Efficient designs based on orthogonal arrays of type I and type II for experiments using units ordered over time or space

For a wide variety of applications, experiments are based on units ordered over time or space. Models for these experiments generally may include one or more of: correlations, systematic trends, carryover effects and interference effects. Since the standard optimal block designs may not be efficient in these situations, orthogonal arrays of type I and type II, which were introduced in 1961 by C.R. Rao [Combinatorial arrangements analogous to orthogonal arrays, Sankhya A 23 (1961) 283–286], have been recently used to construct optimal and efficient designs for many of these experiments. Results in this area are unified and the salient features are outlined.     

Consent and Informed Consent: Their Ongoing Evolutions in Clinical Care and Research on Humans

The dual concepts of 'consent' and 'informed consent' continue to have three evolutions. The primary evolution of consent in the patient–physician relationship began in Great Britain in 1767 in the British case, Slater v. Baker and Stapleton , in the judge-made law of consent. The term 'informed consent' within the patient–physician relationship entered the judicial lexicon in the 1957 California appellate case, Salgo v. Leland Stanford Junior University . In its second evolution within research on humans following the Nuremberg trials that included experimental atrocities on humans, there is a key focus on clarifying the purpose of research and specifying the reasons for the need for an even more extensive risk disclosure to individuals considering volunteering for study participation. This second evolution continues with the further refinement of the Declaration of Helsinki and, within the USA, a focus on the Belmont Report . In its third evolution in research in the social science, there has been a recognition of problems with informed consent to questionnaire research. When questionnaires involve patients with moderate or severe posttraumatic stress disorder or abused individuals, there needs to be intense consideration focused on how to best protect the participants with these conditions during the questionnaire study.     

Secondary analysis of dangerousness among death sentenced capital murderers

The theory of incapacitation involves reducing an offender's ability or capacity to commit further crimes. Capital punishment accomplishes this goal. An executed murderer never murders again. However, we do not execute all murderers, only capital murderers. This policy produces several research questions. Do capital murderers present a special risk to society? Are capital murderers more likely to murder or commit other violent crimes again than other murderers or the average citizen? To answer these questions, many states require a prediction of future dangerousness of a newly convicted murderer. To what extent has the judgment of future dangerousness matched actuarial data of subsequent murders and serious crimes? Using a secondary analysis, this investigation attempted to assemble available data of postconviction dangerousness of death sentenced capital murderers to create a more comprehensive actuarial account of subsequent dangerousness and to present the data in a common format used by the Federal Bureau of Investigation and the Bureau of Justice Statistics. Across 14 studies identified with relevant data, there were 13 instances of subsequent murder and 462 serious crime or prison rule violations.     

Form Perception of Partly Occluded Shapes in 4‐Month‐Old Infants

Two habituation experiments were conducted to investigate how 4‐month‐old infants perceive partly occluded shapes. In the first experiment, we presented a simple, partly occluded shape to the infants until habituation was reached. Then we showed either a probable completion (one that would be predicted on the basis of both local and global cues) or an improbable completion. Longer looking times were found for the improbably completed shape (compared to probable and control conditions), suggesting that the probable shape was perceived during partial occlusion. In the second experiment, infants were habituated to more ambiguous partly occluded shapes, where local and global cues would result in different completions. For adults, the percept of these shapes is usually dominated by global influences. However, after habituation the infants looked longer at the globally completed shapes. These results suggest that by the age of 4 months, infants are able to infer the perceptual completion of partly occluded shapes, but for more ambiguous shapes, this completion seems to be dominated by local influences.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Quadratic estimators of covariance components in a multivariate mixed linear model

It is known that the Henderson Method III (Biometrics 9:226–252, 1953) is of special interest for the mixed linear models because the estimators of the variance components are unaffected by the parameters of the fixed factor (or factors). This article deals with generalizations and minor extensions of the results obtained for the univariate linear models. A MANOVA mixed model is presented in a convenient form and the covariance components estimators are given on finite dimensional linear spaces. The results use both the usual parametric representations and the coordinate-free approach of Kruskal (Ann Math Statist 39:70–75, 1968) and Eaton (Ann Math Statist 41:528–538, 1970). The normal equations are generalized and it is given a necessary and sufficient condition for the existence of quadratic unbiased estimators for covariance components in the considered model.