Skew-normal alpha-power model

In this paper, authors study properties and inference for the newly introduced skew-normal alpha-power model, generalizing both, the power-normal and skew-normal models. Inference is approached via maximum likelihood. Fisher information matrix is derived and shown to be nonsingular at the whole parametric space. Special emphasis is placed on the special case of the power–skew-normal model. Studies with real data illustrate the fact that the model can be very useful in applications, being able to overfit less general models entertained in the literature.     

The generalized two-sided power distribution

The generalized standard two-sided power (GTSP) distribution was mentioned only in passing by Kotz and van Dorp Beyond Beta, Other Continuous Families of Distributions with Bounded Support and Applications, World Scientific Press, Singapore, 2004. In this paper, we shall further investigate this three-parameter distribution by presenting some novel properties and use its more general form to contrast the chronology of developments of various authors on the two-parameter TSP distribution since its initial introduction. GTSP distributions allow for J-shaped forms of its pdf, whereas TSP distributions are limited to U-shaped and unimodal forms. Hence, GTSP distributions possess the same three distributional shapes as the classical beta distributions. A novel method and algorithm for the indirect elicitation of the two-power parameters of the GTSP distribution is developed. We present a Project Evaluation Review Technique example that utilizes this algorithm and demonstrates the benefit of separate powers for the two branches of activity GTSP distributions for project completion time uncertainty estimation.     

A study of Bayesian local robustness with applications in actuarial statistics

Local or infinitesimal Bayesian robustness is a powerful tool to study the sensitivity of posterior magnitudes, which cannot be expressed in a simple manner. For these expressions, the global Bayesian robustness methodology does not seem adequate since the practitioner cannot avoid using inappropriate classes of prior distributions in order to make the model mathematically tractable. This situation occurs, for example, when we compute some types of premiums in actuarial statistics in order to fix the premium to be charged to an insurance policy. In this paper, analytical and simple expressions that allow us to study the sensitivity of premiums, which are usually used in automobile insurance are provided by using the local Bayesian robustness methodology. Some examples are examined by using real automobile claim insurance data.     

Working mothers’ decisions on childcare: the case of Spain

This paper aims to analyze household decisions regarding the childcare of young children. We present two specifications. The first one assumes a sequential decision process. Firstly, parents choose between paid or unpaid care and, secondly, those who opt for paid childcare must decide whether to take their children to a nursery or pre-school or employ somebody to care for them. The second specification is a multinomial Logit in which it is assumed that parents choose from three alternatives: unpaid care, paid care by a nanny, and center-based care. We apply our models to a sample of working mothers with children under three. The database used is the 2008–2010 Spanish Survey of Quality of Working Life (Encuesta de Calidad de Vida en el Trabajo). The results are in line with previous work: Parental education, family composition, income and the characteristics of the mother’s job are important factors in determining the type of childcare chosen for under-three-year-olds.     

Annualized hours as a capacity planning tool in make-to-order or assemble-to-order environment: An agricultural implements company case

Capacity planning is one of the most important elements for an efficient production planning and control system. It is even more important when the main strategies to fulfil sales requirements are make-to-order (MTO) or assemble-to-order (ATO). Neither MTO or ATO companies keep finished products in stock which means that production level is driven by actual sales figures. Therefore, it is very important to have an available capacity as flexible as possible to meet sales requirements. Annualized hours (AH) is a work time control system that helps in increasing the flexibility of available capacity. The main rule of the AH system is to hire employees to work a certain number of hours on a yearly basis. Following some other pre-defined rules and limits, the AH system allows a company to adjust weekly available time in order to reduce idle capacity or to fulfil requirements that could mean overtime. This paper proposes a linear programming model to plan the operations using AH. This model has been implemented in a company that produces agricultural implements, showing that it is very simple to use. It is producing excellent practical results.     

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.     

Financial Strain,Negative Interaction,Coping Styles,and Mental Health Among Low-Income Latinos

This study examines the relationship between financial strain and depressive symptoms in later life, and potential psychosocial mediators of this relationship. Drawing on a sample of 214 low-income Latino older adults, we used structural equation modeling to test the direct effect of chronic financial strain on depressive symptomatology, and the indirect effects via social support, negative interaction, and coping styles. Findings indicated that financial strain increased the level of depressive symptoms among this sample. Avoidance and approach-related coping styles emerged as independent determinants of depressive symptoms albeit in opposite directions; higher levels of avoidance coping was associated with higher levels of depressive symptoms. Contrary to previous findings on the general population, findings from the current study indicate that social support and negative interaction were not directly associated with depressive symptoms. Financial strain mediated the effect of sociocultural (nativity status, years of U.S. residence) and social status factors (age, education) on depressive symptoms. This study highlights the differential impact of financial strain and coping styles on psychological well-being, and the potential psychosocial targets of interventions for older adults with chronic health care conditions.     

Asymmetries in Information Processing in a Decision Theory Framework

Research in psychology suggests that some individuals are more sensitive to positive than to negative information while others are more sensitive to negative rather than positive information. I take these cognitive positive–negative asymmetries in information processing to a Bayesian decision-theory model and explore its consequences in terms of decisions and payoffs. I show that in monotone decision problems economic agents with more positive-responsive information structures are always better off, ex ante, when they face problems where payoffs are relatively more sensitive to the action chosen when the state of nature is favorable.      

Block and Basu bivariate lifetime distribution in the presence of cure fraction

This paper presents estimates for the parameters included in the Block and Basu bivariate lifetime distributions in the presence of covariates and cure fraction, applied to analyze survival data when some individuals may never experience the event of interest and two lifetimes are associated with each unit. A Bayesian procedure is used to get point and confidence intervals for the unknown parameters. Posterior summaries of interest are obtained using standard Markov Chain Monte Carlo methods in rjags package for R software. An illustration of the proposed methodology is given for a Diabetic Retinopathy Study data set.