Kenneth D. Lawrence

Modern Statistical Systems and GPSS Simulation

Discrete Event Computer Simulation Introduction to GPSS Random Number Generation and Testing Random Variable Generation Intermediate GPSS Statistical Design and Analysis of Simulations Advanced GPSS Features Case Study of a Simulation: Design, Analysis, and Programming Appendices References

Data Analysis, Regression and Forecasting

This book contains many classic Harvard cases and offers contemporary concept development. Its low cost makes it an ideal bundle with other Duxbury titles. It is appropriate for short courses in MBA-level statistics and as a supplement in more comprehensive courses.Emphasizing the practice of data analysis, the authors teach the methodology needed to solve a variety of commonly occurring real-world problems that managers encounter daily. Readers learn how to make inferences from limited data, forecast sales in appropriate ways, and avoid potentially disastrous errors of caustic reasoning.

Estimating Weibull Parameters by Linear and Nonlinear Regression

A Monte Carlo simulation experiment was performed to compare the resulting mean square error of two different Weibull estimation methods. It was found that the MSE for both methods was quite high in relation to the Rao-Cramer lower bound. Some suggestions are offered for reducing the MSE of estimates.

Experimental comparison of parametric, non-parametric, and hybrid multigroup classification

Highlights? The proposed hybrid method dominates almost all the other methods on classification performance. ? Logistic regression and neural network provides worst relative performance under most scenarios. ? This shows that the data complexities have adverse impact on the multinomial logistic regression. ? The results indicate that all classification methods are adversely affected by the nonstatic data. ? This study demonstrates the effectiveness of the hybrid method in improving classification accuracy. This study evaluates the relative performance of some well-known classification techniques, as well as a proposed hybrid method. The proposed hybrid method is a combination of k-nearest neighbor (kNN) and linear programming (LP) method for four group classification. Computational experiments are conducted to evaluate the performances of these classification techniques. Monte Carlo simulation is used to generate dataset with varying characteristics such as multicollinearity, nonlinearity, etc. for the experiments. The experimental results indicate that LP approaches, in general, and the proposed hybrid method, in particular, consistently have lower misclassification rates for most data characteristics. Furthermore, the hybrid method utilizes the strengths of both methods - k-NN and linear programming - resulting in considerable improvement in the classification accuracy. The results of this study can aid in the design of various hybrid techniques that combine the strengths of different methods to improve classification accuracy and reliability.

Enterprise Information System and Data Mining

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Segmenting Financial Services Market: An Empirical Study of Statistical and Non-parametric Methods

In this paper, we analyze segmentation of financial markets based on the general segmentation bases. In particular, we identify potentially attractive market segments for financial services using a customer dataset. We develop a multi-group discriminant model to classify the customers into three ordinal classes: prime customers, highly valued customers, and price shoppers based on their income, loan activity, and demographics (age). The multi-group classification of customer segments uses both classical statistical techniques and a mathematical programming formulation. For this study we use the characteristics of a real dataset to simulate multiple datasets of customer characteristics. The results of our experiments show that the mathematical programming model in many case consistently outperforms standard statistical approaches in attaining lower Apparent Error Rates (APER) for 100 replications in both high and low correlation cases.

Short-Term Predictions of the Total Medical Costs of California Counties

The aim of this research is to develop a model to forecast short-term health cost changes. The motivation for producing such a model is to provide local decision makers with a tool to predict short-term health-care costs in their localities. In order to achieve this objective, we collected data on total health-care expenditures and demographic data for California counties from 2000 to 2007. We then used various statistical methods to better understand the data and developed a regression model. Each years prediction model was then used to forecast the following years total health-care expenditure. The model developed adequately predicted health-care costs for the years on which the model was developed (2000–2006), and adequately forecast health-care costs for the holdout year, 2007. The average adjusted R2 value was 0.57, with an average mean absolute deviation score of 34. The best predictors of total health-care expenditures were county population, the number of county health-care facilities, and county per capita personal income. The practical implications of the model are that it will provide public and private decision makers with a useful tool for forecasting short-term demand for health-care services, enabling better planning for health-care manpower, facility planning, and financial planning needs. The contribution of this paper contrasts with the earlier work in that it supports short-term operational, not strategic, planning needs. The papers limitation is that it relies on data from one state. It should be tested in other, dissimilar, areas of the United States.

Analyzing the balancing of error rates for multi-group classification

This paper reports the relative performance of an experimental comparison of some well-known classification techniques such as classical statistical, artificial intelligence, mathematical programming (MP), and hybrid approaches. In particular, we examine the four-group, three-variable problem and the associated error rates for the four groups when each of the models is applied to various sets of simulated data. The data had varying characteristics such as multicollinearity, nonlinearity, sample proportions, etc. We concentrate on individual error rates for the four groups, i.e., we count the number of group 1 values classified into group 2, group 3, and group 4 and vice versa. The results indicate that in general not only are MP, k-NN, and hybrid approaches relatively better at overall classification but they also provide a much better balance between error rates for the top customer groups. The results also indicate that the MP and hybrid approaches provide relatively higher and stable classification accuracy under all the data characteristics.

Bankruptcy Prediction in Retail Industry Using Logistic Regression

In view of the failure of high-profile companies such as Circuit City and Linens n Things, Financial distress or bankruptcy prediction of retail and other firms has generated much interest recently. Recent economic conditions have led to predictions of a wave of retail bankruptcies (e.g., McCracken and O’Connell, 2009). This research develops and tests a model for the prediction of bankruptcy of retail firms. We use accounting variables such as inventories, liabilities, receivables, net income (loss), and revenue. Some guiding discriminate rule is given, and a few factors were identified as measures of a profitable company.

Business intelligence and analytics case studies

The ongoing process of digital transformation of organizational operations resulting from the incorporation of new technologies by businesses and consumers has resulted in the creation of vast data...