Regression
Thoughts on Models with Regularization
Lately I’ve been reflecting on regularization. Early in my data science career I spent some time working with generalized additive models, but I started focusing more and more on traditional statistical methods. I am re-finding the value in regularization and expect to use more of it going forward.
Robust Portfolio Optimization in Generalized Linear Models
Often we run an A/B test in order to inform some decision. But every A/B test involves uncertainty, no matter the sample size. This uncertainty poses a risk to our decision, which can be hedged by a process analogous to diversifying an investment portfolio. Finding a robust-optimal portfolio is both practical and fast.
Robust Power Assessment
An important part of planning any statistical experiment is power analysis. In this post I will focus on power analysis for linear regression models, but I am hopeful much of this can be applied to Generalized Linear Models and hence to the sorts of A/B tests I normally run.
Scheffe's Method for Multiple Comparisons
I’ve written previously about using the Bonferroni correction for the multiple comparisons problem. While it is without a doubt the simplest way to correct for multiple comparisons, it is not the only way. In this post, I discuss Scheffé’s method for constructing simultaneous confidence intervals on arbitrarily many functions of the model parameters.
Supervised Learning as Function Approximation
Supervised learning is perhaps the most central idea in Machine Learning. It is equally central to statistics where it is known as regression. Statistics formulates the problem in terms of identifying the distribution from which observations are drawn; Machine Learning in terms of finding a model that fits the data well.