1,888 research outputs found
The Inuence of Misspecified Covariance on False Discovery Control when Using Posterior Probabilities
This paper focuses on the influence of a misspecified covariance structure on
false discovery rate for the large scale multiple testing problem.
Specifically, we evaluate the influence on the marginal distribution of local
fdr statistics, which are used in many multiple testing procedures and related
to Bayesian posterior probabilities. Explicit forms of the marginal
distributions under both correctly specified and incorrectly specified models
are derived. The Kullback-Leibler divergence is used to quantify the influence
caused by a misspecification. Several numerical examples are provided to
illustrate the influence. A real spatio-temporal data on soil humidity is
discussed.Comment: 22 pages, 5 figure
Sensitivity Analysis using Approximate Moment Condition Models
We consider inference in models deļ¬ned by approximate moment conditions. We show that near-optimal conļ¬dence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspeciļ¬cation of the moment conditions. In order to optimize performance under potential misspeciļ¬cation, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore diļ¬ers from the one that is optimal under correct speciļ¬cation. To formally show the near-optimality of these CIs, we develop asymptotic eļ¬iciency bounds for inference in the locally misspeciļ¬ed GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more
An exact adaptive test with superior design sensitivity in an observational study of treatments for ovarian cancer
A sensitivity analysis in an observational study determines the magnitude of
bias from nonrandom treatment assignment that would need to be present to alter
the qualitative conclusions of a na\"{\i}ve analysis that presumes all biases
were removed by matching or by other analytic adjustments. The power of a
sensitivity analysis and the design sensitivity anticipate the outcome of a
sensitivity analysis under an assumed model for the generation of the data. It
is known that the power of a sensitivity analysis is affected by the choice of
test statistic, and, in particular, that a statistic with good Pitman
efficiency in a randomized experiment, such as Wilcoxon's signed rank
statistic, may have low power in a sensitivity analysis and low design
sensitivity when compared to other statistics. For instance, for an additive
treatment effect and errors that are Normal or logistic or -distributed with
3 degrees of freedom, Brown's combined quantile average test has Pitman
efficiency close to that of Wilcoxon's test but has higher power in a
sensitivity analysis, while a version of Noether's test has poor Pitman
efficiency in a randomized experiment but much higher design sensitivity so it
is vastly more powerful than Wilcoxon's statistic in a sensitivity analysis if
the sample size is sufficiently large.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS508 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Sensitivity Analysis using Approximate Moment Condition Models
We consider inference in models deļ¬ned by approximate moment conditions. We show that near-optimal conļ¬dence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspeciļ¬cation of the moment conditions. In order to optimize performance under potential misspeciļ¬cation, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore diļ¬ers from the one that is optimal under correct speciļ¬cation. To formally show the near-optimality of these CIs, we develop asymptotic eļ¬iciency bounds for inference in the locally misspeciļ¬ed GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more
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