The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform

Elvezio Ronchetti - One of the best experts on this subject based on the ideXlab platform.

  • Optimal conditionally unbiased Bounded-Influence inference in dynamic location and scale models
    Journal of the American Statistical Association, 2005
    Co-Authors: Loriano Mancini, Elvezio Ronchetti, Fabio Trojani
    Abstract:

    This article studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal Bounded-Influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, we obtain optimal Bounded-Influence versions of the classical likelihood-based tests for parametric hypotheses. We propose a feasible and efficient algorithm for the computation of our robust estimators, which uses analytical Laplace approximations to estimate the auxiliary recentering vectors, ensuring Fisher consistency in robust estimation. This strongly reduces the computation time by avoiding the simulation of multidimensional integrals, a task that typically must be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR(1)–ARCH(1) process, we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the ...

  • Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models
    SSRN Electronic Journal, 2003
    Co-Authors: Loriano Mancini, Fabio Trojani, Elvezio Ronchetti
    Abstract:

    This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal Bounded-Influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal Bounded-Influence versions of the classical likelihood-based tests for parametric hypotheses are obtained. We propose a feasible and efficient algorithm for the computation of our robust estimators, which makes use of analytical Laplace approximations to estimate the auxiliary recentering vectors ensuring Fisher consistency in robust estimation. This strongly reduces the necessary computation time by avoiding the simulation of multidimensional integrals, a task that has typically to be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR 1)-ARCH(1) process we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the same time perform very satisfactorily under several forms of departure from conditional normality. On the contrary, classical Pseudo Maximum Likelihood inference procedures are found to be highly inefficient under such local model misspecifications. These patterns are confirmed by an application to robust testing for ARCH.

  • Robust Bounded-Influence Tests in General Parametric Models
    Journal of the American Statistical Association, 1994
    Co-Authors: Stephane Heritier, Elvezio Ronchetti
    Abstract:

    Abstract We introduce robust tests for testing hypotheses in a general parametric model. These are robust versions of the Wald, scores, and likelihood ratio tests and are based on general M estimators. Their asymptotic properties and Influence functions are derived. It is shown that the stability of the level is obtained by bounding the self-standardized sensitivity of the corresponding M estimator. Furthermore, optimally Bounded-Influence tests are derived for the Wald- and scores-type tests. Applications to real and simulated data sets are given to illustrate the tests' performance.

Denis Hamad - One of the best experts on this subject based on the ideXlab platform.

  • Bounded Influence Support Vector Regression for Robust Single-Model Estimation
    IEEE transactions on neural networks, 2009
    Co-Authors: Franck Dufrenois, Johan Colliez, Denis Hamad
    Abstract:

    Support vector regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with severe outlier contamination of both response and predictor variables commonly encountered in numerous real applications. In this paper, we present a Bounded Influence SVR, which downweights the Influence of outliers in all the regression variables. The proposed approach adopts an adaptive weighting strategy, which is based on both a robust adaptive scale estimator for large regression residuals and the statistic of a ldquokernelizedrdquo hat matrix for leverage point removal. Thus, our algorithm has the ability to accurately extract the dominant subset in corrupted data sets. Simulated linear and nonlinear data sets show the robustness of our algorithm against outliers. Last, chemical and astronomical data sets that exhibit severe outlier contamination are used to demonstrate the performance of the proposed approach in real situations.

Franck Dufrenois - One of the best experts on this subject based on the ideXlab platform.

  • Bounded Influence Support Vector Regression for Robust Single-Model Estimation
    IEEE transactions on neural networks, 2009
    Co-Authors: Franck Dufrenois, Johan Colliez, Denis Hamad
    Abstract:

    Support vector regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with severe outlier contamination of both response and predictor variables commonly encountered in numerous real applications. In this paper, we present a Bounded Influence SVR, which downweights the Influence of outliers in all the regression variables. The proposed approach adopts an adaptive weighting strategy, which is based on both a robust adaptive scale estimator for large regression residuals and the statistic of a ldquokernelizedrdquo hat matrix for leverage point removal. Thus, our algorithm has the ability to accurately extract the dominant subset in corrupted data sets. Simulated linear and nonlinear data sets show the robustness of our algorithm against outliers. Last, chemical and astronomical data sets that exhibit severe outlier contamination are used to demonstrate the performance of the proposed approach in real situations.

Maria-pia Victoria-feser - One of the best experts on this subject based on the ideXlab platform.

  • Bounded-Influence Robust Estimation in Generalized Linear Latent Variable Models
    Journal of the American Statistical Association, 2006
    Co-Authors: Irini Moustaki, Maria-pia Victoria-feser
    Abstract:

    Latent variable models are used for analyzing multivariate data. Recently, generalized linear latent variable models for categorical, metric, and mixed-type responses estimated via maximum likelihood (ML) have been proposed. Model deviations, such as data contamination, are shown analytically, using the Influence function and through a simulation study, to seriously affect ML estimation. This article proposes a robust estimator that is made consistent using the basic principle of indirect inference and can be easily numerically implemented. The performance of the robust estimator is significantly better than that of the ML estimators in terms of both bias and variance. A real example from a consumption survey is used to highlight the consequences in practice of the choice of the estimator.

  • Handbook of Statistics no 15 - 4 Practical applications of Bounded-Influence tests
    Handbook of Statistics, 1997
    Co-Authors: Stephane Heritier, Maria-pia Victoria-feser
    Abstract:

    Publisher Summary This chapter discusses the practical applications of Bounded-Influence tests. The robust versions of classical likelihood ratio, Wald or score tests, are now available in a general setting. They are more reliable than their classical counterparts—that is, they are not Influenced by small deviations from the underlying model and can also be used as useful diagnostic tools to identify influential or outlying data points. The chapter illustrates their performance to show that they can be easily implemented in different practical situations. They can be used to robustly choose a model when the hypotheses are non-nested. That is when the model under the null hypothesis cannot be obtained as a particular or limiting case of the model under the alternative hypothesis. The approach followed is the approach based on the Influence function. It is mainly concerned with the local robustness properties of tests. A parametric model is considered to study the effects of departures from the model on the testing procedures. The chapter also discusses robust testing in generalized linear models (or GLIM) and emphasizes the use of robust tests in logistic regression. As typical examples, the Food–Stamp data analyzed by Stefanski, Carroll, and Ruppert and the data introduced by Cormier, Magnan, and Morard in auditing is used.

R Ricci - One of the best experts on this subject based on the ideXlab platform.

  • application and comparison of high breakdown point and Bounded Influence estimators to rotor balancing
    Journal of Vibration and Acoustics, 2010
    Co-Authors: Steven Chatterton, R Ricci, Paolo Pennacchi
    Abstract:

    In the field of rotor balancing, the traditional Influence coefficient method is often employed along with weighted least squares in order to reduce amplitude vibrations, typically at selected rotating speeds such as critical or operating ones. Usually, these weights are manually selected by the operator. In this paper, an automatic procedure for the balancing mass estimation based on robust regression methods is introduced. The analysis is focused on high breakdown-point and Bounded-Influence estimators. The effectiveness and robustness of the proposed balancing procedure are shown by means of simulations of a 180 MW gas turbo generator of a power plant.

  • rotor balancing using high breakdown point and Bounded Influence estimators
    Mechanical Systems and Signal Processing, 2010
    Co-Authors: Paolo Pennacchi, Steven Chatterton, R Ricci
    Abstract:

    In industrial field, one of the most important practical problems of rotating machinery concerns rotor balancing. Many different methods are used for rotor balancing. Traditional Influence coefficient method is often employed along with weighted least squares in order to reduce vibration amplitude, typically at selected rotating speeds like critical or operating ones. Usually the selection of the weights of the least-squares algorithm is manually made by a skilled operator that can decide in which speed range the vibration reduction is more effective. Several methods have been proposed in order to avoid operator's arbitrariness and an automatic procedure based on robust regression is introduced in this paper. In particular, the analysis is focused on high breakdown-point and Bounded-Influence estimators. Theoretical aspects and properties of these methods are investigated. The effectiveness and robustness of the proposed balancing procedure are shown by means of an experimental case using a test-rig.