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Det är viktigt att förstå att rättigheter kan etableras och utvecklas i en viss tid, för att i en annan tid utmanas, urholkas och ersättas av skyldigheter. Olika rättighetskategorier kan få olika tyngd i förhållande till varandra. Ett exempel belyses i detta kapitel, om de mänskliga rättigheternas framväxt i ett internationellt sammanhang. Bland annat behandlar kapitlet hur det kommer sig att ekon
Vilken betydelse har det förvetenskapliga för vetenskapen? Vilka betydelser kan det ha? Ska vi som forskare eftersträva största möjliga vetenskaplighet från början till slut? Annorlunda uttryckt: Ska vi metodiskt trycka bort våra förvetenskapliga impulser? Eller ska vi tvärtom välkomna dem? Att följa varje förvetenskaplig impuls, var vi än befinner oss i forskningsprocessen, skulle förmodligen få
Recenserad bok: Kurator inom hälso- och sjukvård (upplaga 3)Författare: Annika Lundin, Inger Benkel, Gerd de Neergaard, Britt-MarieJohansson och Charlotta ÖhrlingFörlag: Studentlitteratur, Lund, 2019
Empowering Children and Youththrough Law and ParticipationIn 2023, between August the 30th and September the 1st, the Child RightsInstitute at the Department of Sociology of Law, within Faculty of SocialSciences, Lund University, was arranging a three-day research conference onthe theme Empowering Children and Youth through Law and Participation.41 participants from 13 different countries particip
Historians of the universities have not always belonged to the avantgarde of historical research. On the contrary, many studies of the universities have tended to be rather traditional and narrow-minded. In recent years, however, the surge in the history of knowledge has opened up novel perspectives and given new impulses to how to write the history of the universities. In this presentation, Johan
This blog was written by Jacob Lind, Postdoctoral researcher in international migration at Malmö University; Anna Lundberg, Professor of Sociology of Law at Lund University; Hanna Scott, Doctoral student at Lunköpig University; and Karin Åberg, Doctoral student at the University of Gothenburg.
In this paper, we propose a new estimation procedure for discovering the structure of Gaussian Markov random fields (MRFs) with false discovery rate (FDR) control, making use of the sorted ℓ1-norm (SL1) regularization. A Gaussian MRF is an acyclic graph representing a multivariate Gaussian distribution, where nodes are random variables and edges represent the conditional dependence between the con
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity-meaning that the fraction of variables with a nonvanishing effect tends to a constant, however small-this cannot really be the ca
In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific
We discuss the Empirical Bayes approach to the problem of multiple testing and compare it with a very popular frequentist method of Benjamini and Hochberg aimed at controlling the false discovery rate. Our main focus is the 'sparse mixture' case, when only a small proportion of tested hypotheses is expected to be false. The specific parametric model we consider is motivated by the application to d
We consider the situation in which a large database needs to be analyzed to identify a few important predictors of a given quantitative response variable. There is a lot of evidence that in this case classical model selection criteria, such as the Akaike information criterion or the Bayesian information criterion (BIC), have a strong tendency to overestimate the number of regressors. In our earlie
The modified version of Bayesian Information Criterion (mBIC) is a relatively simple model selection procedure that can be used when locating multiple interacting quantitative trait loci (QTL). Our earlier work demonstrated the statistical properties of mBIC for situations where the average genetic map interval is at least 5 cM. In this work mBIC is adapted to genome searches based on a dense map
We provide an overview of current and possible future directions of Applied Statistics and explore choice of topics (e.g., Bioinformatics), paradigms and methodology. We also provide a tentative assessment of special problems arising in analysis of Indian data. Some priorities and need for special care in applied work are suggested, specially in the Indian context.
We consider the problem of locating multiple interacting quantitative trait loci (QTL) influencing traits measured in counts. In many applications the distribution of the count variable has a spike at zero. Zero-inflated generalized Poisson regression (ZIGPR) allows for an additional probability mass at zero and hence an improvement in the detection of significant loci. Classical model selection c
The classical model selection criteria, such as the Bayesian Information Criterion (BIC) or Akaike information criterion (AIC), have a strong tendency to overestimate the number of regressors when the search is performed over a large number of potential explanatory variables. To handle the problem of the overestimation, several modifications of the BIC have been proposed. These versions rely on su
False Discovery Rate (FDR) and the Bayes risk are two different statistical measures, which can be used to evaluate and compare multiple testing procedures. Recent results show that under sparsity FDR controlling procedures, like the popular Benjamini-Hochberg (BH) procedure, perform also very well in terms of the Bayes risk. In particular asymptotic Bayes optimality under sparsity (ABOS) of BH wa
We consider the problem of identifying significant predictors in large data bases, where the response variable depends on the linear combination of explanatory variables through an unknown monotonic link function, corrupted with the noise from the unknown distribution. We utilize the natural, robust and efficient approach, which relies on replacing values of the response variables by their ranks a
Within a Bayesian decision theoretic framework we investigate some asymptotic optimality properties of a large class of multiple testing rules. A parametric setup is considered, in which observations come from a normal scale mixture model and the total loss is assumed to be the sum of losses for individual tests. Our model can be used for testing point null hypotheses, as well as to distinguish la
