Purpose The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds...

In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of...

We develop a model wherein a risk-neutral but ambiguity-averse principal contracts with a risk-averse agent who has a risky project. Both the agent and the principal can observe the project output and a public signal. The correlation between the output and the public signal is private...

In this article, we establish a model of competitive insurance markets based on Rothschild and Stiglitz (1976) where insurers can perform risk classification tests either before insurance contracts are issued (underwriting) or when coverage claims are filed (post-loss test). However, insurers...

We advance the literature starting from the work by Dixit (2000), who considers the effect of the principle of good faith on adverse selection. Introducing a post-loss risk misrepresentation testing in a competitive insurance market model, we identify a sufficient condition to guarantee that a...