135,336 research outputs found
Nation-building and conflict in modern Africa
No full textLSE’s Elliott Green and his co-author Sanghamitra Bandyopadhyay make some surprising findings as they explore the links between nation-building policies and conflict in various African countries
As scaling effects of research productivity diminish, India must step up R&D investment
No full textAdvanced economies may be facing declining returns on research effort. This makes it all the more imperative that the Modi government gets its house, with regard to R&D, in order write Aniruddha Ghosh and Sujan Bandyopadhyay
Nonparametric spatial ordinal models for clustered periodontal data
No full textClinical attachment level is regarded as the most popular measure to assess periodontal disease (PD). These probed tooth site level measures are usually rounded and recorded as whole numbers (in millimetres) producing clustered (site measures within a mouth) error prone ordinal responses representing some ordering of the underlying PD progression. In addition, it is hypothesized that PD progression can be spatially referenced, i.e. proximal tooth sites share similar PD status in comparison with sites that are distantly located. We develop a Bayesian multivariate probit framework for these ordinal responses where the cut point parameters linking the observed ordinal clinical attachment levels to the latent underlying disease process can be fixed in advance. The latent spatial association characterizing conditional independence under Gaussian graphs is introduced via a non-parametric Bayesian approach motivated by the probit stick breaking process, where the components of the stick breaking weights follow a multivariate Gaussian density with the precision matrix distributed as G-Wishart. This yields a computationally simple, yet robust and flexible, framework to capture the latent disease status leading to a natural clustering of tooth sites and subjects with similar PD status (beyond spatial clustering), and improved parameter estimation through sharing of information. Both simulation studies and application to a motivating PD data set reveal the advantages of considering this flexible non-parametric ordinal framework over other alternatives
The Growth-Inequality Relationship in a Model with Discrete Occupational Choice and Redistributive Tax
No full textNo abstract.
Do richer people have more children? Evidence from widow suicides in colonial India
No full textSanghamitra Bandyopadhyay and Elliott Green investigate the relationship between fertility and wealth in a non-European context in an effort to better understand global income distribution
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Bandyopadhyay, paterek, and kaszlikowski reply
No full text10.1103/PhysRevLett.110.178902Physical Review Letters11017-PRLT
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
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