Inferences on the Means of Two Log-Normal Distributions: A Computational Approach Test

In this article, we propose a novel approach for testing the equality of two log-normal populations using a computational approach test (CAT) that does not require explicit knowledge of the sampling distribution of the test statistic. Simulation studies demonstrate that the proposed approach can perform hypothesis testing with satisfying actual size even at small sample sizes. Overall, it is superior to other existing methods. Also, a CAT is proposed for testing about reliability of two log-normal populations when the means are the same. Simulations show that the actual size of this new approach is close to nominal level and better than the score test. At the end, the proposed methods are illustrated using two examples.     

Doing Good and Selling Goods

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This paper explores a network of organizations and their perspectives on the social enterprise commodity. Based on...     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.     

The minimum wage and poverty among full-time workers

V. Conclusions The empirical evidence is strong that minimum wages have had little or no effect on poverty in the U.S. Indeed, the evidence is stronger that minimum wages occasionally increase poverty. It also suggests that the minimum wage does not even lower poverty for the one group that, almost by definition, one would expect to be helped: full-time, year-round workers. While the empirical results suggest minimum wages do not achieve what is ostensibly their primary goal — relieving poverty among the working poor — minimum wages do seem to impose a real cost on society in terms of lost income and output. The empirical evidence on work hours suggests that a $1 increase in the minimum wage, far from being almost costless, could conceivably impose income losses to American workers in the $12-15 billion range per year — an amount equal to the “income deficit” of millions of persons counted as poor by the U.S. Bureau of the Census.     

Politics and Scientific Expertise: Scientists, Risk Perception, and Nuclear Waste Policy

To study the homogeneity and influences on scientists'perspectives of environmental risks, we have examined similarities and differences in risk perceptions, particularly regarding nuclear wastes, and policy preferences among 1011 scientists and engineers. We found significant differences ( p 0.05)in the patterns of beliefs among scientists from different fields of research. In contrast to physicists, chemists, and engineers, life scientists tend to: (a)perceive the greatest risks from nuclear energy and nuclear waste management; (b)perceive higher levels of overall environmental risk; (c)strongly oppose imposing risks on unconsenting individuals; and (d)prefer stronger requirements for environmental management. On some issues related to priorities among public problems and calls for government action, there are significant variations among life scientists or physical scientists. We also found that–independently of field of research–perceptions of risk and its correlates are significantly associated with the type of institution in which the scientist is employed. Scientists in universities or state and local governments tend to see the risks of nuclear energy and wastes as greater than scientists who work as business consultants, for federal organizations, or for private research laboratories. Significant differences also are found in priority given to environmental risks, the perceived proximity of environmental disaster, willingness to impose risks on an unconsenting population, and the necessity of accepting risks and sacrifices.     

The identification of logistic regression models with errors in the variables

The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error.