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Power M-Estimators for Location and Scatter
Power M-estimators for location and scatter are studied by Frahm et al. (J. Multivariate Anal. 176:104569, 2020) in the context of missing data. It...
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M-estimators for Models with a Mix of Discrete and Continuous Parameters
A variety of parametric models are specified by a mix of discrete parameters, which take values from a countable set, and continuous parameters,...
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Asymptotic linear expansion of regularized M-estimators
Parametric high-dimensional regression requires regularization terms to get interpretable models. The respective estimators correspond to regularized...
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Robust ridge M-estimators with pretest and Stein-rule shrinkage for an intercept term
If the data contain both multicollinearity and outliers, the ridge M-estimator is the preferred estimator to the usual least square estimator...
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M-estimators and trimmed means: from Hilbert-valued to fuzzy set-valued data
Different approaches to robustly measure the location of data associated with a random experiment have been proposed in the literature, with the aim...
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The finite sample properties of sparse M-estimators with pseudo-observations
We provide finite sample properties of general regularized statistical criteria in the presence of pseudo-observations. Under the restricted strong...
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Estimating asymptotic variance of M-estimators in ranked set sampling
M-estimator for symmetric location families in ranked set sampling has been studied in the literature. Estimating asymptotic variance of this...
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M-Estimation in GARCH Models in the Absence of Higher-Order Moments
We consider a class of M-estimators of the parameters of a GARCH(p, q) model. These estimators are asymptotically normal, depending on score...
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Asymptotic Behaviour of Penalized Robust Estimators in Logistic Regression When Dimension Increases
In the framework of logistic regression in order to obtain sparse models and automatic variable selection, penalized M-estimators that bound the...
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Stein-rule M-estimation in sparse partially linear models
We propose and investigate the statistical properties of shrinkage M-estimators based on Stein-rule estimation for partially linear models under the...
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Weak Convergence and Empirical Processes With Applications to Statistics
This book provides an account of weak convergence theory, empirical processes, and their application to a wide variety of problems in statistics. The...
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Application of Bernstein Polynomials on Estimating a Distribution and Density Function in a Triangular Array
In this paper, we study some asymptotic properties for the Bernstein estimators of the limit distribution function and the limit density function...
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Asymptotic Properties of the M-estimation for an AR(1) Process with a General Autoregressive Coefficient
In this paper, we consider a first-order autoregressive process with a general autoregressive coefficient. Asymptotic behaviors of an M-estimator of...
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On Robust Estimators of a Sphericity Measure in High Dimension
The need to test (or estimate) sphericity arises in various applications in statistics, and thus the problem has been investigated in numerous...
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The Diverging Definition of Robustness in Statistics and Computer Vision
Statistics and computer vision have a different role for robustness. Statisticians are primarily concerned with the theoretical properties of...
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A non-classical parameterization for density estimation using sample moments
Probability density estimation is a core problem in statistics and data science. Moment methods are an important means of density estimation, but...
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Robust estimation of fixed effect parameters and variances of linear mixed models: the minimum density power divergence approach
Many real-life data sets can be analyzed using linear mixed models (LMMs). Since these are ordinarily based on normality assumptions, under small...
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