Business Commons^™

Computational Thinking (Ct): On Weaving It In, Paul Curzon, Joan Peckham, Harriet G. Taylor, Amber Settle, Eric Roberts

Amber Settle

Cv July 2009, Byron W. Keating

Dr Byron W. Keating

No abstract provided.

Cv March 2009, Byron W. Keating

Dr Byron W. Keating

No abstract provided.

Learning And Forgetting In Maintenance Outsourcing, Hakan Tarakci, Sunantha Teyarachakul, Kwei Tang

Hakan Tarakci

We study the effects of learning and forgetting on maintenance outsourcing in this paper. This is an extension of the paper by Tarakci et al. (2009), which analyzed the effects of learning on maintenance outsourcing. In our model, a manufacturer offers a short-term outsourcing contract, where payment includes a fixed value along with cost subsidization for each preventive maintenance activity, to an external contractor. The contractor schedules and performs preventive maintenance activities as well as repairs the system if there is a failure. We consider two types of learning: natural learning and learning by costly efforts. We then analyze the …

Local Spectral Analysis Via A Bayesian Mixture Of Smoothing Splines” Journal Of The American Statistical Association, Sally Wood, Ori Rosen, David Stoffer

Sally Wood

No abstract provided.

On The Problem Of Production Deadline And Maintenance Outsourcing, Sharafali Moosa, Hakan Tarakci

Hakan Tarakci

In this paper we consider a production-maintenance problem in which a buyer and a supplier have already entered into a contractual relationship for a firm delivery date. Our focus is on the production-related decisions of the supplier rather than the nature of the contract itself. We assume that production rate is actually constant as long as the system is up and running but randomness arises in production due to downtimes as a result of unpredictable failures (breakdowns) and scheduled preventive maintenance activities. Production maintenance is outsourced. As production output is random due to unreliable production facilities, the supplier needs to …

A Bayesian Approach To Ordinal Outcomes For Neurosurgical Clinical Research., Sally Wood

Sally Wood

The objective of this study is to demonstrate a Bayesian approach for the statistical analysis of neurosurgical data where the investigators have used an ordinal scale for the outcome. A Bayesian approach that uses data augmentation and Gibbs sampling to perform ordinal probit regression is demonstrated in a neurosurgical context. The statistical approach is applied to a regression analysis to examine the relationship between female gender and the outcome of severe traumatic brain injury measured with the Glascow Outcome Scale. The approach is applied to a hierarchical meta-analysis to examine the relationship between age and the outcome from subarachnoid haemorrhage …

Innate And Discretionary Accruals Quality And Corporate Governance, Pamela Kent, James Routledge, Jenny Stewart

James Routledge

This paper extends previous research on the association between corporate governance mechanisms and accruals quality. We derive measures of the discretionary and innate components of accruals quality and regress them against corporate governance characteristics. For discretionary accruals, we find use of a Big 4 audit firm and a larger audit committee as the primary governance mechanisms associated with higher accruals quality. For innate accruals quality, we find that higher quality is associated with an independent board of directors, a larger, more independent and more active audit committee, and use of a Big 4 audit firm. Our findings suggest a stronger …

Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie

Sally Wood

A general Bayesian sampling method is developed that uses parallel chains to select between models and to average the predictive density over such models. The method applies to both non-nested models and to nested models, and is particularly useful for mixtures of complex component models, where a novel approach to overcome the label-switching problem is used. The method is illustrated with real and simulated data in model-averaging over alternative financial time series models, mixtures of normal distributions, and mixtures of smoothing spline models.

The Convergence In Workers’ Skill Levels Under Learning And Forgetting: The Fixed-Point-Property Approach, Sunantha Teyarachakul, Dogan Comez, Hakan Tarakci

Hakan Tarakci

This paper presents a study on the convergence of workers’ skill levels under learning and forgetting in processing time in a batch-manufacturing environment. The convergence properties are examined under assumptions of an infinite horizon, a constant demand rate, and a fixed lot size. Our work extends the convergence results of Teyarachakul, Chand, and Ward (2008) beyond Globerson and Levin’s (1987) exponential forgetting function and Wright’s (1936) learning curve to more general classes of learning and forgetting functions. We also discuss why early papers other than Teyarachakul, Chand, and Ward (2008) did not find other types of long-term behaviors beyond convergence …

Mixture Of Random Effects For Individual Learning Curves, Sally Wood, Edward Cripps, Robert Wood

Sally Wood

In the pyschology literature individuals are often classified as entity theorists or incrementalists. In this paper we explore the different learning behaviours over time of these two groups. To assess learning an individual is assigned a task and their performance on the task is measured over a number of trials. Learning behaviour is modelled as a mixture of two random effects, where the random effects components of the mixture correspond to increased learning and spiralling behaviour. We find significant differences in the learning behaviours of the two groups. Specifically those individuals who are categorized as entity theorists are more likely …

Sambhoos, K., Sudit, M., Paul, J. (2009). Improved Situation Assessment Through Early Detection During A Bioterrorist Attack By Using Dynamic Graph Matching, Jomon Aliyas Paul, Kedar Sambhoos, Sudit Moises

Jomon Aliyas Paul

No abstract provided.

Using Dynamic Graph Matching And Gravity Models For Early Detection Of Bioterrorist Attack By Analysis Of Hospital Patient Data., Jomon Aliyas Paul, Kedar Sambhoos, Govind Hariharan

Jomon Aliyas Paul

Timely detection of a bioterrorist attack is of profound significance for efficient emergency public health management. Various systems currently exist which are capable of detecting the biologic agents prior to (e.g. biosensors) and after exposure (syndromic surveillance) but suffer from limitations like high cost and false positives (Stoto et al., Williams). In this paper, we use novel dynamic graph matching and gravity models to formulate a more precise and efficient methodology for detection. The problem is complicated by the similarity of anthrax and small pox symptoms to common diseases like influenza, chickenpox, airborne characteristics of these agents (that increases the …

Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang

Sally Wood

This article proposes a new prior specification for a Bayesian analysis of the k largest order statistics model. We show that using Jeffreys priors for the end-point and shape parameters of the k largest order statistics model leads to biased estimates of the shape parameter for small to medium sample sizes and to the posterior mode of the end-point being equal to the most extreme observed value. We propose a conjugate prior for the shape parameter and a prior for the end-point which removes the posterior mode at the most extreme observed value while remaining uninformative for values of the …

Neural Network Application For Supplier Selection, Davood Golmohammadi Phd

Davood Golmohammadi

No abstract provided.