%%% Title of object: Robust Inference
%%% Canonical Name: RobustInference
%%% Type: Topic
%%% Created on: 2010-08-29 14:17:06
%%% Modified on: 2010-09-28 10:00:40
%%% Creator: ronchett
%%% Modifier: jkimmel
%%% Author: ronchett
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%%% Classification: msc:62F35, msc:62F03
%%% Keywords: minimax, influence function, infinitesimal approach, robustness of validity, robustness of efficiency, neighborhoods
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\begin{center}
{\bf \large ROBUST INFERENCE}\\[4mm]
{\large \it Elvezio Ronchetti \footnote{Professor Elvezio Ronchetti is Past Vice-President of the
Swiss Statistical Association (1988---91).
He was Chair, Department of Econometrics, University of Geneva (2001---2007).
He is an Elected Fellow of the American Statistical Association (2001) and of the International Statistical
Institute (2008). Currently he is an Associate Editor, \textit{Journal of the American Statistical
Association}(2005---present) and Director of the Master of Science and PhD Program in Statistics, University
of Geneva (2009---present). He is the co-author (with F.R. Hampel, P.J. Rousseeuw and W.A. Stahel) of the well known text \textit{Robust Statistics : The Approach Based on Influence Functions}
(Wiley, New York, 1986, translated also into Russian), and of the 2nd edition of Huber's classic \textit{Robust Statistics} (with P. J. Huber, Wiley, 2009).}}
Professor, Department of Econometrics
University of Geneva, CH-1211 Geneva, Switzerland
\end{center}
The primary goal of robust statistics is the development of procedures which are still
reliable and reasonably efficient under small deviations from the model,
i.e. when the underlying distribution lies in a neighborhood of the assumed
model.
Robust statistics is then an extension of parametric statistics, taking into
account that parametric models are at best only
approximations to reality.
The field is now some 50 years old. Indeed one can consider Tukey (1960),
Huber (1964), and Hampel (1968) the fundamental papers which laid the foundations
of modern robust statistics. Book-length expositions can be found in
Huber (1981, 2nd edition by Huber and Ronchetti 2009),
Hampel, Ronchetti, Rousseeuw, Stahel (1986), Maronna, Martin,
Yohai (2006).
More specifically, in robust testing one would like the level of a test to
be stable under small, arbitrary departures from the distribution at
the null hypothesis ({\it robustness of validity}). Moreover, the
test should still have good power under small arbitrary departures
from specified alternatives {\it (robustness of efficiency)}.
For confidence intervals, these criteria correspond to stable
coverage probability and length of the confidence interval.
Many classical tests do not satisfy these criteria.
An extreme case of nonrobustness is the F-test for comparing two variances.
Box (1953) showed that the level of this test becomes large in the
presence of tiny deviations from the normality assumption; see
Hampel {\it et al.} (1986), p. 188-189.
Well known classical tests exhibit
robustness problems too. The classical t-test
and F-test for linear models
are relatively robust with respect to the level, but they lack
robustness of efficiency with respect to small departures
from the normality assumption on the errors;
cf. Hampel (1973), Schrader and Hettmansperger (1980),
Ronchetti (1982), Heritier {\it et al.} (2009), p. 35. Nonparametric tests
are attractive
since they have an exact level under symmetric distributions
and good robustness of efficiency.
However, the distribution free property of their level
is affected by asymmetric contamination,
cf. Hampel {\it et al.} (1986), p. 201.
Even randomization tests
which keep an exact level,
are not robust with respect to the
power if they are based on a non-robust test statistic like the
mean.
The first approach to formalize the robustness problem
was Huber's (1964, 1981) minimax theory, where the statistical problem
is viewed as a game between the Nature (which
chooses a distribution in the neighborhood of the model) and the statistician
(who chooses a statistical procedure in a given class). The statistician
achieves robustness by constructing a minimax procedure which minimizes
a loss criterion
at the worst possible distribution in the neighborhood. More specifically,
in the problem of testing a simple hypothesis against
a simple alternative, Huber (1965, 1981)
found the test which maximizes the minimum
power over a neighborhood of the alternative, under the side
condition that the maximum level over a neighborhood of the
hypothesis is bounded. The solution to this problem
which is an extension of Neyman-Pearson's Lemma,
is the censored likelihood ratio test. It can be interpreted in
the framework of capacities (Huber and Strassen, 1973) and it
leads to exact finite sample minimax confidence intervals
for a location parameter (Huber, 1968). While
Huber's minimax theory is one of the key ideas in
robust statistics and leads to elegant and exact finite sample
results, it seems difficult to extend it to general
parametric models, when no invariance structure is available.
The infinitesimal approach introduced in Hampel (1968) in the framework
of estimation, offers an alternative for
more complex models. The idea is to view the quantities of interest
(for instance the bias or the variance of an estimator) as functionals of the
underlying distribution and to use their linear approximations to study their
behavior in a neighborhood of the ideal model. A key tool is a derivative of such
a functional, the influence function (Hampel, 1974) which describes the local
stability of the functional.
To illustrate the idea in the framework of testing,
consider a parametric model $\{F_{\theta}\}$, where
$\theta$ is a real parameter and
a test statistic $T_n$ which can be written (at least asymptotically)
as a functional
$T(F_{n})$ of the empirical distribution function $F_{n}$.
Let $H_0:$ $\theta=\theta_0$ be the null hypothesis and
$\theta_n = \theta_0 + {\Delta / \sqrt n}$ a sequence
of alternatives.
We consider a neighborhood of distributions
$F_{\epsilon, \theta ,n}=( 1 - {\epsilon / \sqrt n}) F_{\theta} +
( {\epsilon / \sqrt n}) G,$ where $G$ is an arbitrary
distribution and we can view
the asymptotic level $\alpha$ of the test as a
functional of a distribution in the neighborhood. Then
by a von Mises expansion of $\alpha$ around $F_{\theta_0}$,
where $\alpha(F_{\theta_0})=\alpha_0$, the nominal level of the test,
the asymptotic level
and (similarly) the asymptotic power under contamination can be expressed as
\begin{eqnarray}
\lim_{n \rightarrow \infty} \alpha(F_{\epsilon, \theta_0 ,n}) &=&
\alpha_0 + \epsilon \int IF(x; \alpha, F_{\theta_0})dG(x)
+ o(\epsilon), \label{as_level_univariate} \\
\lim_{n \rightarrow \infty} \beta(F_{\epsilon, \theta_n ,n}) &=&
\beta_0 + \epsilon \int IF(x; \beta, F_{\theta_0})dG(x)
+ o(\epsilon), \label{as_power_univariate}
\end{eqnarray}
where
\begin{eqnarray*}
IF(x; \alpha, F_{\theta_0}) &=& \phi (\Phi^{-1}(1-\alpha_0))
IF(x; T, F_{\theta_0})\Big /[V(F_{\theta_0}, T)]^{1/2}, \\
IF(x; \beta, F_{\theta_0}) &=& \phi (\Phi^{-1}(1-\alpha_0)-
\Delta \sqrt E)
IF(x; T, F_{\theta_0})\Big /[V(F_{\theta_0}, T)]^{1/2},
\end{eqnarray*}
$\alpha_0=\alpha(F_{\theta_0})$ is the nominal asymptotic level,
$\beta_0= 1-\Phi(
\Phi^{-1}(1-\alpha_0)- \Delta \sqrt E)$ is the nominal
asymptotic power,
$E= [\xi'(\theta_0)]^2 \Big /V(F_{\theta_0}, T)$
is Pitman's efficacy of the test,
$\xi(\theta)=
T(F_\theta)$, $V(F_{\theta_0}, T)=
\int IF(x; T, F_{\theta_0})^2dF_{\theta_0}(x)$ is
the asymptotic variance of T, and
$\Phi^{-1}(1-\alpha_0)$ is the $1-\alpha_0$ quantile of the
standard normal distribution $\Phi$ and $\phi$ is its density;
see Ronchetti (1979), Rousseeuw and Ronchetti (1979).
More details can be found in
Markatou and Ronchetti (1997) and Huber and Ronchetti (2009), Ch. 13.
Therefore, bounding the influence function
of the the test statistic $T$ from {\it above} will ensure
{\it robustness of validity}
and bounding it from
{\it below} will ensure {\it robustness of efficiency}
This is
in agreement with the exact finite sample result about the
structure of the censored likelihood ratio test obtained using
the minimax approach.
In the multivariate case and for general parametric models,
the classical theory provides three asymptotically equivalent tests,
Wald, score, and likelihood
ratio test, which are asymptotically
uniformly most powerful with respect to a sequence of contiguous
alternatives.
If the parameter of the model is estimated by a robust estimator such
as an $M-$estimator $T_n$ defined by the estimating equation
$\sum_{i=1}^n \psi(x_i;T_n)=0, $
natural extensions of the three classical tests can be constructed
by replacing the score function of the model by the function $\psi$.
This leads to formulas similar to (\ref{as_level_univariate}) and
(\ref{as_power_univariate}) and to optimal
bounded influence tests;
see Heritier and Ronchetti (1994).
\vspace{1.5cc}
\noindent{\bf References}
\newcounter{ref}
\begin{list}{\small [\,\arabic{ref}\,]}{\usecounter{ref} \leftmargin 4mm \itemsep -1mm}
{\small
\item
Box G.E.P. (1953).
Non-normality and Tests on Variances'.
{\it Biometrika}. {\bf 40},
318--335.
\item Hampel F. R. (1968). Contribution to the Theory of
Robust Estimation. Ph.D Thesis, University of California,
Berkeley.
\item Hampel F.R. (1973). Robust Estimation: a Condensed
Partial Survey.
{\it Zeitschrift f\"ur Wahrscheinlichkeitstheorie und Verwandte
Gebiete} {\bf 27}, 87--104.
\item Hampel F.R. (1974). The Influence Curve and its Role in
Robust Estimation. {\it Journal of the American Statistical
Association} {\bf 69}, 383--393.
\item Hampel F.R., Ronchetti E.M., Rousseeuw P.J. and Stahel W.A. (1986).
{\it Robust Statistics: The Approach Based on Influence Functions}.
Wiley, New York.
\item Heritier S. and Ronchetti E. (1994).
Robust Bounded-influence Tests in General Parametric Models.
{\it Journal of the American Statistical Association}, {\bf 89},
897--904.
\item Heritier S., Cantoni E., Copt S. and Victoria-Feser M.-P. (2009).
{\it Robust Methods in Biostatistics}. Wiley, Chichester.
\item Huber P.J. (1964). Robust Estimation of a Location Parameter.
{\it Annals of Mathematical Statistics} {\bf 35}, 73--101.
\item Huber P.J. (1965). A Robust Version of the Probability Ratio
Test.
{\it Annals of Mathematical Statistics} {\bf 36}, 1753--1758.
\item Huber P.J. (1968). Robust Confidence Limits.
{\it Zeitschrift f\"ur Wahrscheinlichkeitstheorie und Verwandte
Gebiete} {\bf 10}, 269--278.
\item Huber P. J. and Strassen V. (1973). Minimax Tests and
the Neyman-Pearson Lemma for Capacities.
{\it The Annals of Statistics} {\bf 1}, 251--263; {\bf 2},
223--224.
\item Huber P.J. (1981). {\it Robust Statistics}. Wiley, New York.
\item Huber P.J. and Ronchetti E. M. (2009). {\it Robust Statistics}.
2nd edition, Wiley, New York.
\item Markatou M. and Ronchetti E. (1997).
Robust Inference: The Approach Based on Influence Functions.
In Maddala G. S. and Rao C. R. eds.,
{\it Handbook of Statistics}. {\bf 15}, North Holland,
49--75.
\item Maronna R. A., Martin R. D. and Yohai V. J. (2006).
{\it Robust Statistics: Theory and Methods}. Wiley, New York.
\item Ronchetti E. (1979).
Robustheitseigenschaften von Tests.
Diploma Thesis, ETH Z\"urich, Switzerland.
\item Ronchetti E. (1982).
Robust Testing in Linear Models: The Infinitesimal Approach.
PhD Thesis, ETH Z\"urich, Switzerland.
\item Rousseeuw, P.J. and Ronchetti E. (1979).
The Influence Curve for Tests.
Research Report 21, Fachgruppe f\"ur Statistik,
ETH Z\"urich, Switzerland.
\item Schrader R. M. and Hettmansperger T. P. (1980).
Robust Analysis of Variance Based Upon a Likelihood Ratio
Criterion.
{\it Biometrika}, {\bf 67}, 93--101.
\item Tukey J. W. (1960). A Survey of Sampling from Contaminated
Distributions.
In Olkin I. ed.,
{\it Contributions to Probability and Statistics},
Stanford University Press, 448--485.
}
\end{list}
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