What finite-additivity can add to decision theory

Statistical Methods & Applications - We examine general decision problems with loss functions that are bounded below. We allow the loss function to assume the value $$\infty $$ . No other...     

Comparability of Mortality Estimates from Social Surveys and Vital Statistics Data in the United States

Population Research and Policy Review - Social surveys prospectively linked with death records provide invaluable opportunities for the study of the relationship between social and economic...     

Exposing the protected: Ghana’s disability laws and the rights of disabled people

Disabled people in Ghana continue to experience various forms of discrimination and social exclusion. These occur despite the fact that there are several anti-discriminatory laws that are meant to protect the rights of disabled people and facilitate their participation in mainstream social, political and economic activities. As it is, the laws have not completely eroded the discrimination and in some instances appear to even institutionalise the discrimination that disabled people experience. It is important that the state pays more attention to amending aspects of these laws and putting them into practice.     

Dose–Response Modeling: Extrapolating From Experimental Data to Real‐World Populations

Dose–response modeling of biological agents has traditionally focused on describing laboratory‐derived experimental data. Limited consideration has been given to understanding those factors that are controlled in a laboratory, but are likely to occur in real‐world scenarios. In this study, a probabilistic framework is developed that extends Brookmeyer's competing‐risks dose–response model to allow for variation in factors such as dose‐dispersion, dose‐deposition, and other within‐host parameters. With data sets drawn from dose–response experiments of inhalational anthrax, plague, and tularemia, we illustrate how for certain cases, there is the potential for overestimation of infection numbers arising from models that consider only the experimental data in isolation.     

Birds of a feather: Estimating the value of statistical life from dual-earner families

Journal of Risk and Uncertainty - Economists have long employed hedonic wage analysis to estimate income-fatality risk trade-offs, but some scholars have raised concerns about systematic...     

Boosted coefficient models

Regression methods typically construct a mapping from the covariates into the real numbers. Here, however, we consider regression problems where the task is to form a mapping from the covariates into a set of (univariate) real-valued functions. Examples are given by conditional density estimation, hazard regression and regression with a functional response. Our approach starts by modeling the function of interest using a sum of B-spline basis functions. To model dependence on the covariates, the coefficients of this expansion are each modeled as functions of the covariates. We propose to estimate these coefficient functions using boosted tree models. Algorithms are provided for the above three situations, and real data sets are used to investigate their performance. The results indicate that the proposed methodology performs well. In addition, it is both straightforward, and capable of handling a large number of covariates.     

Inference for the Identified Set in Partially Identified Econometric Models

This paper provides computationally intensive, yet feasible methods for inference in a very general class of partially identified econometric models. Let P denote the distribution of the observed data. The class of models we consider is defined by a population objective function Q(θ, P) for θ∈Θ. The point of departure from the classical extremum estimation framework is that it is not assumed that Q(θ, P) has a unique minimizer in the parameter space Θ. The goal may be either to draw inferences about some unknown point in the set of minimizers of the population objective function or to draw inferences about the set of minimizers itself. In this paper, the object of interest is Θ₀(P)=argmin_θ∈ΘQ(θ, P), and so we seek random sets that contain this set with at least some prespecified probability asymptotically. We also consider situations where the object of interest is the image of Θ₀(P) under a known function. Random sets that satisfy the desired coverage property are constructed under weak assumptions. Conditions are provided under which the confidence regions are asymptotically valid not only pointwise in P, but also uniformly in P. We illustrate the use of our methods with an empirical study of the impact of top‐coding outcomes on inferences about the parameters of a linear regression. Finally, a modest simulation study sheds some light on the finite‐sample behavior of our procedure.     

The Effect of Perceived Novelty on the Adoption of Information Technology Innovations: A Risk/Reward Perspective

A fundamental characteristic of any innovation is its novelty, the newness or freshness of the innovation in the eyes of the adopter. Past research has often considered novelty to be inherent to an information technology (IT) innovation, yet it is also likely that perceptions of novelty differ widely across individuals. Nevertheless, the role that the novelty of an IT innovation plays in adoption is not well understood. The primary goal of this research effort is to frame the perceived novelty of an IT innovation as a salient affective belief in the nomological network related to adoption. Further, we examine how perceived novelty influences the way individuals reconcile their perceptions of risk versus reward when considering the adoption of an IT innovation. Two empirical studies with 424 and 138 participants, respectively, examine the effect of perceived novelty on IT innovations from a risk/reward perspective. Results indicate that perceived novelty is a salient affective belief that plays a significant role in the adoption of IT innovations. Implications for both theory and organizational decision making are examined.     

Executive development: History lessons learned from Barnard

The purpose of this paper is to derive practical lessons from Barnard's less well-known contributions to our understanding of leadership, leadership development and executive education. The main contributions of this paper are the introduction of Bevir's logic to the leadership studies and revelation that Barnard might have established the early foundations of pragmatic leadership. The introduction of Bevir's logic from the history of ideas may be instrumental to overcoming biases toward historicism and presentism, which are latent, yet common in leadership studies. The recognition of Barnard's historical contributions to the conceptualization of leadership, leadership development, and executive education is significant. These contributions are not widely known because they are not a part of his well-known book The Functions of the Executive. Barnard has much to say about these issues and we should listen and heed his suggestions.     

The effects of leadership style on stress outcomes

The present study manipulated transformational and transactional leadership styles to examine their influence on individuals' performance on a stressful task, and on perceived social support, self-efficacy beliefs, emotions, and stressor appraisals. In addition, this study examined whether these variables mediated the relationship between leadership style and performance. Two hundred fourteen participants viewed video instructions for a stressful task presented by an actor depicting one of three leadership styles (transformational, transactional-contingent reward, and transactional-management by exception). Participants' psychological, emotional, and motivational responses to the videos were assessed prior to their engagement with the task. The transformational leadership condition was associated with enhanced task performance, higher social support perceptions, greater efficacy beliefs, lower negative affect, and lower threat appraisals compared to the transactional conditions. Causal modeling revealed that leadership style had a direct, rather than indirect, effect on task performance. The present research extends leadership research by providing an experimental evaluation of the costs/benefits of transformational and transactional leadership under stressful task conditions. Some of the results parallel those from correlational field studies, thus corroborating transformational leadership theory while other results diverge from theory, but present opportunities for future research.