30,808 research outputs found
Robust estimation of superhedging prices
We consider statistical estimation of superhedging prices using historical
stock returns in a frictionless market with d traded assets. We introduce a
plugin estimator based on empirical measures and show it is consistent but
lacks suitable robustness. To address this we propose novel estimators which
use a larger set of martingale measures defined through a tradeoff between the
radius of Wasserstein balls around the empirical measure and the allowed norm
of martingale densities. We establish consistency and robustness of these
estimators and argue that they offer a superior performance relative to the
plugin estimator. We generalise the results by replacing the superhedging
criterion with acceptance relative to a risk measure. We further extend our
study, in part, to the case of markets with traded options, to a multiperiod
setting and to settings with model uncertainty. We also study convergence rates
of estimators and convergence of superhedging strategies.Comment: This work will appear in the Annals of Statistics. The above version
merges the main paper to appear in print and its online supplemen
Robust estimation for ARMA models
This paper introduces a new class of robust estimates for ARMA models. They
are M-estimates, but the residuals are computed so the effect of one outlier is
limited to the period where it occurs. These estimates are closely related to
those based on a robust filter, but they have two important advantages: they
are consistent and the asymptotic theory is tractable. We perform a Monte Carlo
where we show that these estimates compare favorably with respect to standard
M-estimates and to estimates based on a diagnostic procedure.Comment: Published in at http://dx.doi.org/10.1214/07-AOS570 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Robust estimation in simultaneous equations models
In this paper we review existing work on robust estimation for simultaneous equations models. Then we discuss three strategies for obtaining estimators with a high breakdown point, a controllable efficiency, and a reasonable computational cost: (a) robustifying Three-Stages Least Squares, (b) robustifying the Full Information Maximum Likelihood method by minimizing the determinant of a robust covariance matrix of residuals, and (c) generalizing multivariate tauestimators (Lopuhaa 1991) to these models. The latter seems the most promising approach
Robust estimation for ordinal regression.
Ordinal regression is used for modelling an ordinal response variable as a function of some explanatory variables. The classical technique for estimating the unknown parameters of this model is Maximum Likelihood (ML). The lack of robustness of this estimator is formally shown by deriving its breakdown point and its influence function. To robustify the procedure, a weighting step is added to the Maximum Likelihood estimator, yielding an estimator with bounded influence function. We also show that the loss in efficiency due to the weighting step remains limited. A diagnostic plot based on the Weighted Maximum Likelihood estimator allows to detect outliers of different types in a single plot.Breakdown point; Diagnostic plot; Influence function; Ordinal regression; Weighted maximum likelihood; Robust distances;
Robust Estimation for Linear Panel Data Models
In different fields of applications including, but not limited to,
behavioral, environmental, medical sciences and econometrics, the use of panel
data regression models has become increasingly popular as a general framework
for making meaningful statistical inferences. However, when the ordinary least
squares (OLS) method is used to estimate the model parameters, presence of
outliers may significantly alter the adequacy of such models by producing
biased and inefficient estimates. In this work we propose a new, weighted
likelihood based robust estimation procedure for linear panel data models with
fixed and random effects. The finite sample performances of the proposed
estimators have been illustrated through an extensive simulation study as well
as with an application to blood pressure data set. Our thorough study
demonstrates that the proposed estimators show significantly better
performances over the traditional methods in the presence of outliers and
produce competitive results to the OLS based estimates when no outliers are
present in the data set
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