Abstract One popular small area estimation method for estimating poverty and inequality indicators is the empirical best predictor under unit-level nested error regression model with a continuous dependent variable. However, parameter more challenging when response variable grouped due to data confidentiality concerns or about survey burden. The work in this paper proposes methodology that enab...

The idea of Information Visualization is to get insights into great amounts of abstract data. Especially document sets found by searching the World Wide Web are a special challenge. The paper gives a short overview on the variety of possible visualizations for this application area. Crucial factors for the success of visualizations are discussed. A combined approach is presented to use alternat...

The quality of topic models highly depends on quality of used documents. Insufficient information may result in topics that are difficult to interpret or evaluate. Including external data to can help to increase the quality of topic models. We propose sparsity and grouped sparsity inducing priors on the meta parameters of word topic probabilities in fully Bayesian Latent Dirichlet Allocation (L...

A non-parametric estimator of a non-increasing density is found in a class of piecewise linear functions when the data consist only of counts. An EM-Algorithm for computing the estimator is developed, and the iterates in the algorithm are shown to converge to the maximum likelihood estimator. Potential applications to distance sampling models are described and illustrated with a numerical examp...

In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been developed to construct network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. To address this issue, we propose a modelbas...

In this paper, we introduce the ROADMAP meta-model, designed to describe intelligent adaptive systems in open environments, using agent concepts such as roles. Developing intelligent adaptive systems creates new challenges in engineering software quality attributes such as correctness and reliability. The ROADMAP meta-model captures our understanding of properties of intelligent adaptive system...