Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models

We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of multivariate time series. The perspective is that of a decision-maker with a specific forecasting objective that guides thinking about relevant models. Based on formal Bayesian decision-theoretic reasoning, we develop a time-adaptive approach to exploring, weighting, combining and selecting models that differ in terms of predictive variables included. The adaptivity allows for changes in the sets of favored models over time, and is guided by the specific forecasting goals. A synthetic example illustrates how decision-guided variable selection differs from traditional Bayesian model uncertainty analysis and standard model averaging. An applied study in one motivating application of long-term macroeconomic forecasting highlights the utility of the new approach in terms of improving predictions as well as its ability to identify and interpret different sets of relevant models over time with respect to specific, defined forecasting goals.Comment: 23 pages, 11 figure

Prior Influence in Bayesian Statistics

1 online resource (PDF, 71 pages

Local Predicitive Influence

1 online resource (PDF, 15 pages

Explaining the Perfect Sampler.

In 1996, Propp and Wilson introduced coupling from the past (CFTP), an algorithm for generating a sample from the exact stationary distribution of a Markov chain. In 1998, Fill proposed another so–called perfect sampling algorithm. These algorithms have enormous potential in Markov Chain Monte Carlo (MCMC) problems because they eliminate the need to monitor convergence and mixing of the chain. This article provides a brief introduction to the algorithms, with an emphasis on understanding rather than technical detail.Coupling from the past; Fill's algorithm; Markov Chain Monte Carlo; Stochastic processes;

Liberty Bell Hospital: A Case Study In Employee Information Systems Fraud

Information systems provide an attractive opportunity for dishonest employees in sensitive job positions to develop and implement a fraudulent scheme. Many different types of technical information systems controls help prevent these situations from occurring and can also detect occurrences after they have happened. However, in some cases, employees are able to circumvent critical segregation of duties. In addition, management of a company may override traditional internal controls in order to achieve business objectives. Overriding internal controls can produce an environment that is conducive to fraud.Internal auditors with an information systems specialty can often identify red flags prior to fraudulent acts taking place in the organization. This allows an organization to utilize preventive measures to reduce the likelihood of a fraud occurring. In a specific situation where an information system fraud is suspected, internal auditors are often charged with leading the investigation. This case analyzes an employee fraud involving a breakdown of internal information technology and management controls, falsification of business records, and a lack of segregation of duties. This case is designed for use in either an undergraduate auditing, information systems security, accounting ethics, internal auditing, computer ethics or other related class. Its primary purpose is to introduce students to a very common type of employee fraud and to illustrate how professional guidance can be applied in such a situation. While the case is based on a true situation, all identities have been modified to protect each individuals right to privacy

Kernel Intensity Estimation of 2-Dimensional Spatial Poisson Point Processes From k-Tree Sampling

To estimate the spatial intensity (density) of plants and animals, ecologists often sample populations by prespecifing a spatial array of points, then measuring the distance from each point to the k nearest organisms, a so-called k-tree sampling method. A variety of ad hoc methods are available for estimating intensity from k-tree sampling data, but they assume that two distinct points of the array do not share nearest neighbors. However, nearest neighbors are likely to be shared when the population intensity is low, as it is in our application. The purpose of this paper is twofold: (a) to derive and use for estimation the likelihood function for a k-tree sample under an inhomogeneous Poisson point-process model and (b) to estimate spatial intensity when nearest neighbors are shared. We derive the likelihood function for an inhomogeneous Poisson point-process with intensity λ(x,y) and propose a likelihood-based, kernel-smoothed estimator $\hat{\lambda}(x,y)$. Performance of the method for k=1 is tested on four types of simulated populations: two homogeneous populations with low and high intensity, a population simulated from a bivariate normal distribution of intensity, and a “cliff” population in which the region is divided into high- and low-intensity subregions. The method correctly detected spatial variation in intensity across different subregions of the simulated populations. Application to 1-tree samples of carnivorous pitcher plants populations in four New England peat bogs suggests that the method adequately captures empirical patterns of spatial intensity. However, our method suffers from two evident sources of bias. First, like other kernel smoothers, it underestimates peaks and overestimates valleys. Second, it has positive bias analogous to that of the MLE for the rate parameter of exponential random variables.Organismic and Evolutionary Biolog

Hemlock Woolly Adelgid and Elongate Hemlock Scale Induce Changes in Foliar and Twig Volatiles of Eastern Hemlock

Eastern hemlock (Tsuga canadensis) is in rapid decline because of infestation by the invasive hemlock woolly adelgid (Adelges tsugae; \u27HWA\u27) and, to a lesser extent, the invasive elongate hemlock scale (Fiorinia externa; \u27EHS\u27). For many conifers, induced oleoresin-based defenses play a central role in their response to herbivorous insects; however, it is unknown whether eastern hemlock mobilizes these inducible defenses. We conducted a study to determine if feeding by HWA or EHS induced changes in the volatile resin compounds of eastern hemlock. Young trees were experimentally infested for 3 years with HWA, EHS, or neither insect. Twig and needle resin volatiles were identified and quantified by gas chromatography/mass spectrometry. We observed a suite of changes in eastern hemlock\u27s volatile profile markedly different from the largely terpenoid-based defense response of similar conifers. Overall, both insects produced a similar effect: most twig volatiles decreased slightly, while most needle volatiles increased slightly. Only HWA feeding led to elevated levels of methyl salicylate, a signal for systemic acquired resistance in many plants, and benzyl alcohol, a strong antimicrobial and aphid deterrent. Green leaf volatiles, often induced in wounded plants, were increased by both insects, but more strongly by EHS. The array of phytochemical changes we observed may reflect manipulation of the tree\u27s biochemistry by HWA, or simply the absence of functional defenses against piercing-sucking insects due to the lack of evolutionary contact with these species. Our findings verify that HWA and EHS both induce changes in eastern hemlock\u27s resin chemistry, and represent the first important step toward understanding the effects of inducible chemical defenses on hemlock susceptibility to these exotic pests