Towards the price of leasing online

We consider online optimization problems in which certain goods have to be acquired in order to provide a service or infrastructure. Classically, decisions for such problems are considered as final: one buys the goods. However, in many real world applications, there is a shift away from the idea of buying goods. Instead, leasing is often a more flexible and lucrative business model. Research has realized this shift and recently initiated the theoretical study of leasing models (Anthony and Gupta in Proceedings of the integer programming and combinatorial optimization: 12th International IPCO Conference, Ithaca, NY, USA, June 25–27, 2007; Meyerson in Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), 23–25 Oct 2005, Pittsburgh, PA, USA, 2005; Nagarajan and Williamson in Discret Optim 10(4):361–370, 2013) We extend this line of work and suggest a more systematic study of leasing aspects for a class of online optimization problems. We provide two major technical results. We introduce the leasing variant of online set multicover and give an \(O\left( \log (mK)\log n\right) \)-competitive algorithm (with n, m, and K being the number of elements, sets, and leases, respectively). Our results also imply improvements for the non-leasing variant of online set cover. Moreover, we extend results for the leasing variant of online facility location. Nagarajan and Williamson (Discret Optim 10(4):361–370, 2013) gave an \(O\left( K\log n\right) \)-competitive algorithm for this problem (with n and K being the number of clients and leases, respectively). We remove the dependency on n (and, thereby, on time). In general, this leads to a bound of \(O\left( l_{\text {max}} \log l_{\text {max}} \right) \) (with the maximal lease length \(l_{\text {max}} \)). For many natural problem instances, the bound improves to \(O\left( K^2\right) \).     

Efficiency of consumer intertemporal choice under life cycle cost conditions

Many decisions made by consumers are intertemporal. Life cycle cost (LCC) conditions represent a specific type of intertemporal decisions, typically referring to items involving two cost components: present purchase price and future maintenance costs. This paper presents a conceptual framework for analyzing consumer LCC decision making. Within this framework the notion of choice efficiency is highlighted. The main contribution of the study is the direct estimation of consumers’ choice efficiency, as compared to previous studies that estimate only consumers’ implicit discount rates. Effects of situational and personal variables on efficiency of choice are estimated by means of a series of manipulated choice settings. The main empirical findings show situational effects of monetary size, type of object and time horizon. Additional findings show the effect of personal variables such as gender, marital status and education.     

Decreasing absolute risk aversion, prudence and increased downside risk aversion

Downside risk increases have previously been characterized as changes preferred by all decision makers u(x) with u????(x) > 0. For risk averse decision makers, u????(x) > 0 also defines prudence. This paper finds that downside risk increases can also be characterized as changes preferred by all decision makers displaying decreasing absolute risk aversion (DARA) since those changes involve random variables that have equal means. Building on these findings, the paper proposes using ??more decreasingly absolute risk averse?? or ??more prudent?? as alternative definitions of increased downside risk aversion. These alternative definitions generate a transitive ordering, while the existing definition based on a transformation function with a positive third derivative does not. Other properties of the new definitions of increased downside risk aversion are also presented.     

Density estimation with distribution element trees

The estimation of probability densities based on available data is a central task in many statistical applications. Especially in the case of large ensembles with many samples or high-dimensional sample spaces, computationally efficient methods are needed. We propose a new method that is based on a decomposition of the unknown distribution in terms of so-called distribution elements (DEs). These elements enable an adaptive and hierarchical discretization of the sample space with small or large elements in regions with smoothly or highly variable densities, respectively. The novel refinement strategy that we propose is based on statistical goodness-of-fit and pairwise (as an approximation to mutual) independence tests that evaluate the local approximation of the distribution in terms of DEs. The capabilities of our new method are inspected based on several examples of different dimensionality and successfully compared with other state-of-the-art density estimators.     

Robust confidence regions for the semi-parametric regression model with responses missing at random

In this paper, a regression semi-parametric model is considered where responses are assumed to be missing at random. From the empirical likelihood function defined based on the rank-based estimating equation, robust confidence intervals/regions of the true regression coefficient are derived. Monte Carlo simulation experiments show that the proposed approach provides more accurate confidence intervals/regions compared to its normal approximation counterpart under different model error structure. The approach is also compared with the least squares approach, and its superiority is shown whenever the error distribution in the simulation study is heavy tailed or contaminated. Finally, a real data example is given to illustrate our proposed method.     

Bayesian bivariate survival analysis using the power variance function copula

Copula models have become increasingly popular for modelling the dependence structure in multivariate survival data. The two-parameter Archimedean family of Power Variance Function (PVF) copulas includes the Clayton, Positive Stable (Gumbel) and Inverse Gaussian copulas as special or limiting cases, thus offers a unified approach to fitting these important copulas. Two-stage frequentist procedures for estimating the marginal distributions and the PVF copula have been suggested by Andersen (Lifetime Data Anal 11:333–350, 2005), Massonnet et al. (J Stat Plann Inference 139(11):3865–3877, 2009) and Prenen et al. (J R Stat Soc Ser B 79(2):483–505, 2017) which first estimate the marginal distributions and conditional on these in a second step to estimate the PVF copula parameters. Here we explore an one-stage Bayesian approach that simultaneously estimates the marginal and the PVF copula parameters. For the marginal distributions, we consider both parametric as well as semiparametric models. We propose a new method to simulate uniform pairs with PVF dependence structure based on conditional sampling for copulas and on numerical approximation to solve a target equation. In a simulation study, small sample properties of the Bayesian estimators are explored. We illustrate the usefulness of the methodology using data on times to appendectomy for adult twins in the Australian NH&MRC Twin registry. Parameters of the marginal distributions and the PVF copula are simultaneously estimated in a parametric as well as a semiparametric approach where the marginal distributions are modelled using Weibull and piecewise exponential distributions, respectively.     

Motivating perpetrators of domestic and family violence to engage in behaviour change: The role of fatherhood

Policies and practices around domestic and family violence (DFV) increasingly focus on perpetrator accountability. With growing evidence that punitive responses alone have a limited deterrent effect on perpetrators, behaviour change programs play a significant role in creating accountability and improving safety for victims and children. Motivating perpetrators to engage in such programs can, however, be challenging. Few perpetrators seem to recognize the need to change for their intimate (ex)‐partner due to victim‐blaming attitudes and a sense that relationships are replaceable. Relationships with their children on the other hand seem to hold more value. This article explores the role of fatherhood as a motivating factor for male perpetrators to engage in relevant behaviour change programs. Based on face‐to‐face interviews with 18 fathers in a court‐mandated intervention program, findings alert to the need for education of abusive fathers in 3 key areas: the impact of DFV on children's well‐being, the impact of DFV on the parent–child relationship, and the impact of DFV‐related repercussions on the parent–child relationship. Fathers' desire to have a relationship with their children suggests fatherhood offers a viable angle to motivate their engagement in interventions that address gendered forms of DFV and subsequently improve victims' and children's safety.     

Gender differences in self-regulation patterns and attitudes toward driving among older adults

The automobile is essential for many older adults to fulfill their daily needs, especially since many live where they lack access to public transit or other acceptable modes of transportation. Increased self-regulation is one way older drivers continue to drive safely and maintain mobility. This research considers whether self-regulation attitudes and patterns differ by gender. Results indicate that women and men report distinct patterns of self-regulation behaviors. Age, health status, and household status also interact with gender, influencing the extent of self-regulation. The results also show that women report lower levels of confidence in their driving skills than men, although the difference varies based on whether or not a woman lives alone. Implications of these results are considered for an aging population--particularly women--that over the coming decades will be more reliant on the automobile for transportation than ever before.