REGRESSION METHODOLOGY WITH GROSS OBSERVATION ERRORS IN THE EXPLANATORY VARIABLES***

The robustness of linear programming regression estimators is examined where the disturbance terms are normally distributed and there are observation errors in the explanatory variables. These errors are occasional gross biases between one set of observations and another. The simulation of short series data offers preliminary evidence that when these biases have a non-zero mean, MSAE estimation is more robust than least squares.     

Peer Victimization,Self‐esteem,and Ego Resilience Types in Adolescents: A Prospective Analysis of Person‐context Interactions

This study examined bidirectional, longitudinal associations between peer victimisation and self‐esteem in adolescents, and tested for moderator effects of undercontrolling, overcontrolling, and ego‐resilient personality types in these associations. Data were used from 774 adolescents ages 11–16 years who participated in a three‐wave (i.e., 2005, 2006, and 2007) longitudinal study. Structural equation modelling analyses in Mplus demonstrated that, controlling for earlier levels of self‐esteem, self‐reported peer victimization was associated with lower self‐esteem across one‐year time intervals. Vice versa, however, low self‐esteem was not predictive of subsequent self‐reported victimization. Evidence was also found for a moderator effect of personality type on the longitudinal associations between self‐esteem and victimization. Only in the subgroup of overcontrolling adolescents was lower self‐esteem related to subsequently higher levels of peer victimization; their undercontrolling and ego‐resilient peers were unaffected.     

Tests for assessment of agreement using probability criteria

For the assessment of agreement using probability criteria, we obtain an exact test, and for sample sizes exceeding 30, we give a bootstrap-t$t$ test that is remarkably accurate. We show that for assessing agreement, the total deviation index approach of Lin [2000. Total deviation index for measuring individual agreement with applications in laboratory performance and bioequivalence. Statist. Med. 19, 255–270] is not consistent and may not preserve its asymptotic nominal level, and that the coverage probability approach of Lin et al. [2002. Statistical methods in assessing agreement: models, issues and tools. J. Amer. Statist. Assoc. 97, 257–270] is overly conservative for moderate sample sizes. We also show that the nearly unbiased test of Wang and Hwang [2001. A nearly unbiased test for individual bioequivalence problems using probability criteria. J. Statist. Plann. Inference 99, 41–58] may be liberal for large sample sizes, and suggest a minor modification that gives numerically equivalent approximation to the exact test for sample sizes 30 or less. We present a simple and accurate sample size formula for planning studies on assessing agreement, and illustrate our methodology with a real data set from the literature.     

Easy-to-construct confidence bands for comparing two simple linear regression lines

This paper shows how to construct confidence bands for the difference between two simple linear regression lines. These confidence bands provide directly the information on the magnitude of the difference between the regression lines over an interval of interest and, as a by-product, can be used as a formal test of the difference between the two regression lines. Various different shapes of confidence bands are illustrated, and particular attention is paid towards confidence bands whose construction only involves critical points from standard distributions so that they are consequently easy to construct.     

Generalized inferences on the common mean vector of several multivariate normal populations

The hypothesis testing and confidence region are considered for the common mean vector of several multivariate normal populations when the covariance matrices are unknown and possibly unequal. A generalized confidence region is derived using the concepts of generalized method based on the generalized p$p$-value. The generalized confidence region is illustrated with two numerical examples. The merits of the proposed method are numerically compared with those of existing methods with respect to their expected area or expected d-dimensional volumes and coverage probabilities under different scenarios.     

Bayesian inference for dynamic social network data

We consider a continuous-time model for the evolution of social networks. A social network is here conceived as a (di-) graph on a set of vertices, representing actors, and the changes of interest are creation and disappearance over time of (arcs) edges in the graph. Hence we model a collection of random edge indicators that are not, in general, independent. We explicitly model the interdependencies between edge indicators that arise from interaction between social entities. A Markov chain is defined in terms of an embedded chain with holding times and transition probabilities. Data are observed at fixed points in time and hence we are not able to observe the embedded chain directly. Introducing a prior distribution for the parameters we may implement an MCMC algorithm for exploring the posterior distribution of the parameters by simulating the evolution of the embedded process between observations.     

Principles for Conduct of Pest Risk Analyses: Report of an Expert Workshop

The North American Free Trade Agreement (NAFTA) and the General Agreement on Tariffs and Trade (GATT) have focused attention on risk assessment of potential insect, weed, and animal pests and diseases of livestock. These risks have traditionally been addressed through quarantine protocols ranging from limits on the geographical areas from which a product may originate, postharvest disinfestation procedures like fumigation, and inspections at points of export and import, to outright bans. To ensure that plant and animal protection measures are not used as nontariff trade barriers, GATT and NAFTA require pest risk analysis (PRA) to support quarantine decisions. The increased emphasis on PRA has spurred multiple efforts at the national and international level to design frameworks for the conduct of these analyses. As approaches to pest risk analysis proliferate, and the importance of the analyses grows, concerns have arisen about the scientific and technical conduct of pest risk analysis. In January of 1997, the Harvard Center for Risk Analysis (HCRA) held an invitation-only workshop in Washington, D.C. to bring experts in risk analysis and pest characterization together to develop general principles for pest risk analysis. Workshop participants examined current frameworks for PRA, discussed strengths and weaknesses of the approaches, and formulated principles, based on years of experience with risk analysis in other setting and knowledge of the issues specific to analysis of pests. The principles developed highlight the both the similarities of pest risk analysis to other forms of risk analysis, and its unique attributes.     

Fostering Risk Taking in Research and Development: The Importance of a Project's Terminal Value

Large firms face a conflict in managing a portfolio of high-risk projects. When an ongoing project is thought to have a low likelihood of success, project team members take risks to improve its chances of success. However, upper-level managers who allocate resources tend to withhold resources from a project with a low likelihood of success in favor of others in the portfolio that look more promising. Because this paucity of resources influences project team members to avoid risk, the total effect of success likelihood on risk taking is conflicted. The influence on risk taking of a project's terminal value—defined as the value that remains in the firm in the event of project failure—is unequivocally positive, because both senior management resource allocation and project team risk-taking propensity are encouraged by terminal value. Thus, firms can override the ambivalent effect of likelihood of success on project decision making by focusing attention on a project's terminal value.