Estimating the effectiveness of a misdemeanor drug diversion program using propensity score matching and survival analyses

This study analyzes three years of data on misdemeanor drug offenders in Winnebago County, Wisconsin. A portion of these offenders opted into a Misdemeanor Drug Diversion Program (MDDP) offered instead of traditional adjudication. Recidivism in the treatment and comparison groups is estimated using standard binary response techniques augmented with propensity score matching to address selection bias. Results show that the MDDP reduces the probability of re-offense by 16%, after adjusting for possible selection bias. Cox proportional hazard modeling is also used to assess time-to-re-offense differentials between the treatment and comparison groups. The survival analysis indicates that the hazard rate of re-offense is 60% lower per day among those treated with the MDDP program than those who did not complete the program. The average number of days to re-offense among those that do re-offend is 297 days in the treatment group and 203 days in the comparison group.     

Economic design of acceptance sampling plans for truncated life test using Frechet distribution

In this article designing of acceptance sampling plans for the truncated life test is proposed using economic approach by assuming that the lifetime of a product follows Frechet distribution based on median. The optimal sampling plans to meet the consumer’s confidence level with minimum total cost are designed. Efficiency of the proposed plan is also analyzed.     

Urban and agricultural soils: conflicts and trade-offs in the optimization of ecosystem services

On-going human population growth and changing patterns of resource consumption are increasing global demand for ecosystem services, many of which are provided by soils. Some of these ecosystem services are linearly related to the surface area of pervious soil, whereas others show non-linear relationships, making ecosystem service optimization a complex task. As limited land availability creates conflicting demands among various types of land use, a central challenge is how to weigh these conflicting interests and how to achieve the best solutions possible from a perspective of sustainable societal development. These conflicting interests become most apparent in soils that are the most heavily used by humans for specific purposes: urban soils used for green spaces, housing, and other infrastructure and agricultural soils for producing food, fibres and biofuels. We argue that, despite their seemingly divergent uses of land, agricultural and urban soils share common features with regards to interactions between ecosystem services, and that the trade-offs associated with decision-making, while scale- and context-dependent, can be surprisingly similar between the two systems. We propose that the trade-offs within land use types and their soil-related ecosystems services are often disproportional, and quantifying these will enable ecologists and soil scientists to help policy makers optimizing management decisions when confronted with demands for multiple services under limited land availability.     

Keeping Surveys Valid,Reliable, and Useful: A Tutorial

This tutorial focuses on how to produce reliable and generalizable data from random‐digit‐dialing (RDD) landline and cell phone surveys. The article notes that RDD response rates have declined and explores the impact of this pronounced decline. The tutorial addresses order, response mode, and many other biases, sample size, cooperation and response rates, weighting, and hybrid designs‐all using examples from risk analysis to illustrate the key points. The article ends with a brief review of the advantages and disadvantages of major Internet and paper surveys tools, and how these can be molded and sometimes combined in repeated, longitudinal, and other designs to answer questions about risk preferences and perceptions.     

Bayes Inference in Constant Partially Accelerated Life Tests for the Generalized Exponential Distribution with Progressive Censoring

In this paper, the problem of constant partially accelerated life tests when the lifetime follows the generalized exponential distribution is considered. Based on progressive type-II censoring scheme, the maximum likelihood and Bayes methods of estimation are used for estimating the distribution parameters and acceleration factor. A Monte Carlo simulation study is carried out to examine the performance of the obtained estimates.     

A Bayesian approach to non-parametric monotone function estimation

Summary.  The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone.     

Tests of fit for normal inverse Gaussian distributions

Goodness-of-fit tests for the family of symmetric normal inverse Gaussian distributions are constructed. The tests are based on a weighted integral incorporating the empirical characteristic function of suitably standardized data. An EM-type algorithm is employed for the estimation of the parameters involved in the test statistic. Monte Carlo results show that the new procedure is competitive with classical goodness-of-fit methods. An application with financial data is also included.     

Outsourcing to emerging markets: Theoretical perspectives and policy implications

Creating value through outsourcing has emerged as a popular competitive strategy for firms in various industries. In order to survive in the domestic and international marketplaces, firms, especially in developed countries, are seeking opportunities offshore, which is one focus of globalization. Offshore outsourcing has emerged as a popular competitive strategy and emerging markets have become increasingly attractive locations. As firms in developed countries (e.g., the US) continue to face enormous challenges to sustain competitive advantage, outsourcing to emerging markets is becoming an increasingly important source of business renewal and corporate transformation.In spite of the growing strategic significance of sourcing, we have limited knowledge of offshoring and outsourcing to emerging markets. The objectives of this review are fourfold: (1) to provide a better understanding of the concepts of offshoring and outsourcing business models, (2) to discuss relevant theoretical perspectives related to outsourcing, (3) to present a taxonomy of outsourcing strategies drawing on the extant literature, and (4) to discuss public policy implications. Conclusions and direction for future research are provided.