An Adaptive,Rate‐Optimal Test of a Parametric Mean‐Regression Model Against a Nonparametric Alternative

We develop a new test of a parametric model of a conditional mean function against a nonparametric alternative. The test adapts to the unknown smoothness of the alternative model and is uniformly consistent against alternatives whose distance from the parametric model converges to zero at the fastest possible rate. This rate is slower than n^−1/2. Some existing tests have nontrivial power against restricted classes of alternatives whose distance from the parametric model decreases at the rate n^−1/2. There are, however, sequences of alternatives against which these tests are inconsistent and ours is consistent. As a consequence, there are alternative models for which the finite‐sample power of our test greatly exceeds that of existing tests. This conclusion is illustrated by the results of some Monte Carlo experiments.     

Identification of Standard Auction Models

This paper presents new identification results for models of first–price, second–price, ascending (English), and descending (Dutch) auctions. We consider a general specification of the latent demand and information structure, nesting both private values and common values models, and allowing correlated types as well as ex ante asymmetry. We address identification of a series of nested models and derive testable restrictions enabling discrimination between models on the basis of observed data. The simplest model—symmetric independent private values—is nonparametrically identified even if only the transaction price from each auction is observed. For richer models, identification and testable restrictions may be obtained when additional information of one or more of the following types is available: (i) the identity of the winning bidder or other bidders; (ii) one or more bids in addition to the transaction price; (iii) exogenous variation in the number of bidders; (iv) bidder–specific covariates. While many private values (PV) models are nonparametrically identified and testable with commonly available data, identification of common values (CV) models requires stringent assumptions. Nonetheless, the PV model can be tested against the CV alternative, even when neither model is identified.     

Implants and ethnocide: learning from the cochlear implant controversy

This paper uses the fictional case of the ‘Babel fish’ to explore and illustrate the issues involved in the controversy about the use of cochlear implants in prelinguistically deaf children. Analysis of this controversy suggests that the development of genetic tests for deafness poses a serious threat to the continued flourishing of Deaf culture. I argue that the relationships between Deaf and hearing cultures that are revealed and constructed in debates about genetic testing are themselves deserving of ethical evaluation. Making good policy about genetic testing for deafness will require addressing questions in political philosophy and anthropology about the value of culture and also thinking hard about what sorts of experiences and achievements make a human life worthwhile.     

Assessing additivity in nonparametric models —A kernel‐based method

In this article, we address the testing problem for additivity in nonparametric regression models. We develop a kernel‐based consistent test of a hypothesis of additivity in nonparametric regression, and establish its asymptotic distribution under a sequence of local alternatives. Compared to other existing kernel‐based tests, the proposed test is shown to effectively ameliorate the influence from estimation bias of the additive component of the nonparametric regression, and hence increase its efficiency. Most importantly, it avoids the tuning difficulties by using estimation‐based optimal criteria, while there is no direct tuning strategy for other existing kernel‐based testing methods. We discuss the usage of the new test and give numerical examples to demonstrate the practical performance of the test. The Canadian Journal of Statistics 39: 632–655; 2011. © 2011 Statistical Society of Canada     

Simultaneous semi-sequential testing of dual alternatives for pattern recognition

In this paper, we propose a new nonparametric simultaneous test for dual alternatives. Simultaneous tests for dual alternatives are used for pattern detection of arsenic contamination level in ground water. We consider two possible patterns, namely, monotone shift and an umbrella-type location alternative, as the dual alternatives. Pattern recognition problems of this nature are addressed in Bandyopadhyay et al. [5], stretching the idea of multiple hypotheses tests as in Benjamini and Hochberg [6]. In the present context, we develop an alternative approach based on contrasts that helps us to detect three underlying pattern much more efficiently. We illustrate the new methodology through a motivating example related to highly sensitive issue of arsenic contamination in ground water. We provide some Monte-Carlo studies related to the proposed technique and give a comparative study between different detection procedures. We also obtain some related asymptotic results.     

Improving OpenURL Menus: User Testing of Revisions to SFX® Menus

Twenty-one volunteers tested the usability of revisions to the Texas A&M University Libraries' SFX® OpenURL link resolver menus, including the addition of Ex Libris' new bX™ recommendation service and a plug-in which pulls additional information about the journal into the menu. The volunteers also evaluated the quality and desirability of the bX recommendations and discussed their preferences for help options and full-text format. Results of the usability testing are reported along with the resultant menu changes. This study will be of interest to librarians implementing or redesigning OpenURL menus as well as those interested in the user experience.     

Evaluating surrogate variables for improving microarray multiple testing inference

The use of surrogate variables has been proposed as a means to capture, for a given observed set of data, sources driving the dependency structure among high-dimensional sets of features and remove the effects of those sources and their potential negative impact on simultaneous inference. In this article we illustrate the potential effects of latent variables on testing dependence and the resulting impact on multiple inference, we briefly review the method of surrogate variable analysis proposed by Leek and Storey (PNAS 2008; 105:18718-18723), and assess that method via simulations intended to mimic the complexity of feature dependence observed in real-world microarray data. The method is also assessed via application to a recent Merck microarray data set. Both simulation and case study results indicate that surrogate variable analysis can offer a viable strategy for tackling the multiple testing dependence problem when the features follow a potentially complex correlation structure, yielding improvements in the variability of false positive rates and increases in power.     

Methods to Distinguish Between Polynomial and Exponential Tails

Two methods to distinguish between polynomial and exponential tails are introduced. The methods are based on the properties of the residual coefficient of variation for the exponential and non‐exponential distributions. A graphical method, called a CV‐plot, shows departures from exponentiality in the tails. The plot is applied to the daily log‐returns of exchange rates of US dollar and Japanese yen. New statistics are introduced for testing the exponentiality of tails using multiple thresholds. They give better control of the significance level than previous tests. The powers of the new tests are compared with those of some others for various sample sizes.     

Bayes Factor Consistency for One-way Random Effects Model

In this article, we consider Bayesian hypothesis testing for the balanced one-way random effects model. A special choice of the prior formulation for the ratio of variance components is shown to yield an explicit closed-form Bayes factor without integral representation. Furthermore, we study the consistency issue of the resulting Bayes factor under three asymptotic scenarios: either the number of units goes to infinity, the number of observations per unit goes to infinity, or both go to infinity. Finally, the behavior of the proposed approach is illustrated by simulation studies.     

Prediction of Times to Failure of Censored Items for a Simple Step-Stress Model with Regular and Progressive Type I Censoring from the Exponential Distribution

The problem of predicting times to failure of units from the Exponential Distribution which are censored under a simple step-stress model is considered in this article. We discuss two types of censoring—regular and progressive Type I—and two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP) for each type of censoring. Numerical examples are used to illustrate the prediction methods. Using simulation studies, mean squared prediction error (MSPE) and prediction intervals are generated for these examples. MLP and the CMP are then compared with respect to MSPE and the prediction interval.