<p> Fraudulent automobile insurance claims are not only a loss for insurance companies, but also for their policyholders. In order for insurance companies to prevent significant loss from false claims, they must raise their premiums for the policyholders. The goal of this research is to develop a decision making algorithm to determine whether a claim is classified as fraudulent based on the observed characteristics of a claim, which can in turn help prevent future loss. The data includes 923 cases of false claims, 14,497 cases of true claims and 33 describing variables from the years 1994 to 1996. To achieve the goal of this research, parametric and nonparametric methods are used to determine what variables play a major role in detecting fraudulent claims. These methods include logistic regression, the LASSO (least absolute shrinkage and selection operator) method, and Random Forests. This research concluded that a non-parametric Random Forests model classified fraudulent claims with the highest accuracy and best balance between sensitivity and specificity. Variable selection and importance are also implemented to improve the performance at which fraudulent claims are accurately classified.</p>

Recent technological advances in sensor and computer technology allow the observation of business and industrial processes at fairly high frequencies. For example, data used for monitoring critical parameters of industrial furnaces, conveyor belts or chemical processes may be sampled every minute or second. A high sampling rate is also possible in business related processes such as mail order distribution, fast food restaurant operations, and electronic commerce. Data obtained from frequently monitored business processes are likely to be autocorrelated time series that may or may not be stationary. If left alone, processes will typically not be stable, and hence they will usually not posses a fixed mean, thus exhibiting homogeneous non-stationarity. For monitoring, control, and forecasting purposes of such potentially non-stationary processes it is often important to develop an understanding of the dynamic properties of processes. However, it is sometimes difficult if not impossible to conduct deliberate experiments on full scale industrial plants or business processes to gain the necessary insight of their dynamic properties. Fortunately, intentional or inadvertent process changes that occur in the course of normal operation sometimes offer an opportunity to identify and estimate aspects of the dynamic behavior. To determine if a time series is stationary, the standard exploratory data analytic approach is to check that the sample autocorrelation function (ACF) fades out relatively quickly. An alternative, and at times a sounder approach is to use the variogram – a data exploratory tool widely used in spatial (geo) statistics for the investigation of spatial correlation of data. The first objective of this dissertation is to derive the basic properties of the variogram and to provide the literature on confidence intervals for the variogram. We then show how to use the multivariate Delta method to derive asymptotic confidence intervals for the variogram that are both practical and computationally appealing. The second objective of this dissertation is to review the theory of dynamic process modeling based on time series intervention analysis and to show how this theory can be used for an assessment of the dynamic properties of business and industrial processes. This is accompanied by a detailed example of the study of a large scale ceramic plant that was exposed to an intentional but unplanned structural change (a quasi experiment). The third objective of this dissertation concerns the analysis of multiple interventions. Multiple interventions occur either as a result of multiple changes made to the same process or because of a single change having non-homogeneous effects on time series. For evaluating the effects of undertaken structural changes, it is important to assess and compare the effects, such as gains or losses, of multiple interventions. A statistical hypothesis test for comparing the effects among multiple interventions on process dynamics is developed. Further, we investigate the statistical power of the suggested test and elucidate the results with examples.

<p> The purpose of this quantitative research study presents the correlations between education, years of experience, and Six Sigma experience versus an individual&rsquo;s compensation because of training and participating in the Six Sigma Methodology with regards to organizational management theory and leadership. The literature review revealed the historic foundations of Total Quality Management, Six Sigma and Lean aspects of participating in Six Sigma. The participant population consisted of Green Belts, Black Belts, Master Black Belts and Champions from several different industries within the United States and abroad. The participant data were collected through a comprehensive questionnaire with the following categories: (a) demographics; (b) Six Sigma training; (c) industry experience; (d) Six Sigma project completion; (e) compensation; (f) Lean Six Sigma tools frequency of use. The Spearman&rsquo;s rho results of the study indicated that Six Sigma training and participation is statistically significant and an important benefit to participants as compared to years of experience and education. An individual trained in the Six Sigma Methodology earns more to significantly more over the course of their career after participating and practicing Six Sigma. The study provides leadership with an understanding of the expected Six Sigma participant salaries and bonus percentage after training and successfully practicing the Six Sigma Methodology, as well as a brief discourse and recommendations on Six Sigma implementation within matrix organizations through a transformational leadership style.</p>

<p> Neglecting uncertainties in the estimation of activities, costs, and durations can significantly contribute to overruns in a project's budget and schedule. On the other hand, properly enforced penalties and incentives can motivate contractors to finish on time and within the allotted budget. However, the current literature on this topic does not sufficiently address project penalties and incentives within the context of uncertainty and dependence. Thus, this dissertation considers how allocating penalties and incentives can impact a stochastic project network in which activity durations are random variables, and some of the activities are subcontracted. The impact of penalty/incentive schemes on project and activities uncertainties is also examined. Overall, one of the main pursued benefits of this work is to provide project stakeholders with a tool that can help determine the appropriate penalty and incentive rates for outsourced activities when creating the contract. </p><p> The study revealed that a total allocation of a project level penalty/incentive to relevant activities was considered a fair allocation. A Monte Carlo Simulation model (MCS) was used to generate random variables, incorporate activity distributions, incorporate dependence uncertainties, and to examine the effect the penalty/incentive scheme has on the aggregated project cost. In order to validate the simulation model, its outcomes were verified with deterministic outcomes. Furthermore, based on the several allocation methods explored, the most adequate allocation method found was the normalized allocation of project penalty/incentive to activities based on the probability of a zero slack activity lying on the critical path. The presented MCS model was then expanded and applied on a larger network. The results of this study demonstrated that the penalty/incentive scheme can increase the project uncertainty at earlier stages of the project, but by using the proper allocation method at later stages, it is contained to the baseline levels that do not comprise any penalty/incentive. The study also revealed that the common practice of assuming project activities as being independent underestimates the most critical, not the least critical, activities' penalty/incentive rates.</p>

Thesis (Ph.D.)--Indiana University, Dept. of Economics, 2009. / Title from PDF t.p. (viewed on Jul 15, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: A, page: 4783. Adviser: Pravin K. Trivedi.

Over the past decades technological developments have both changed and increased human in influence on the marine environment. We now have greater potential than ever before to introduce disturbance and deplete marine resources. Two of the issues currently under public scrutiny are the exploitation of fish stocks worldwide and levels of anthropogenic noise in the marine environment. The aim of this thesis is to investigate and develop novel analyses and simulations to provide additional insight into some of the challenges facing the marine ecosystem today. These methodologies will improve the management of these risks to marine ecosystems. This thesis first addresses the issue of competition between humans and grey seals (Halichoerus grypus) for marine resources, providing compelling evidence that a substantial proportion of the sandeels consumed by grey seals in the North Sea are in fact H. lanceolatus, which is not commercially exploited, rather than the commercially important A. marinus. In addition, we present quantitative results regarding sources of bias when estimating the total biomass of sandeels consumed by grey seals. Secondly, we investigate spatially adaptive 2-dimensional smoothing to improve the prediction of both the presence and density of marine species, information that is often key in the management of marine ecosystems. Particularly, we demonstrate the benefits of such methods in the prediction of sandeel occurrence. Lastly this thesis provides a quantitative assessment of the protocols for real-time monitoring of marine mammal presence, which require that acoustic operations cease when an animal is detected within a certain distance (i.e. the &quot;monitoring zone&quot;) of the sound source. We assess monitoring zones of different sizes with regards to their effectiveness in reducing the risks of temporary and permanent damage to the animals' hearing, and demonstrate that a monitoring zone of 2 km is generally recommendable.