Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels

ABSTRACT

This article argues that researchers do not need to completely abandon the p-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of α, the probability of rejecting a true null hypothesis, and β, the probability of failing to reject a false null, instead of fixing α and minimizing β. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests.     

Three-dimensional virtual reality: Applications to the 12 grand challenges of social work

Abstract

The American Academy of Social Work and Social Welfare (AASWSW) identified 12 Grand Challenges of Social Work to unify the profession with focused research, practice and policy applications for the most pressing social issues. Virtual Reality (VR), specifically three-dimensional immersive computer-generated environments, has a history of research and applications to address social and behavioral problems. VR is becoming more readily available, as the technology is becoming more common in mainstream platforms such as mobile technology. While social work interventions are often tailored for vulnerable populations that may not have access to VR computing, it appears that virtual reality is gaining accessibility with these advances. Within the framework of the 12 Grand Challenges of Social Work, researchers from three social work virtual reality laboratories in the United States, Texas State University, the University of Houston, and the University of Alabama, review applications previously tested and currently in development to focus future research and intervention in social work practice.     

Bayesian significance test for discriminating between survival distributions

An evaluation of FBST, Fully Bayesian Significance Test, restricted to survival models is the main objective of the present paper. A Survival distribution should be chosen among the tree celebrated ones, lognormal, gamma, and Weibull. For this discrimination, a linear mixture of the three distributions is an important tool: the FBST is used to test the hypotheses defined on the mixture weights space. Another feature of the paper is that all three distributions are reparametrized in that all the six parameters are written as functions of the mean and the variance of the population been studied. Some numerical results from simulations with some right-censored data are considered.     

Landslide Risk Models for Decision Making

This contribution presents a quantitative procedure for landslide risk analysis and zoning considering hazard, exposure (or value of elements at risk), and vulnerability. The method provides the means to obtain landslide risk models (expressing expected damage due to landslides on material elements and economic activities in monetary terms, according to different scenarios and periods) useful to identify areas where mitigation efforts will be most cost effective. It allows identifying priority areas for the implementation of actions to reduce vulnerability (elements) or hazard (processes). The procedure proposed can also be used as a preventive tool, through its application to strategic environmental impact analysis (SEIA) of land-use plans. The underlying hypothesis is that reliable predictions about hazard and risk can be made using models based on a detailed analysis of past landslide occurrences in connection with conditioning factors and data on past damage. The results show that the approach proposed and the hypothesis formulated are essentially correct, providing estimates of the order of magnitude of expected losses for a given time period. Uncertainties, strengths, and shortcomings of the procedure and results obtained are discussed and potential lines of research to improve the models are indicated. Finally, comments and suggestions are provided to generalize this type of analysis.     

VOLUNTARY ACTIVITIES AND DAILY HAPPINESS IN THE UNITED STATES

This article analyzes differences in daily happiness between those individuals in the United States who perform voluntary activities during the day and those who do not. Using the Well‐Being Module of the American Time Use Survey 2010, we find that those who devote any time to voluntary activities during the day report higher levels of daily happiness than those who do not. Comparing the happiness obtained from a range of activities, we find that volunteering is among the most enjoyable, indicating that time spent on voluntary activities is utility‐enhancing. We document that the effect of voluntary activities on the experienced utility of individuals can be decomposed into a “time‐composition” effect and an “activity” effect, with the latter explaining between 11% and 46% of the observed difference. (JEL D13, J16, J22)     

A semi-parametric Bayesian extreme value model using a Dirichlet process mixture of gamma densities

In this paper, we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible for posterior density estimation and posterior inference for high quantiles. The model works well even for small sample sizes and in the absence of prior information. We evaluate the performance of the proposed model through a simulation study. Finally, the proposed model is applied to a real environmental data.     

Agency in Fertility Decisions in Western Europe During the Demographic Transition: A Comparative Perspective

We use a set of linked reproductive histories taken from Sweden, the Netherlands, and Spain for the period 1871–1960 to address key issues regarding how reproductive change was linked specifically to mortality and survivorship and more generally to individual agency. Using event-history analysis, this study investigates how the propensity to have additional children was influenced by the number of surviving offspring when reproductive decisions were made. The results suggest that couples were continuously regulating their fertility to achieve reproductive goals. Families experiencing child fatalities show significant increases in the hazard of additional births. In addition, the sex composition of the surviving sibset also appears to have influenced reproductive decisions in a significant but changing way. The findings offer strong proof of active decision-making during the demographic transition and provide an important contribution to the literature on the role of mortality for reproductive change.     

DO WOMEN PAY MORE FOR CREDIT? EVIDENCE FROM ITALY

By using a unique and large data set on loan contracts between banks and microfirms, we find robust evidence that women in Italy pay more for credit than men, although we do not find any evidence that women borrowers are riskier than men. The male/female differential remains even after controlling for a large number of characteristics of the type of business, the borrower, and the structure of the credit market. The result is not driven by lack of credit history, nor by women using a different type of bank than men, since the same bank charges different rates to male and female borrowers.