Manufacturing feature-based cost management system: a case study in Brazil

Feature costing is a topic of recent discussion related to cost management systems (CMSs) aimed at understanding how product features influence production process costs. It raises as a key issue in mass customisation environments where a single product model can present hundreds of menu options. In this study, we explore the concept of feature costing beyond the use of activity-based costing (ABC). We propose a CMS based on features and common elements as costing objects instead of products. In our model, we use the unit of production effort method to organise the direct manufacturing costs, ABC method to address the indirect cost and the standard methods to compute raw materials costs. We illustrate the method in the production of bus seats in a Brazilian manufacturing company. This study gives different perspectives to the international academic community on the use of a different CMS used by many Brazilian companies.     

Robust principal component analysis for compositional tables

A data table arranged according to two factors can often be considered a compositional table. An example is the number of unemployed people, split according to gender and age classes. Analyzed as compositions, the relevant information consists of ratios between different cells of such a table. This is particularly useful when analyzing several compositional tables jointly, where the absolute numbers are in very different ranges, e.g. if unemployment data are considered from different countries. Within the framework of the logratio methodology, compositional tables can be decomposed into independent and interactive parts, and orthonormal coordinates can be assigned to these parts. However, these coordinates usually require some prior knowledge about the data, and they are not easy to handle for exploring the relationships between the given factors. Here we propose a special choice of coordinates with direct relation to centered logratio (clr) coefficients, which are particularly useful for an interpretation in terms of the original cells of the tables. With these coordinates, robust principal component analysis (rPCA) is performed for dimension reduction, allowing to investigate relationships between the factors. The link between orthonormal coordinates and clr coefficients enables to apply rPCA, which would otherwise suffer from the singularity of clr coefficients.     

Modeling of paired zero-inflated continuous data without breaking down paired designs

Paired data have been widely collected in the efficiency studies of a new method against an established method in environmental, ecological and medical studies. For example, in comparative fishing studies, ability of catching target species (fish catch) or reducing the catch of non-target species (fish bycatch) is usually investigated through a paired design. These paired fish catches by weight are generally skewed and continuous, but with a significant portion of exact zeros (no catch). Such zero-inflated continuous data are traditionally handled by two-part models where the zero and positive components are handled separately; however, this separation generally destroys paired structure, and thus may result in substantial difficulty in characterizing the relative efficiency between two methods. To overcome this problem, we consider compound Poisson mixed model for paired data with which the zero and non-zero components are characterized in an integral way. In our approach, the clustering effects by pair are captured by incorporating relevant random effects. Our model is estimated using orthodox best linear unbiased predictor approach. Unlike two-part models, our approach unifies inferences of zero and positive components. Our method is illustrated with analyses of winter flounder bycatch data and ultrasound safety data.     

Truncated location-scale non linear regression models

We present a class of truncated non linear regression models for location and scale where the truncated nature of the data is incorporated into the statistical model by assuming that the response variable follows a truncated distribution. The location parameter of the response variable is assumed to be modeled by a continuous non linear function of covariates and unknown parameters. In addition, the proposed model also allows for the scale parameter of the responses to be characterized by a continuous function of the covariates and unknown parameters. Three particular cases of the proposed models are presented by considering the response variable to follow a truncated normal, truncated skew normal, and truncated beta distribution. These truncated non linear regression models are constructed assuming fixed known truncation limits and model parameters are estimated by direct maximization of the log-likelihood using a non linear optimization algorithm. Standardized residuals and diagnostic metrics based on the cases deletion are considered to verify the adequacy of the model and to detect outliers and influential observations. Results based on simulated data are presented to assess the frequentist properties of estimates, and a real data set on soil-water retention from the Buriti Vermelho River Basin database is analyzed using the proposed methodology.     

From distance sampling to spatial capture–recapture

Distance sampling and capture–recapture are the two most widely used wildlife abundance estimation methods. capture–recapture methods have only recently incorporated models for spatial distribution and there is an increasing tendency for distance sampling methods to incorporated spatial models rather than to rely on partly design-based spatial inference. In this overview we show how spatial models are central to modern distance sampling and that spatial capture–recapture models arise as an extension of distance sampling methods. Depending on the type of data recorded, they can be viewed as particular kinds of hierarchical binary regression, Poisson regression, survival or time-to-event models, with individuals’ locations as latent variables and a spatial model as the latent variable distribution. Incorporation of spatial models in these two methods provides new opportunities for drawing explicitly spatial inferences. Areas of likely future development include more sophisticated spatial and spatio-temporal modelling of individuals’ locations and movements, new methods for integrating spatial capture–recapture and other kinds of ecological survey data, and methods for dealing with the recapture uncertainty that often arise when “capture” consists of detection by a remote device like a camera trap or microphone.     

FINANCIAL MARKETS' SHUTDOWN AND REACCESS

We employ a discrete‐time parametric duration model on a group of 121 countries over the period 1970–2011 and find that the probability of the end of financial markets' shutdown and reaccess falls as these events become longer. We also show that: (1) shutdown episodes are longer when economic prospects are poor and the degree of financial openness falls, the chief executive has been in office for long periods, and the country has a default history and (2) spells of reaccess tend to be longer when economic growth improves and financial openness increases, there are neither government crises nor government instability, and the country did not default in the past. (JEL C41, G15)     

Assessing Health Endowment,Access and Choice Determinants: Impact on Retired Europeans’ (In)activity and Quality of Life

A future for the E.U., dominated by an ever-increasing population of retired citizens represents a major challenge to social and health policy in European countries. Under Rowe and Kahn’s (Gerontol 37(4):433–440, 1997) perspective on positive aging, this paper is interested in exploring the role of health on citizens’ active participation after retirement and social engagement to life and quality of life. This paper also aims at finding whether Sen’s (Public health, ethics, and equity. Oxford University Press, Oxford, 2004) capability approach or cumulative disadvantage or advantage theory relative to the access to health also verifies in a context of multi-national developed economies. The first part of this study is therefore concerned with generating a health indicator that enables this, whilst controlling for individual heterogeneity in self-rated health responses from 10,859 retired individuals from the SHARE survey. Socioeconomic determinants of health are found not to be critical in determining health in such a developed context whilst cumulative advantage is found relevant for the positive aging of Europeans. Evidence is found that active engagement in activities and quality of life are most certainly a prerogative for the more educated and the healthier retirees, hinting a strategy for European policymakers: cumulative advantage, leveraged by education and health policy, might just be the long-term strategy for contouring an aging and unproductive European population, transforming what could be a ‘burden’ into an asset.     

Does country-risk influence electricity production worldwide?

This paper investigates the relationship between country-risks (or conflicts) within countries and electricity production. The determinants of electricity production have been neglected in the literature that favours the relationship between energy consumption, growth and development, and despite of the major challenges on electricity supply systems. We empirically establish that weighted conflict index, as well as sub-items as anti-government demonstrations, government crises and riots negatively influence electricity production per capita, after controlling for income per capita. Country conflicts affect electricity production mostly in the long-run. Our results imply that conflicts may affect electricity production substantially and consequently the economy as a whole. Policy should emphasize the institutional framework to avoid conflicts within countries in order to secure electricity production.     

On the Strong Consistency of Ridge Estimates

In this article, we establish strong consistency of the ridge estimates using extended results for the strong consistency of the least squares estimates in multiple regression models which discard the usual assumption of null mean value for the errors and only requires them to be i.i.d. with absolute moment of order r (0 < r ? 1).