Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time

In this article we study the problem of classification of three-level multivariate data, where multiple q$q$-variate observations are measured on u$u$-sites and over p$p$-time points, under the assumption of multivariate normality. The new classification rules with certain structured and unstructured mean vectors and covariance structures are very efficient in small sample scenario, when the number of observations is not adequate to estimate the unknown variance–covariance matrix. These classification rules successfully model the correlation structure on successive repeated measurements over time. Computation algorithms for maximum likelihood estimates of the unknown population parameters are presented. Simulation results show that the introduction of sites in the classification rules improves their performance over the existing classification rules without the sites.     

Optimal and lead-in adaptive allocation for binary outcomes: A comparison of Bayesian methods

We compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered. Simulation studies show that efficacy comparisons lead to more adaptation than center comparisons, though at some power loss, skeptically predictive efficacy comparisons and natural lead-in approaches lead to less adaptation but offer reduced allocation variability. Though nuanced, these results help clarify the power-adaptation trade-off in adaptive randomization.     

A Theoretical Comparison of Relational Maintenance and Closeness as Mediators of Family Communication Patterns in Parent-Child Relationships

This investigation sought to elaborate Koerner and Fitzpatrick’s theory of family communication by testing two theoretical models of family communication patterns (FCP), child-to-parent inclusion of the other in the self (IOS), and relational maintenance. Participants included 374 young adults recruited from two universities in different regions of the United States. Results best supported a model positioning maintenance as a mediator of the association between FCP and IOS, as compared to an alternative model with IOS as a mediator of the association between FCP and maintenance. These findings extend Koerner and Fitzpatrick’s theory by highlighting communication as the means by which overarching family schemas influence relationship-specific schemas; additionally, they commend the self-expansion model as a useful theoretical approach to relational maintenance behavior in family contexts.     

A New Kuznetsian Dynamic: The Knowledge Economy and Income Inequality in the United States, 1917–2008

The rise of the knowledge economy resulted in higher levels of income inequality in the United States and forced many to question the Kuznets Inverted‐U hypothesis. However, this study argues that the establishment of a knowledge economy does not negate the importance of employment shifts for income inequality. Instead, the expansion of knowledge employment alters the major sectors that are responsible for the overall distribution of income. To this end, this article presents the key argument that the current service–knowledge transition impacts income inequality trends, of today, in a way that is similar to the agricultural–industrial transition, of the past. According to the autoregressive conditional heteroskedasticity regressions, the agricultural–industrial transition returns stronger associations with income inequality in the United States before 1950. The agricultural–industrial transition's impact diminishes thereafter as the service–knowledge transition shares a more robust association with income inequality after 1980.     

On evaluating interactive statistical program packages

Most statistical computing for data analysis has come to depend upon statistical program packages. In recent years, interactive computing has become widespread both on large time-sharing systems and on mini-computers. This paper identifies ways in which interactive statistical software packages differ from batchoriented software and discusses evaluation considerations pertaining specifically to interactive packages.     

Bridging the awful gap between strategy and action

This article describes the lessons learned from a study carried out within British Telecommunications as part of the author's doctoral research programme. It was found that strategy was formulated and implemented, with direct action taken and resources commited, by Divisional managers at all three organizational levels in BT so that implementation did not always follow the lines intended by the HQ strategic planners. Implementation was found to be an interactive rather than a rational/sequential process. Marked variations in practice were observed and explanations for these are offered. The relative success of the strategies differed widely, both overall and within the divisional field units. The manner of implementation and factors managers perceived to help and hinder it were studied. Success or failure was felt to hinge on getting a few basics right: resources, organizational ‘fit’, historical performance and the expectations it generated (track record), information and support, market acceptance, technical competence, consistent goals and top management support.