Estimating utility functions using generalized maximum entropy

This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.     

Four criteria to evaluate providers' service-delivery response to new contraceptive introduction

This article presents an evaluation framework developed to assess the first-level effects of introducing the Standard Days Method (SDM) in Peru Ministry of Health clinics. Four questions are asked: 1) To what extent do providers routinely achieve SDM service delivery standards? 2) Is the time invested in SDM delivery consistent with program norms? 3) How does SDM delivery compare with delivery of established methods? and 4) How does SDM introduction affect delivery of established methods? A study at 62 clinics demonstrated the framework's usefulness. The Standard Days Method introduction had positive overall effects on the quality of care but provider training needed adjustments.     

A new regression model for bimodal data and applications in agriculture

We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.     

<Emphasis Type="BoldItalic">MAD-STEC</Emphasis>: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.     

Strains in Sustainability Debates: Traditional Ecological Knowledge and Western Science through the Lens of Extension Agents in a Pastoral Region

Those involved in sustainability debates on developmental pathways concur in the synergistic potential of integrating traditional ecological knowledge (TEK) and Western scientific approaches. Transhumant pastoralism is a livelihood strategy adapted to spatiotemporal environmental variability in many mountainous and arid regions worldwide. This form of livelihood is based on a mobile logic that is increasingly threatened by novel lifestyles promoted from a Western mind-set and by climate change. The aim of this article is to identify and characterize the different perspectives of environmental and social issues in a pastoral region and their association with labor collaboration among extension agents, framed in an institutional action. We tackled the inquiry about viewpoints with Q methodology and related it to regional problems, alternative solutions, and future development pathways for transhumant pastoralism and landscape management in northwest Patagonia. We identified six perspectives and characterized them with their topological position in the social network. Mediating positions registered the highest network centrality of labor collaborations among agents, whereas more dominant perspectives emphasizing TEK or scientific knowledge registered intermediate centrality. There was consensus on the need for sustainable developmental options, but the emphasis on combining knowledge still needs convergent solutions.     

Quality strategies in supply chain alliances of disposable items

This paper studies a single-product distribution channel where a supplier manufactures items of a given type, some of which are defective, that are sold by a retailer who only detects a subset of the defective items, passing the rest along to customers. We conjecture the structure of the demand and cost functions, assuming customers to have a decreasing marginal aversion to bad quality while both the supplier and the retailer make marginally increasing efforts to avoid bad quality. This allows us to deduce several implicit parameters of a cost model based on observable data, such as the share of the channel margin. Once the parameters of the model are available, we analyze the result of vertical integration. Although we confirm the well-known fact that vertical integration improves the quality perceived by the customer, we characterize the supplier's decision of whether or not to provide a better quality in terms of the individual channel margins. As an alternative, we derive the conditions under which the supplier and the retailer may devise a mutually beneficial transfer contract that simultaneously increases their profit and improves quality.     

On the Exact and Near-Exact Distributions of the Product of Generalized Gamma Random Variables and the Generalized Variance

In this article, the authors first obtain the exact distribution of the logarithm of the product of independent generalized Gamma r.v.’s (random variables) in the form of a Generalized Integer Gamma distribution of infinite depth, where all the rate and shape parameters are well identified. Then, by a routine transformation, simple and manageable expressions for the exact distribution of the product of independent generalized Gamma r.v.’s are derived. The method used also enables us to obtain quite easily very accurate, manageable and simple near-exact distributions in the form of Generalized Near-Integer Gamma distributions. Numerical studies are carried out to assess the precision of different approximations to the exact distribution and they show the high accuracy of the approximations provided by the near-exact distributions. As particular cases of the exact distributions obtained we have the distribution of the product of independent Gamma, Weibull, Frechet, Maxwell-Boltzman, Half-Normal, Rayleigh, and Exponential distributions, as well as the exact distribution of the generalized variance, the exact distribution of discriminants or Vandermonde determinants and the exact distribution of any linear combination of generalized Gumbel distributions, as well as yet the distribution of the product of any power of the absolute value of independent Normal r.v.’s.     

Small area estimation of mean price of habitation transaction using time-series and cross-sectional area-level models

In this paper, a new small domain estimator for area-level data is proposed. The proposed estimator is driven by a real problem of estimating the mean price of habitation transaction at a regional level in a European country, using data collected from a longitudinal survey conducted by a national statistical office. At the desired level of inference, it is not possible to provide accurate direct estimates because the sample sizes in these domains are very small. An area-level model with a heterogeneous covariance structure of random effects assists the proposed combined estimator. This model is an extension of a model due to Fay and Herriot [5], but it integrates information across domains and over several periods of time. In addition, a modified method of estimation of variance components for time-series and cross-sectional area-level models is proposed by including the design weights. A Monte Carlo simulation, based on real data, is conducted to investigate the performance of the proposed estimators in comparison with other estimators frequently used in small area estimation problems. In particular, we compare the performance of these estimators with the estimator based on the Rao–Yu model [23]. The simulation study also accesses the performance of the modified variance component estimators in comparison with the traditional ANOVA method. Simulation results show that the estimators proposed perform better than the other estimators in terms of both precision and bias.     

Multidimensional Targeting: Identifying Beneficiaries of Conditional Cash Transfer Programs

Conditional cash transfer programs (CCTs) have two main objectives: reducing poverty and increasing the human capital of children. To reach these objectives, transfers are given to poor households conditioned on investments in their children’s education, health, and nutrition. Targeting mechanisms used by CCTs have been generally successful in identifying the income poor but have not fared as well in identifying households that under-invest in human capital. These mechanisms do not consider the multidimensional aspect of poverty, even when composite measures are used, as they do not capture each dimension-specific deprivation. This paper proposes a multidimensional targeting approach to identifying beneficiaries that explicitly takes into consideration the multiple objectives of CCTs and the multiple deprivations of the poor household. Results indicate that the proposed multidimensional targeting methodology significantly improves the selection of households with children who are most deprived in the dimensions often relevant to CCTs. In the case of Mexico’s Oportunidades, ex-ante evaluation results indicate that the multidimensional identification of beneficiaries increases the impact of transfers on school attendance compared to alternative targeting models.     

Who am I when I don't know who I am? The problem of personal identity in infants and elderly with cognitive disabilities

This article addresses the problem of whether we can speak of personal identity in cases of infants or elderly with cognitive disabilities as hydrocephaly or dementia, lives that could be considered borderline in terms of personal identity because they lack certain characteristics normally considered indispensable for personal life. Taking as a reference recent discussions on personal identity, particularly the narrative theories of Hilde Lindemann, Françoise Baylis and Marya Schechtman, the article analyses in what sense, under what assumptions, and in what way such a thing could be defended. Finally, some problems and objections to these approaches are considered.