Suicide risk assessment in the depressed elderly patient with cancer

Although several studies have identified different factors that increase the risk for suicide in the elderly, depression, especially in cancer patients, puts people at even greater risk. In geriatric patients with co-morbid illnesses, depression is often diagnosed or overlooked because of the assumption that it is to be expected. When patients with cancer are diagnosed with depression, it is imperative that clinicians have a clear-cut tool for assessing and identifying signs of suicidality in order to develop a treatment plan to manage the patient. This article delineates the steps involved in conducting a thorough and systematic suicide risk assessment in depressed elderly patients with cancer.     

Does shareholder protection affect the performance of analysts as a gatekeeper?

This paper investigates the effect of shareholder protection on analysts’ performance. The important corporate governance role analysts can play by monitoring management has largely been ignored in the literature. Using a newly constructed index, we are for the first time able to analyse how changes in shareholder protection over time affect analysts’ performance as a gatekeeper. We find that strengthened shareholder protection improves analysts’ performance as a gatekeeper, i.e. analysts’ ability to reduce information asymmetry improves with strengthened shareholder protection. However, we also find a substitution effect: Strengthened shareholder protection makes analysts’ services less valuable to investors, thereby leading to a reduction in the number of analysts following firms.     

Likelihood Inference for Discretely Observed Nonlinear Diffusions

This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis‐Hastings algorithm, in conjunction with the Euler‐Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.