Publications by Matt Bogard
Basic Econometrics in R and SAS
Regression Basicsy= b0 + b1 *X ‘regression line we want to fit’The method of least squares minimizes the squared distance between the line ‘y’ andindividual data observations yi. That is minimize: ∑ ei2 = ∑ (yi – b0 – b1 Xi )2 with respect to b0 and b1 .This can be accomplished by taking the partial derivatives of ∑...
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Regression via Gradient Descent in R
In a previous post I derived the least squares estimators using basic calculus, algebra, and arithmetic, and also showed how the same results can be achieved using the canned functions in SAS and R or via the matrix programming capabilities offered by those languages. I’ve also introduced the concept of gradient descent here and her...
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Time Series Intervention Analysis wih R and SAS
In a previous post, I worked through the theory behind intervention analysis. In his time series course, University of Georgia political science professor Jamie Monogan demonstrates how to implement intervention analysis in R. The following example is from this course. It investigates the impact of the terrorist attacks of 911 on pr...
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An Introduction to Social Network Analysis with R and NetDraw
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Using SNA in Predictive Modeling
In a previous post, I described the basics of social network analysis. I plan to extend that example here with an application in predictive analytics. Let’s suppose we have the following network (visualized in R)Suppose we have used the igraph package in R to derive measures of centrality, and we combined that information with other...
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Nonnegative Matrix Factorization and Recommendor Systems
Albert Au Yeung provides a very nice tutorial on non-negative matrix factorization and an implementation in python. This is based very loosely on his approach. Suppose we have the following matrix of users and ratings on movies: If we use the information above to form a matrix R it can be decomposed into two matrices W and H such that R~ WH' wh...
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R Code for A Justification and Application of Eigenvector Centrality
Leo Spizzirri does an excellent job of providing mathematical intuition behind eigenvector centrality. As I was reading through it, I found it easier to just work through the matrix operations he proposes using R. You can find his paper here: https://www.math.washington.edu/~morrow/336_11/papers/leo.pdfMy R code follows. This is f...
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How John Deere uses R
HT: Revolution Analytics Very good discussion about real applied econometrics and analytics including the use of ARIMA models, decision trees, and genetic algorithms. He also has a very smart approach in his attitude toward big data and data science. I also appreciated his views on the pitfalls of ‘black box’ analytics softwa...
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Data science = failure of imagination
From: https://www.r-bloggers.com/data-driven-science-is-a-failure-of-imagination/I think I like this distinction between Bayesian and Frequentist statistics: “we are nearly always ultimately curious about the Bayesian probability of the hypothesis (i.e. “how probable it is that things work a certain way, given what we see”) rather then in ...
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Decomposition: The Statistics Software Signal
From: Decomposition: The Statistics Software Signal http://seanjtaylor.com/post/39573264781/the-statistics-software-signal“When you don’t have to code your own estimators, you probably won’t understand what you’re doing. I’m not saying that you definitely won’t, but push-button analyses make it easy to compute numbers tha...
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