State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.     

Dennis R. Young et al.: Economics for Nonprofit Managers and Social Entrepreneurs

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -     

Two for One: Public Welfare and Regime Legitimacy Through State Funding for CSOs in Russia

Civil societies are usually seen as facilitators of democracy or as oppositional powers withstanding authoritarian rule. However, more and more often civil society organizations (CSOs) appear to contribute to the legitimacy of non-democratic incumbents. Taking the example of contemporary Russia, this paper argues that state funding for CSOs under authoritarian regime conditions serves for securing regime legitimacy in two respects—by supporting CSOs contribution to public welfare and by transmitting state-led legitimacy discourse to the civil society sector. The analysis of applications submitted between 2013 and 2016 to the Presidential Grant Competition (PGC), the biggest public funding programme for CSOs in Russia, shows that the state is (1) supporting CSO activities above all in social, health and education-related fields, and (2) privileging projects that relate to a state-led conservative public discourse not only but foremost within those welfare-related fields. These results highlight the importance of investigating state support to CSOs in order to access the changing role of civil society under authoritarian regime conditions.

     

Writing resistance together

This piece of writing is a joint initiative by the participants in the Gender, Work and Organization writing workshop organized in Helsinki, Finland, in June 2019. This is a particular form of writing differently. We engage in collective writing and embody what it means to write resistance to established academic practices and conventions together. This is a form of emancipatory initiative where we care for each other as writers and as human beings. There are many author voices and we aim to keep the text open and dialogical. As such, this piece of writing is about suppressed thoughts and feelings that our collective picket line allows us to express. In order to maintain the open‐ended nature of the text, and perhaps also to retain some ‘dirtiness’ that is essential to writing, the article has not been language checked throughout by a native speaker of English.     

The Persistence of High Levels of Living Alone Among Adults with Disabilities in Sweden, 1993–2011

Population Research and Policy Review - This study investigates how the probability to live alone has developed among working age individuals with and without disabilities in Sweden during the...     

Being Poor and Feeling Rich or Vice Versa? The Determinants of Unequal Income Positions in Old Age Across Europe

Individual prosperity and welfare can be measured using both objective and subjective criteria. Although theory and previous research suggest that these two methods can produce corresponding results, the measurements can also be inconsistent. Against this background, the current paper investigates the relationship between the objective income position of older Europeans (aged 50?+?years) and their perception of their financial situation, using the seventh wave of the Survey of Health, Aging, and Retirement in Europe (SHARE) conducted in 2017. The main research questions include (1) how is objective income distributed in old age across Europe?, (2) how do elderly Europeans evaluate their income situation subjectively?, (3) is there a discrepancy between the objective prosperity position and their subjective perception observable?, (4) are there country-specific differences that are observable?, and (5) how can such discrepancies be explained?

The results show that objective income positions can be congruent with subjective self-perceptions, both good (well-being) and bad (deprivation), of one’s income situation. However, this is not always the case, and country-specific variations do exist. In analyzing the causes of the 2 forms of nonconformance—namely, adaptation (satisfaction paradox) and dissonance (dissatisfaction dilemma)—this paper concludes that sociodemographic and socioeconomic determinants alone cannot account for discrepancies. The consideration of certain social-psychological influences or personality traits and especially social comparison processes (namely, with one’s past) is essential in explaining both the satisfaction paradox and the dissatisfaction dilemma.

     

On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

Small‐sphere distributions for directional data with application to medical imaging

We propose novel parametric concentric multi‐unimodal small‐subsphere families of densities for p ? 1 ≥ 2‐dimensional spherical data. Their parameters describe a common axis for K small hypersubspheres, an array of K directional modes, one mode for each subsphere, and K pairs of concentrations parameters, each pair governing horizontal (within the subsphere) and vertical (orthogonal to the subsphere) concentrations. We introduce two kinds of distributions. In its one‐subsphere version, the first kind coincides with a special case of the Fisher–Bingham distribution, and the second kind is a novel adaption that models independent horizontal and vertical variations. In its multisubsphere version, the second kind allows for a correlation of horizontal variation over different subspheres. In medical imaging, the situation of p ? 1 = 2 occurs precisely in modeling the variation of a skeletally represented organ shape due to rotation, twisting, and bending. For both kinds, we provide new computationally feasible algorithms for simulation and estimation and propose several tests. To the best knowledge of the authors, our proposed models are the first to treat the variation of directional data along several concentric small hypersubspheres, concentrated near modes on each subsphere, let alone horizontal dependence. Using several simulations, we show that our methods are more powerful than a recent nonparametric method and ad hoc methods. Using data from medical imaging, we demonstrate the advantage of our method and infer on the dominating axis of rotation of the human knee joint at different walking phases.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.