In this paper, I devise a new way to construct con…dence sets for a parameter of interest in models comprised of a …nite number of moment inequalities. Many models of this form have appeared in the literature to date, particularly in the recent literature on partial identi…cation, but performing statistical inference in these settings is an area of ongoing research. Toward this end, I establish...

This paper introduces a novel, data{adaptive ltering framework: aane order{statistic lters. AAne order{statistic lters operate eeectively on a wide range of signal statistics, are sensitive to the dispersion of the observed data, and are therefore particularly useful in the processing of nonstationary signals. These properties result from the introduction of a tunable aanity function that measu...

In a randomized experiment with noncompliance, scientific interest is often in testing whether the treatment exposure X has an effect on the final outcome Y [2, 1]. We have proposed a finite-population significance test of the sharp null hypothesis that X has no effect on Y , within the principal stratum of compliers, using a generalized likelihood ratio test [4]. As both the null and alternati...

Recent research by Bliemer and Rose (2005, 2009, in press) and Rose and Bliemer (2005) suggest as a measure for calculating sample size requirements for models estimated using stated choice data, the S-error statistic. Prior to this, existing sampling theories failed to adequately address the issue of sample size requirements specifically for this type of data and hence researchers have had to ...

Database privacy has garnered a recent surge in interest from the theoretical computer science community following the seminal work of [DMNS06], which proposed the strong notion of differential privacy. Classical differentially private mechanisms produce a noisy statistic on an input database that is ε-differentially private for some input parameter ε. This work continues a new line of research...

T he process of drawing conclusions about an entire population on the basis of the information contained in a random sample drawn from that population is known as statistical inference. Methods of statistical inference fall into 2 general categories: estimation and hypothesis testing. With estimation, our goal is to describe or estimate some characteristic of a population of interest, such as t...

Sensitivity analysis stands in contrast to diagnostic testing in that sensitivity analysis aims to answer the question of whether it matters that a nuisance parameter is non-zero, whereas a diagnostic test ascertains explicitly if the nuisance parameter is different from zero. In this paper, we introduce and derive the finite sample properties of a sensitivity statistic measuring the sensitivit...

Kendall and Gehan estimating functions are used to estimate the regression parameter in accelerated failure time (AFT) model with censored observations. The accelerated failure time model is the preferred survival analysis method because it maintains a consistent association between the covariate and the survival time. The jackknife empirical likelihood method is used because it overcomes compu...

Assessing the therapeutic noninferiority or equivalence of one medical treatment compared to another is often based on the difference of response rates from a matched binary pairs design. This paper develops new exact unconditional tests for noninferiority and equivalence that are more powerful than available alternatives. There are three new elements presented in this paper. First we introduce...

Sequential attack detection in a distributed estimation system is considered, where each sensor successively produces one-bit quantized samples of a desired deterministic scalar parameter corrupted by additive noise. The unknown parameters in the pre-attack and post-attack models, namely the desired parameter to be estimated and the injected malicious data at the attacked sensors pose a signifi...