Małgorzata W. Korolkiewicz

Visualizing Big Data: Everything Old Is New Again

Recent advances have led to increasingly more data being available, leading to the advent of Big Data. The volume of Big Data runs into petabytes of information, offering the promise of valuable insight. Visualization is key to unlocking these insights, however repeating analytical behaviors reserved for smaller data sets runs the risk of ignoring latent relationships in the data, which is at odds with the motivation for collecting Big Data. In this chapter, we focus on commonly used tools (SAS, R, Python) in aid of Big Data visualization, to drive the formulation of meaningful research questions. We present a case study of the public scanner database Dominick’s Finer Foods, containing approximately 98 million observations. Using graph semiotics, we focus on visualization for decision-making and explorative analyses. We then demonstrate how to use these visualizations to formulate elementary-, intermediate- and overall-level analytical questions from the database.

Smoothed Parameter Estimation for a Hidden Markov Model of Credit Quality

We consider a hidden Markov model of credit quality. We assume that the credit rating evolution can be described by a Markov chain but that we do not observe this Markov chain directly. Rather, it is hidden in “noisy” observations represented by the posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise. We derive smoothed estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model.

The Golden Arches: An Approach to Teaching Statistics in a First-Year University Service Course

The realities of large first year service courses add substantially to the challenges of creating an environment conducive to learning. Given the increased understanding of the importance of context in Statistics education, discipline relevance is a key consideration in designing effective and engaging curriculum. However, students enter university with increasingly diverse levels of competency in quantitative subjects, and a survey conducted in the first week of teaching typically reveals negative perceptions of Mathematics, and by extension of Statistics, tempered with anxiety about quantitative subjects in general. We present strategies to overcome some of these challenges in relation to a quantitative methods course for first year Business students. Analysis of a follow-up survey at the end of the course reveals a positive shift in students’ attitudes and improvement in student success in the course.

Using Real Property Option Valuation Methods When Market Data is not Available

Abstract Titman (1985) famously applied optionality to derive the value of a vacant block of land then used as a car park at UCLA for which, in common with traditional property valuation approaches, a willing but not anxious buyer was assumed. However, what might the value of such a vacant block of land be in a property market downturn when willing but not anxious buyers are hesitant and there is an absence of comparable sales for reference? This paper applies concepts of optionality and indifference pricing to explore how such a vacant block of land might be considered relative to other, non-property asset classes to derive an assessment of value in the absence of an active property market.