Well-being, Gamete Donation, and Genetic Knowledge: The Significant Interest View

The Significant Interest view entails that even if there were no medical reasons to have access to genetic knowledge, there would still be reason for prospective parents to use an identity-release donor as opposed to an anonymous donor. This view does not depend on either the idea that genetic knowledge is profoundly prudentially important or that donor-conceived people have a right to genetic knowledge. Rather, it turns on general claims about parents’ obligations to help promote their children’s well-being and the connection between a person’s well-being and the satisfaction of what I call their “worthwhile significant subjective interests.” To put this view simply, the fact that a donor-conceived person—who knows she is donor-conceived—is likely to be very interested in acquiring genetic knowledge gives prospective parents a weighty reason to use an identity-release donor. This is because parents should promote their children’s well-being through the satisfaction of their children’s worthwhile significant interests

Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation

Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L1-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized loglikelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of otentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets

Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting

With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed models. In contrast to common procedures it can be used in high-dimensional settings where many covariates are available and the form of the influence is unknown. It is constructed as a componentwise boosting method and hence is able to perform variable selection. The complexity of the resulting estimator is determined by information criteria. The method is nvestigated in simulation studies for binary and Poisson responses and is illustrated by using real data sets

Binary and Ordinal Random Effects Models Including Variable Selection

A likelihood-based boosting approach for fitting binary and ordinal mixed models is presented. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available. Constructed as a componentwise boosting method it is able to perform variable selection with the complexity of the resulting estimator being determined by information criteria. The method is investigated in simulation studies both for cumulative and sequential models and is illustrated by using real data sets

Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation

Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L1-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized loglikelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of otentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets

Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting

With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed models. In contrast to common procedures it can be used in high-dimensional settings where many covariates are available and the form of the influence is unknown. It is constructed as a componentwise boosting method and hence is able to perform variable selection. The complexity of the resulting estimator is determined by information criteria. The method is nvestigated in simulation studies for binary and Poisson responses and is illustrated by using real data sets

The pro-poorness, growth and inequality nexus: Some findings from a simulation study

A widely accepted criterion for pro-poorness of an income growth pattern is that it should reduce a (chosen) measure of poverty by more than if all incomes were growing equiproportionately. Inequality reduction is not generally seen as either necessary or sufficient for pro-poorness. As shown in Lambert (2010), in order to conduct nuanced investigation of the pro-poorness, growth and inequality nexus, one needs at least a 3-parameter model of the income distribution. In this paper, we explore in detail the properties of inequality reduction and pro-poorness, using the Watts poverty index and Gini inequality index, when income growth takes place within each of the following models: the displaced lognormal, Singh-Maddala and Dagum distributions. We show by simulation, using empirically relevant parameter estimates, that distributional change preserving the form of each of these income distributions is, in the main, either pro-poor and inequality reducing, or pro-rich and inequality exacerbating. Instances of pro-rich and inequality reducing change do occur, but we find no evidence that distributional change could be both pro-poor and inequality exacerbating.poverty, growth, pro-poorness, income distribution.

Heat pipes for spacecraft temperature control: An assessment of the state-of-the-art

Various heat pipe temperature control techniques are critically evaluated using characteristic features and properties, including heat transport capability, volume and mass requirements, complexity and ease of fabrication, reliability, and control characteristics. Advantages and disadvantages of specific approaches are derived and discussed. Using four development levels, the state of-the-art of the various heat pipe temperature control techniques is assessed. The need for further research and development is discussed and suggested future efforts are projected

Measuring Concentration in Data with an Exogenous Order

Concentration measures order the statistical units under observation according to their market share. However, there are situations where an order according to an exogenous variable is more appropriate or even required. The present article introduces a generalized definition of market concentration and defines a corresponding concentration measure. It is shown that this generalized concept of market concentration satisfies the common axioms of (classical) concentration measures. In an application example, the proposed approach is compared with classical concentration measures; the data are transfer spendings of German Bundesliga soccer teams, the ``obvious'' exogenous order of the teams is the league ranking