I recently had the opportunity to participate in UC Berkeley's Actuarial case competition, hosted by the Cal Actuarial League. The topics of this case competition are Property & Casualty and General Analytics. The full case competition debrief is available here (.pdf), and a copy of the starter files is available here (.zip). Students were allotted roughly 2 weeks (Oct 23 - Nov 8) to complete the following tasks. Property & . . .
This project showcases a spectrum of technical skills in a nontraditional actuarial setting. Many methods are known for analyzing and predicting movement in a financial market and quantifying risk. We seek to give similar treatment in the case of Spotify's Top Charts, where popularity "drives up" the ranking (price) of a given index (song). In particular, we draw special attention to Queen's "Bohemian Rhapsody". Put concisely, in all of Spotify's . . .
Live demos (.gif / video) will be added to this article as time permits, as I'm still developing and improving my definitions and snippets. There will be more added over time (especially for diagrams and plots). Please see my github for the current development!Examples of my notes, created in-class during lectures. Here's the current version of an ongoing collaboration with John-Michael Laurel for Stochastic Processes by Professor Jim Pitman. Math . . .
Suppose we want to create a simple Life Insurance pricing model, as presented in Actuarial Mathematics. Given a set of assumptions, we wish to find the expected value of Life Insurance benefits, discounted to present value. Suppose then we have the following assumptions: Life Insurance Policy A: Pays a flat benefit amount of $50,000 in case of death (up to age 65) Life Insurance Policy B: Pays X% of . . .
Ranging over different lines of business and applications, these are some of my topics of interest for independent research projects: Analyses on personal health data gathered by wearables; what indicators and variables are missing to construct a better understanding of the user?How can actuarial practices (such as generalized linear models or loss triangles) be extended to other data-science and machine learning motivations such as predictive modeling for sales and . . .
Tweedie distributions are a special case of exponential dispersion models and are particularly useful in generalized linear models, as in fitting claims data to statistical distributions. We use exponential dispersion models (and particularly the Tweedie distribution) for pure premium approaches for actuarial estimations. There are particular cases where the Tweedie compound Poisson distribution is suitable and appropriate for a given regression. See here for a useful overview on using a . . .