Robust canonical discriminant analysis

A method for robust canonical discriminant analysis via two robust objective loss functions is discussed. These functions are useful to reduce the influence of outliers in the data. Majorization is used at several stages of the minimization procedure to obtain a monotonically convergent algorithm. An advantage of the proposed method is that it allows for optimal scaling of the variables. In a simulation study it is shown that under the presence of outliers the robust functions outperform the ordinary least squares function, both when the underlying structure is linear in the variables as when it is nonlinear. Furthermore, the method is illustrated with empirical data.The research of the first author was supported by the Netherlands Organization of Scientific Research (NWO grant 560-267-029).     

Robustness in the Pareto‐solutions for the multi‐criteria minisum location problem

In this paper, a new trend is introduced into the field of multi‐criteria location problems. We combine the robustness approach using the minmax regret criterion together with Pareto‐optimality. We consider the multi‐criteria squared Euclidean minisum location problem which consists of simultaneously minimizing a number of weighted sum‐distance functions and the set of Pareto‐optimal locations as its solution concept. The Pareto‐optimal solutions for the set of robust locations with respect to the original weighted sum‐distance functions is completely characterized. These Pareto‐optimal solutions have both the properties of stability and non‐domination which are required in robust and multi‐criteria programming. Copyright © 2001 John Wiley & Sons, Ltd.     

Gapping

Tukey's scheme for finding separations in univariate data strings is described and tested. It is found that one can use the size of a data gap coupled with its ordinal position in the distribution to determine the likelihood of its having arisen by chance. It was also shown that this scheme is relatively robust for fatter-tailed-than-Gaussian distributions and has some interesting implications in multidimensional situations.     

Bayesian Gaussian distributional regression models for more efficient norm estimation

A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost-efficient high-quality norms. The method is illustrated using empirical normative data from the IDS-2 intelligence test.     

Attributionism and Counterfactual Robustness

In this journal (AJP 2016), Vishnu Sridharan presents a novel objection to attributionism, the view according to which agents are responsible for their conduct when it reflects who they are or what they value. The key to Sridharan's objection is that agents can fulfil all attributionist conditions for responsibility while being under the control of a manipulator. In this paper, we show that Sridharan's objection falls prey to a dilemma—either his manipulator is counterfactually robust, or she is not—and that neither of its horns undermines attributionism.     

Mean Comparison: Manifest Variable Versus Latent Variable

Mean comparisons are of great importance in the application of statistics. Procedures for mean comparison with manifest variables have been well studied. However, few rigorous studies have been conducted on mean comparisons with latent variables, although the methodology has been widely used and documented. This paper studies the commonly used statistics in latent variable mean modeling and compares them with parallel manifest variable statistics. Our results indicate that, under certain conditions, the likelihood ratio and Wald statistics used for latent mean comparisons do not always have greater power than the Hotelling T² statistics used for manifest mean comparisons. The noncentrality parameter corresponding to the T² statistic can be much greater than those corresponding to the likelihood ratio and Wald statistics, which we find to be different from those provided in the literature. Under a fixed alternative hypothesis, our results also indicate that the likelihood ratio statistic can be stochastically much greater than the corresponding Wald statistic. The robustness property of each statistic is also explored when the model is misspecified or when data are nonnormally distributed. Recommendations and advice are provided for the use of each statistic. The research was supported by NSF grant DMS-0437167 and Grant DA01070 from the National Institute on Drug Abuse. We would like to thank three referees for suggestions that helped in improving the paper.     

Asymptotic efficiency of the pseudo‐maximum likelihood estimator in multi‐group factor models with pooled data

A multi‐group factor model is suitable for data originating from different strata. However, it often requires a relatively large sample size to avoid numerical issues such as non‐convergence and non‐positive definite covariance matrices. An alternative is to pool data from different groups in which a single‐group factor model is fitted to the pooled data using maximum likelihood. In this paper, properties of pseudo‐maximum likelihood (PML) estimators for pooled data are studied. The pooled data are assumed to be normally distributed from a single group. The resulting asymptotic efficiency of the PML estimators of factor loadings is compared with that of the multi‐group maximum likelihood estimators. The effect of pooling is investigated through a two‐group factor model. The variances of factor loadings for the pooled data are underestimated under the normal theory when error variances in the smaller group are larger. Underestimation is due to dependence between the pooled factors and pooled error terms. Small‐sample properties of the PML estimators are also investigated using a Monte Carlo study.     

A Unified Framework for the Prioritization of Organ Transplant Patients: Analytic Hierarchy Process,Sensitivity and Multifactor Robustness Study

Fair and equitable allocation of donor organs in the USA is a daunting yet necessary task, which can mean the difference between life and death for patients on transplant waiting lists. This paper proposes a multi‐criterion decision‐making model using the analytic hierarchy process to allow for an allocation decision to be made on the basis of urgency, efficiency, benefit and equity. These four perspectives and objectives can be broken down into both quantitative and qualitative measures, which can be easily combined and weighted through group consensus. The proposed model overcomes the limitations of a single type of system, integrates the views of many organ allocation philosophies, improves the decision maker's ability to collaborate, helps justify the decision and reaches the optimal result. In addition, the proposed profile matrix allows decision makers to graphically trade off criteria against each other and to clearly articulate the decision rationale. Our computational study suggests that the proposed model not only satisfactorily serves the objectives of many constituents, but also remains noticeably robust under various criteria‐weight‐change scenarios. It improves stakeholder confidence in the organ allocation procedure, maximizes the usefulness of the organ and enhances welfare to society. Copyright © 2012 John Wiley & Sons, Ltd.     

The robustness of the usual correction for restriction in range due to explicit selection

Corrections for restriction in range due to explicit selection assume the linearity of regression and homoscedastic array variances. This paper develops a theoretical framework in which the effects of some common forms of violation of these assumptions on the estimation of the unrestricted correlation can be investigated. Simple expressions are derived for both the restricted and corrected correlations in terms of the target (unrestricted) correlation in these situations.The author is grateful to D. Holt, C. J. Skinner and T. M. F. Smith (all University of Southampton) for their helpful comments. Research was initially supported by grant No. HR7152 from the Economic and Social Research Council.     

Approximate confidence intervals for a robust scale parameter

A recent paper by Wainer and Thissen has renewed the interest in Gini's mean difference,G, by pointing out its robust characteristics. This note presents distribution-free asymptotic confidence intervals for its population value,γ, in the one sample case and for the difference Δ=(γ ₁?γ ₂) in the two sample situations. Both procedures are based on a technique of jackknifingU-statistics developed by Arvesen.