Digital Commons Network^™

Actuarial Credibility Approach In Adjusting Initial Cost Estimates Of Transport Infrastructure Projects, Bartłomiej Rokicki, Krzysztof Ostaszewski

Faculty Publications – Mathematics

This paper presents a novel methodology based on the modified actuarial credibility approach. It allows for the adjustment of initial cost estimates of public infrastructure projects by accounting for the additional risk/uncertainty factor. Hence, it offers an interesting alternative to other existing forecasting methods. We test our approach by applying data for over 300 major infrastructure projects implemented in Poland between 2004 and 2020. We prove that, despite its simplicity, the actuarial credibility approach can deliver accurate cost estimates compared to more complex methods such as regression analysis (OLS) or machine learning (LASSO). In particular, we show that, although the …

Time Series Forecasting Of Covid-19 Deaths In Massachusetts, Andrew Disher

Honors Program Theses and Projects

The aim of this study was to use data provided by the Department of Public Health in the state of Massachusetts on its online dashboard to produce a time series model to accurately forecast the number of new confirmed deaths that have resulted from the spread of CoViD-19. Multiple different time series models were created, which can be classified as either an Auto-Regressive Integrated Moving Average (ARIMA) model or a Regression Model with ARIMA Errors. Two ARIMA models were created to provide a baseline forecasting performance for comparison with the Regression Model with ARIMA Errors, which used the number of …

Multicollinearity In Regression Analyses Conducted In Epidemiologic Studies, Kristina Vatcheva, Minjae Lee, Joseph B. Mccormick, Mohammad H. Rahbar

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers …

The Effect Of Ignoring Statistical Interactions In Regression Analyses Conducted In Epidemiologic Studies: An Example With Survival Analysis Using Cox Proportional Hazards Regression Model, Kristina Vatcheva, Joseph B. Mccormick, Mohammad H. Rahbar

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Objective: To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies.

Study design and setting: Based on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models.

Results: Compared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated …

The Slope Mean And Its Invariance Properties, Jun Ji, Charles Kicey

Faculty Articles

Discusses the slope mean and its invariance properties. Notion of invariance; Comparison of linear regression methods; Comparison with classic means; Characterization by invariance; Focus on quasi-arithmetic means; Theorems used.

Advanced Portfolio Theory: Why Understanding The Math Matters, Tom Arnold

Finance Faculty Publications

The goal of this paper is to motivate the use of efficient set mathematics for portfolio analysis [as seen in Roll, 1977] in the classroom. Many treatments stop at the two asset portfolio case (avoiding the use of matrix algebra) and an alarming number of treatments rely on illustration and templates to provide a heuristic sense of the material without really teaching how efficient portfolios are generated. This is problematic considering that the benefits of understanding efficient set mathematics go beyond portfolio analysis and into such topics as regression analysis (as demonstrated here).