Bootstrap methods for multivariate hypothesis testing

The nonparametric and parametric bootstrap methods for multivariate hypothesis testing are developed. They are used to approximate the null distribution of the test statistics proposed by Duchesne and Francq (2015 Duchesne, P., Francq, C. (2015). Multivariate hypothesis testing using generalized and {2}-inverses—with applications. Statistics 49:475–496.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), resulting in bootstrap testing procedures. In the problem of testing for the mean vector of a multivariate distribution, the asymptotic validity of the bootstrap methods is proved. The finite sample performance of the new solutions is demonstrated by means of Monte Carlo simulation studies. They indicate that for small-sample size, the bootstrap tests provide a better finite sample properties than the asymptotic tests considered by Duchesne and Francq (2015 Duchesne, P., Francq, C. (2015). Multivariate hypothesis testing using generalized and {2}-inverses—with applications. Statistics 49:475–496.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]).     

Psychological Testing in Child Custody Evaluations With Ethnically Diverse Families: Ethical Concerns and Practice Recommendations

Given increasing ethnic diversity of divorcing families, it is more important than ever for family court evaluators to understand the ethical concerns and principles underlying the use of psychological testing with ethnic family members. Major professional organizations have promulgated the guidelines of child custody evaluation with an imperative to consider cultural factors in the evaluation. However, there is a paucity of literature that addresses ethnicity and psychological testing in child custody evaluations. This article addresses three major ethical domains: (a) competence, (b) test fairness, and (c) test interpretation when psychological tests are used. Practice recommendations provide guidelines to ensure ethical standards and competence in using psychological testing with ethnically diverse families in child custody and other family court evaluations.     

State work and the testing concours of citizenship

Anyone trying to be a citizen has to pass through a set of practices trying to be a state. This paper investigates some of the ways testing practices calibrate citizens, and in doing so, perform “the state.” The paper focuses on three forms of citizenship testing, which it considers exemplary forms of “state work,” and which all, in various ways, concern “migration.” First, the constitution of a “border crossing,” which requires an identity test configured by deceptibility. Second, the Dutch asylum process, in which “being gay” can, in certain cases, be reason for being granted asylum, but where “being gay” is also the outcome of an examination organized by suspicion. And third, the Dutch measurement of immigrants’ “integration,” which is comprised of a testing process in which such factishes as “being a member of society” and “being modern” surface. Citizenship is analyzed in this paper as accrued and (re)configured along a migration trajectory that takes shape as a testing concours, meaning that subjects become citizens along a trajectory of testing practices. In contributing both to work on states and citizenship, and to work on testing, this paper thus puts forward the concept of citizenship testing as state work, where “state work is the term for that kind of labor that most knows itself as comparison, equivalency, and exchange in the social realm” (Harney, 2002, pp. 10–11). Throughout the testing practices discussed here, comparison, equivalency, and exchange figure prominently as the practical achievements of crafting states and citizens.     

Underground testing: Name-altering practices as probes in electronic music

Name-altering practices are common in many creative fields—pen names in literature, stage names in the performing arts, and aliases in music. More than just reflecting artistic habits or responding to the need for distinctive brands, these practices can also serve as test devices to probe, validate, and guide the artists’ active participation in a cultural movement. At the same time, they constitute a powerful probe to negotiate the boundaries of a subculture, especially when its features are threatened by appropriation from the mass-oriented culture. Drawing evidence from electronic music, a field where name-altering practices proliferate, we outline dynamics of pseudonymity, polyonymy, and anonymity that surround the use of aliases. We argue that name-altering practices are both a tool that artists use to probe the creative environment and a device to recursively put one’s creative participation to the test. In the context of creative subcultures, name-altering practices constitute a subtle but effective form of underground testing.     

Projection‐Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments

It is well known that standard asymptotic theory is not applicable or is very unreliable in models with identification problems or weak instruments. One possible way out consists of using a variant of the Anderson–Rubin ((1949), AR) procedure. The latter allows one to build exact tests and confidence sets only for the full vector of the coefficients of the endogenous explanatory variables in a structural equation, but not for individual coefficients. This problem may in principle be overcome by using projection methods (Dufour (1997), Dufour and Jasiak (2001)). At first sight, however, this technique requires the application of costly numerical algorithms. In this paper, we give a general necessary and sufficient condition that allows one to check whether an AR‐type confidence set is bounded. Furthermore, we provide an analytic solution to the problem of building projection‐based confidence sets from AR‐type confidence sets. The latter involves the geometric properties of “quadrics” and can be viewed as an extension of usual confidence intervals and ellipsoids. Only least squares techniques are needed to build the confidence intervals.     

Wavelet‐Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet‐based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one‐way or two‐way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data‐driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data‐driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.     

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.     

The Value of a Statistical Life for Risk‐Averse and Risk‐Seeking Individuals

This article estimates the value of a statistical life (VSL) for Chile under the hedonic wage method while accounting for individual risk preferences. Two alternative measures of risk aversion are used. First, risk aversion is directly measured using survey measures of preferences over hypothetical gambles, and second, over observed individual behaviors that may proxy for risk preferences, such as smoking status, are used. I reconcile the results with a theoretical model of economic behavior that predicts how the wage‐risk tradeoff changes as risk aversion differs across individuals. The VSL estimates range between 0.61 and 8.68 million dollars. The results using smoking behavior as a proxy for risk attitudes are consistent with previous findings. However, directly measuring risk aversion corrects the wage‐risk tradeoff estimation bias in the opposite direction. The results are robust to other observed measures of risk aversion such as drinking behavior and stock investments. Results suggest that, consistent with the literature that connects smoking behavior with labor market outcomes, smoking status could be capturing poor health productivity effect in addition to purely risk preferences.     

Incremental Sampling Methodology: Applications for Background Screening Assessments

This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two‐sample t ‐tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.