Bayesian Conditional Tensor Factorizations for High-Dimensional Classification

In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors. In settings such as genomics, there can be complex interactions among the predictors. By using a carefully-structured Tucker factorization, we define a model that can characterize any conditional probability, while facilitating variable selection and modeling of higher-order interactions. Following a Bayesian approach, we propose a Markov chain Monte Carlo algorithm for posterior computation accommodating uncertainty in the predictors to be included. Under near sparsity assumptions, the posterior distribution for the conditional probability is shown to achieve close to the parametric rate of contraction even in ultra high-dimensional settings. The methods are illustrated using simulation examples and biomedical applications

Negative Hedging: Performance Sensitive Debt and CEOs’ Equity Incentives (CRI 2009-014)

We examine the relation between CEOs’ equity incentives and their use of performance-sensitive debt contracts. These contracts require higher or lower interest payments when the borrower\u27s performance deteriorates or improves, thereby increasing expected costs of financial distress while making a firm riskier to the benefit of option holders. We find that managers whose compensation is more sensitive to stock volatility choose steeper and more convex performance pricing schedules, while those with high delta incentives choose flatter, less convex pricing schedules. Performance pricing contracts therefore seem to provide a channel for managers to increase firms’ financial risk to gain private benefits

Labour Market Outcomes for Young Graduates

This paper examines income and employment outcomes for 18 to 30 year old New Zealanders with post-school qualifications, using data from the 1996 and 2001 Censi. Outcomes are analysed by field of study, to highlight the variation in outcomes within the post-school graduate (PSG) population. Fields are characterised according to the specialisation of job choices made by PSGs. A preliminary investigation is undertaken of changes in supply and demand of PSGs in different fields. Part A of the report summarises patterns for all PSGs, and compares fields of study. Part B contains field profiles for each of 26 grouped fields of study that can be compared across the two years.Labour Market outcomes, Tertiary Qualification, Young Graduates, New Zealand

Implementing spatial segregation measures in R.

Reliable and accurate estimation of residential segregation between population groups is important for understanding the extent of social cohesion and integration in our society. Although there have been considerable methodological advances in the measurement of segregation over the last several decades, the recently developed measures have not been widely used in the literature, in part due to their complex calculation. To address this problem, we have implemented several newly proposed segregation indices in R, an open source software environment for statistical computing and graphics, as a package called seg. Although there are already a few standalone applications and add-on packages that provide access to similar methods, our implementation has a number of advantages over the existing tools. First, our implementation is flexible in the sense that it provides detailed control over the calculation process with a wide range of input parameters. Most of the parameters have carefully chosen defaults, which perform acceptably in many situations, so less experienced users can also use the implemented functions without too much difficulty. Second, there is no need to export results to other software programs for further analysis. We provide coercion methods that enable the transformation of our output classes into general R classes, so the user can use thousands of standard and modern statistical techniques, which are already available in R, for the post-processing of the results. Third, our implementation does not require commercial software to operate, so it is accessible to a wider group of people

Model-Independent Measurement of the Primordial Power Spectrum

In inflationary models with minimal amount of gravity waves, the primordial power spectrum of density fluctuations, $A_S^2(k)$, together with the basic cosmological parameters, completely specify the predictions for the cosmic microwave background (CMB) anisotropy and large scale structure. Here we show how we can strongly constrain both $A_S^2(k)$ and the cosmological parameters by combining the data from the Microwave Anisotropy Probe (MAP) and the galaxy redshift survey from the Sloan Digital Sky Survey (SDSS). We allow $A_S^2(k)$ to be a free function, and thus probe features in the primordial power spectrum on all scales. MAP and SDSS have scale-dependent measurement errors that decrease in opposite directions on astrophysically interesting scales; they complement each other and allow the measurement of the primordial power spectrum independent of inflationary models, giving us valuable information on physics in the early Universe, and providing clues to the correct inflationary model.Comment: 4 pages including 4 figures. To appear in "Particle Physics and the Early Universe (COSMO-98)", editor David O. Caldwell (American Institute of Physics