Using Conditional Nonparametric Efficiency Models, The 24th Conference of the Romanian Society of Probability and Statistics (SPSR), Bucharest, April 21-22, 2023.
Luiza Badin, C. Mastromarco, R. Lagravinese, (2023) A nonparametric journey through conditional frontier models, Annual Scientific Conference of Romanian Academic Economists from Abroad (ERMAS), Bucharest, July 26-28, 2023.
Silvia Dedu, Vasile Preda, Muhammad Sheraz, (2023) A new estimation approach to loss models and survival models using general information measures, Statistical Modeling with Applications StatMod2023, Bucharest, September 29-30, 2023.
Silvia Dedu, Vasile Preda, Muhammad Sheraz, (2023) General information measures for loss models and survival models involving truncated and censored random variables, Applied Stochastic Models and Data Analysis International Conference - ASMDA 2023, Heraklion, Crete, Greece, June 6-9, 2023.
Vasile Preda, Muhammad Sheraz, Silvia Dedu, Imran Nasir, (2023) Information measures and modeling financial market volatility: A comparative approach, The 10th International Conference Economic Scientific Research - Theoretical, Empirical and Practical Approaches ESPERA 2023, Bucharest, November 23-24, 2023.
Conferences and Workshops organized in the framework of the project and with support from the project
5th International Workshop on Goodness-of-Fit and Change Point, Rennes, France, 2-4 September 2022, https://ensai.fr/wp-content/uploads/2022/08/book.pdf (Organized by Valentin Patilea).
New Advances in Statistical Modeling and Applications, Bucharest University of Economic Studies, 17th November 2022 (Organized by Aida Toma, Luiza Badin and Silvia Dedu).
The 24th Conference of the Romanian Society of Probability and Statistics - SPSR 2023, 21-22 April 2023, organized at Bucharest University of Economic Studies.
SYNOPSIS
The purpose of the project was to introduce robust versions of the empirical likelihood method, as well as other new statistical methods for various semiparametric models and especially for moment condition models and two sample density ratio models. The theme proposed in this project addresses an important issue in statistics and econometrics, namely the assessment of the sensitivity of statistical methods to possible deviations from the assumed model and the use of robust methods. For example, in econometrics, imposing restrictions without assuming underlying distributions to modelize complex realities is a valuable methodological tool. However, if some restrictions were not correctly specified or the data set contains outliers, the usual statistical methods for correctly specified moment condition models may give completely erroneous results. The use of robust methods has much practical importance, in order to ensure accurate conclusions of the statistical analysis and for improving knowledge from “non-perfect” data sets.
Some of the new statistical methods proposed through this project use information measures, namely divergences and entropy measures. Statistical methods based on divergence minimization extend the likelihood paradigm and often have the advantage to provide a trade-off between efficiency and robustness. In the framework of this project, using duality techniques to the divergence optimization, we obtained large classes of estimators and test statistics for moment condition models and for two sample density ratio models. The duality approach has the advantage to avoid any smoothing or grouping technique which would be necessary for a more direct divergence minimization approach for the same problem. The proposed new statistical methods represent attractive alternatives to the classical empirical likelihood method. They have robustness properties, as well as other asymptotic properties including consistency and may also be efficient when the model is correctly specified.
For the first stage of the project, our objective was related to the developement and the theoretical study of new statistical methods for semiparametric models. Empirical likelihood (EL) is a powerful and currently widely used approach for developing efficient nonparametric statistical methods for various semiparametric models. The EL estimator for moment condition models is preferable to other estimators, due to the higher-order asymptotic properties, but these properties are valid only in the case of the correct specification of the moment conditions. Also, in the case of the presence of outliers in the sample, the EL estimator may give completely erroneous results. We proposed robust versions of the EL estimator for moment condition models and proved their asymptotic properties including consistency and asymptotic normality. The robust versions of the EL estimator are based on using truncated orthogonality functions. The truncated orthogonality function is constructed using the multivariate Huber function, such that the original restrictions of the model and the new ones, based on truncated function, are satisfied for the same value of the parameter of interest. We proved the robustness of the new estimators by using the influence function approach. Extensions of the smooth MD approach for inference in semiparametric partially linear models that can be defined by conditional moment conditions have also been considered. We also studied the estimation and inference for conditional estimating equations models in the context of survey sampling.
For the second stage of the project, our objective was related to the development and the study of new statistical methods for various semiparametric models, as well as the implementation of these methods using specialized statistical software. We introduced a wide class of robust minimum empirical divergence estimators and tests for moment condition models. We considered truncated orthogonality functions, such that the original restrictions of the model and the new restrictions, based on truncated functions, are satisfied for the same value of the parameter of interest. We defined the moment condition model associated to an empirical version of truncated orthogonality function, model that includes the original reference model. Then we defined a large class of estimators for the parameter of interest, by minimizing an empirical version of the dual form of the divergence between the new model and the true model, corresponding to the data. The class of estimators is indexed by the -function corresponding to the used divergence and contains some known estimators, as special cases. We proved theoretical properties for the new estimators, including B-robustness and the consistency. We derived new robust tests by using estimators of the used divergence. We also proposed large classes of equivariant robust estimators for moment condition models invariant with respect to additive or multiplicative groups of transformations on the observations space. Within such a class of equivariant robust estimators, we characterized the minimum risk equivariant estimator or the Pitman estimator. We obtained asymptotic approximations for the Pitman estimator, in the case when the moment condition model is invariant with respect to additive groups. We developed new model check procedures using lack-of-fitness statistics and proved some convergence results. We also considered various particular models that can be written using moment equations for which we studied new statistical methods. Models examined in the empirical finance literature, often imply moment conditions that can be used in a straightforward way to estimate the model parameters without making strong assumptions regarding the stochastic properties of observed variables. For example, the stochastic lognormal volatility (SLV) model offers a powerful alternative to GARCH-type models to explain the well-documented time varying volatility and can be written under the form of moment equations.
In the third stage of the project, we continued studies from the previous stages and developed new robust statistical methods for semiparametric moment condition models. We defined new robust Z-estimators for moment condition models and studied their theoretical properties. We also defined minimum dual divergence estimators for semiparametric two-sample density ratio models, for which we studies robustness properties. Our study reveals that, appropriate choices of the divergence from the Cressie-Read class lead to B-robust estimators through the duality technique, thus providing robust alternatives for the empirical likelihood estimator in this context. We also considered the problem of conditional independence between vectors and proposed a new nonparametric test. The approach is based on writing the conditional independence under the form of a set, usually infinite, of conditional moment equations. Statistical methods for other various semiparametric models have been developed, including conditional frontier models or models for functional data. Quantile frontier models, including the partial, robust alpha-frontiers can be related to moment equations, since the quantiles, conditional or not, can be defined through moment equations as well.
A special attention was given to the numerical aspects, including creating algorithms, implementing the proposed statistical methods and the Monte Carlo simulation studies.