30,963 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
Automatic speech recognition in air-ground data link
In the present air traffic system, information presented to the transport aircraft cockpit crew may originate from a variety of sources and may be presented to the crew in visual or aural form, either through cockpit instrument displays or, most often, through voice communication. Voice radio communications are the most error prone method for air-ground data link. Voice messages can be misstated or misunderstood and radio frequency congestion can delay or obscure important messages. To prevent proliferation, a multiplexed data link display can be designed to present information from multiple data link sources on a shared cockpit display unit (CDU) or multi-function display (MFD) or some future combination of flight management and data link information. An aural data link which incorporates an automatic speech recognition (ASR) system for crew response offers several advantages over visual displays. The possibility of applying ASR to the air-ground data link was investigated. The first step was to review current efforts in ASR applications in the cockpit and in air traffic control and evaluated their possible data line application. Next, a series of preliminary research questions is to be developed for possible future collaboration
Unbiased Instrumental Variables Estimation Under Known First-Stage Sign
We derive mean-unbiased estimators for the structural parameter in
instrumental variables models with a single endogenous regressor where the sign
of one or more first stage coefficients is known. In the case with a single
instrument, there is a unique non-randomized unbiased estimator based on the
reduced-form and first-stage regression estimates. For cases with multiple
instruments we propose a class of unbiased estimators and show that an
estimator within this class is efficient when the instruments are strong. We
show numerically that unbiasedness does not come at a cost of increased
dispersion in models with a single instrument: in this case the unbiased
estimator is less dispersed than the 2SLS estimator. Our finite-sample results
apply to normal models with known variance for the reduced-form errors, and
imply analogous results under weak instrument asymptotics with an unknown error
distribution
Optimal inference in a class of regression models
We consider the problem of constructing confidence intervals (CIs) for a
linear functional of a regression function, such as its value at a point, the
regression discontinuity parameter, or a regression coefficient in a linear or
partly linear regression. Our main assumption is that the regression function
is known to lie in a convex function class, which covers most smoothness and/or
shape assumptions used in econometrics. We derive finite-sample optimal CIs and
sharp efficiency bounds under normal errors with known variance. We show that
these results translate to uniform (over the function class) asymptotic results
when the error distribution is not known. When the function class is
centrosymmetric, these efficiency bounds imply that minimax CIs are close to
efficient at smooth regression functions. This implies, in particular, that it
is impossible to form CIs that are tighter using data-dependent tuning
parameters, and maintain coverage over the whole function class. We specialize
our results to inference on the regression discontinuity parameter, and
illustrate them in simulations and an empirical application.Comment: 39 pages plus supplementary material
New structural approach for determining load carrying capability of filament wound composite materials
Metal lined boron and graphite composites exhibit high strength and minimum weight, making them superior to aluminum cylindrical shell structures and to steel or aluminum constructed pressure vessels. S glass filament-epoxy resin matrix with aluminum liner is suitable for cryogenic tanks
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