Quantitative Trading Handbook / 1084p

statistical inferencing

Our last post covered probability theory I. Probability theory quantifies uncertainty of random variables, and is the basis for statistical inferencing techniques.

Both parametric families (e.g. Gaussian, poisson, exponentials) and non-parametric (distribution-free) methods are critical for quantitative practice. In particular, robust tests - and tests that don’t rely on restrictive assumptions are valuable.

This add is about 80pages long.

Here is a nice excerpt from our note additions on non-parametric tests:

and an example:

Building on the permutation method for p-value tests, finite sampling probabilistic bounds (see our post before) we have built the intuition for cutting edge scientific approaches. We only scraped the surface of statistical decision theory.

We will next write code for them to concretise said ideas.

Lecture notes are appeneded: