Bootstrap confidence regions computed from autoregressions of arbitrary order

Given a linear time series, e.g. an autoregression of infinite order, we may construct a finite order approximation and use that as the basis for confidence regions. The sieve or autoregressive bootstrap, as this method is often called, is generally seen as a competitor with the better-understood block bootstrap approach. However, in the present paper we argue that, for linear time series, the sieve bootstrap has significantly better performance than blocking methods and offers a wider range of opportunities. In particular, since it does not corrupt second-order properties then it may be used in a double-bootstrap form, with the second bootstrap application being employed to calibrate a basic percentile method confidence interval. This approach confers second-order accuracy without the need to estimate variance. That offers substantial benefits, since variances of statistics based on time series can be difficult to estimate reliably, and—partly because of the relatively small amount of information contained in a dependent process—are notorious for causing problems when used to Studentize. Other advantages of the sieve bootstrap include considerably greater robustness against variations in the choice of the tuning parameter, here equal to the autoregressive order, and the fact that, in contradistinction to the case of the block bootstrap, the percentile t version of the sieve bootstrap may be based on the 'raw' estimator of standard error. In the process of establishing these properties we show that the sieve bootstrap is second order correct.     

Introduction: “There Is a Person Here”: An Interview with Judith Butler

In this interview, the coeditors, along with other contributors, ask Judith Butler a variety of questions regarding queer theory, gender identities, scientific and legal discourse, bodily abjection, race and class positioning, and political organizing. This range of subject matter suggests not only the breadth of Butler's work, but also its applicability to any number of people, whose relation to theory ranges from highly politicized to politically indifferent. The interview demonstrates the responsiveness of Butler's work to cultural translation and political action.     

Peer modeling and college men's sexually impositional behavior in the laboratory

The purpose of this study was to examine the influence of peer modeling on sexually impositional behavior in the laboratory. Male participants with and without a self‐reported history of sexually aggressive behavior viewed video clips depicting nonaggressive and sexually aggressive behavior and then chose one of the clips to show to a female confederate. Half of the participants were first exposed to a male confederate who showed the sexually aggressive video clip to a female confederate. The other half of the participants were exposed to a male confederate who showed a nonaggressive video clip to a female confederate. Exposure to a male confederate who showed a sexually aggressive video clip to a female was associated with participants' choosing to engage in this same behavior. A self‐reported history of sexually aggressive behavior was also associated with participants' showing the sexually aggressive video clip in spite of believing the effect on the female viewer would be negative.     

Literacy in the modern world

Books reviewed in this article:
Clark, R. and Ivanic, R. The Politics of Writing
Hasan, Ruqaiya and Williams, Geoff (eds.). Literacy in Society (Applied Linguistics and Language Study)
Johns, Ann M. Text, Role, and Context: Developing Academic Literacies (Cambridge Applied Linguistics)
Lankshear, Colin with Gee, James Paul; Knobel, Michele and Searle, Chris. Changing Literacies
Andre´e, Tabouret-Keller; Le Page, Robert B.; Gardner-Chloros, Penelope and Varro, Gabrielle (eds.). Vernacular Literacy: A Re-evaluation (Oxford Studies in Anthropological Linguistics)     

Biased Bootstrap Methods for Reducing the Effects of Contamination

Contamination of a sampled distribution, for example by a heavy-tailed distribution, can degrade the performance of a statistical estimator. We suggest a general approach to alleviating this problem, using a version of the weighted bootstrap. The idea is to 'tilt' away from the contaminated distribution by a given (but arbitrary) amount, in a direction that minimizes a measure of the new distribution's dispersion. This theoretical proposal has a simple empirical version, which results in each data value being assigned a weight according to an assessment of its influence on dispersion. Importantly, distance can be measured directly in terms of the likely level of contamination, without reference to an empirical measure of scale. This makes the procedure particularly attractive for use in multivariate problems. It has several forms, depending on the definitions taken for dispersion and for distance between distributions. Examples of dispersion measures include variance and generalizations based on high order moments. Practicable measures of the distance between distributions may be based on power divergence, which includes Hellinger and Kullback–Leibler distances. The resulting location estimator has a smooth, redescending influence curve and appears to avoid computational difficulties that are typically associated with redescending estimators. Its breakdown point can be located at any desired value ε∈ (0, ½) simply by 'trimming' to a known distance (depending only on ε and the choice of distance measure) from the empirical distribution. The estimator has an affine equivariant multivariate form. Further, the general method is applicable to a range of statistical problems, including regression.     

ON BOOTSTRAP HYPOTHESIS TESTING

We describe methods for constructing bootstrap hypothesis tests, illustrating our approach using analysis of variance. The importance of pivotalness is discussed. Pivotal statistics usually result in improved accuracy of level. We note that hypothesis tests and confidence intervals call for different methods of resampling, so as to ensure that accurate critical point estimates are obtained in the former case even when data fail to comply with the null hypothesis. Our main points are illustrated by a simulation study and application to three real data sets.