Particle Learning for General Mixtures

This paper develops particle learning (PL) methods for the estimation of general mixture models. The approach is distinguished from alternative particle filtering methods in two major ways. First, each iteration begins by resampling particles according to posterior predictive probability, leading to a more efficient set for propagation. Second, each particle tracks only the "essential state vector" thus leading to reduced dimensional inference. In addition, we describe how the approach will apply to more general mixture models of current interest in the literature; it is hoped that this will inspire a greater number of researchers to adopt sequential Monte Carlo methods for fitting their sophisticated mixture based models. Finally, we show that PL leads to straight forward tools for marginal likelihood calculation and posterior cluster allocation.Business Administratio

How Digital Are the Digital Humanities? An Analysis of Two Scholarly Blogging Platforms

In this paper we compare two academic networking platforms, HASTAC and Hypotheses, to show the distinct ways in which they serve specific communities in the Digital Humanities (DH) in different national and disciplinary contexts. After providing background information on both platforms, we apply co-word analysis and topic modeling to show thematic similarities and differences between the two sites, focusing particularly on how they frame DH as a new paradigm in humanities research. We encounter a much higher ratio of posts using humanities-related terms compared to their digital counterparts, suggesting a one-way dependency of digital humanities-related terms on the corresponding unprefixed labels. The results also show that the terms digital archive, digital literacy, and digital pedagogy are relatively independent from the respective unprefixed terms, and that digital publishing, digital libraries, and digital media show considerable cross-pollination between the specialization and the general noun. The topic modeling reproduces these findings and reveals further differences between the two platforms. Our findings also indicate local differences in how the emerging field of DH is conceptualized and show dynamic topical shifts inside these respective contexts

Is the left-right scale a valid measure of ideology? Individual-level variation in associations with "left" and "right" and left-right self-placement

In order to measure ideology, political scientists heavily rely on the so-called left-right scale. Left and right are, however, abstract political concepts and may trigger different associations among respondents. If these associations vary systematically with other variables this may induce bias in the empirical study of ideology. We illustrate this problem using a unique survey that asked respondents open-ended questions regarding the meanings they attribute to the concepts "left" and "right". We assess and categorize this textual data using topic modeling techniques. Our analysis shows that variation in respondents’ associations is systematically related to their self-placement on the left-right scale and also to variables such as education and respondents’ cultural background (East vs. West Germany). Our findings indicate that the interpersonal comparability of the left-right scale across individuals is impaired. More generally, our study suggests that we need more research on how respondents interpret various abstract concepts that we regularly use in survey questions