8,525 research outputs found
A Note on Minimax Testing and Confidence Intervals in Moment Inequality Models
This note uses a simple example to show how moment inequality models used in
the empirical economics literature lead to general minimax relative efficiency
comparisons. The main point is that such models involve inference on a low
dimensional parameter, which leads naturally to a definition of "distance"
that, in full generality, would be arbitrary in minimax testing problems. This
definition of distance is justified by the fact that it leads to a duality
between minimaxity of confidence intervals and tests, which does not hold for
other definitions of distance. Thus, the use of moment inequalities for
inference in a low dimensional parametric model places additional structure on
the testing problem, which leads to stronger conclusions regarding minimax
relative efficiency than would otherwise be possible
An adaptation theory for nonparametric confidence intervals
A nonparametric adaptation theory is developed for the construction of
confidence intervals for linear functionals. A between class modulus of
continuity captures the expected length of adaptive confidence intervals. Sharp
lower bounds are given for the expected length and an ordered modulus of
continuity is used to construct adaptive confidence procedures which are within
a constant factor of the lower bounds. In addition, minimax theory over
nonconvex parameter spaces is developed.Comment: Published at http://dx.doi.org/10.1214/009053604000000049 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Efficient two-sample functional estimation and the super-oracle phenomenon
We consider the estimation of two-sample integral functionals, of the type
that occur naturally, for example, when the object of interest is a divergence
between unknown probability densities. Our first main result is that, in wide
generality, a weighted nearest neighbour estimator is efficient, in the sense
of achieving the local asymptotic minimax lower bound. Moreover, we also prove
a corresponding central limit theorem, which facilitates the construction of
asymptotically valid confidence intervals for the functional, having
asymptotically minimal width. One interesting consequence of our results is the
discovery that, for certain functionals, the worst-case performance of our
estimator may improve on that of the natural `oracle' estimator, which is given
access to the values of the unknown densities at the observations.Comment: 82 page
Adaptive confidence intervals for regression functions under shape constraints
Adaptive confidence intervals for regression functions are constructed under
shape constraints of monotonicity and convexity. A natural benchmark is
established for the minimum expected length of confidence intervals at a given
function in terms of an analytic quantity, the local modulus of continuity.
This bound depends not only on the function but also the assumed function
class. These benchmarks show that the constructed confidence intervals have
near minimum expected length for each individual function, while maintaining a
given coverage probability for functions within the class. Such adaptivity is
much stronger than adaptive minimaxity over a collection of large parameter
spaces.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1068 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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