Here is the research finding that punches HARD on the conventional hypothesis of learning : ( Study + Study + Study +…+ Final Test ) leads to better learning. [youtube https://www.youtube.com/watch?v=oqae85jbfbE?rel=0]

Via NYTtimes : Every September, millions of parents try a kind of psychological witchcraft, to transform their summer-glazed campers into fall students, their video-bugs into bookworms. Advice is cheap and all too familiar: Clear a quiet work space. Stick to a homework schedule. Set goals. Set boundaries. Do not bribe (except in emergencies). And check out the classroom. Does Junior’s learning style match the new teacher’s approach? Or the school’s philosophy?

Yihui Xie, the author of Knitr package mentions my book review on the package demo site A book review on RPubs by RK I had rewritten my review as an Rmd and had posted on RPubs for others to read.

This paper mentions a mechanism to clean high frequency data of outliers. The setting is NYSE TAQ(Trades and Quotes data) and many initial filters(data cleaning) applied are specific to NYSE. However the mechanism for removing outliers that is mentioned by is market agnostic. The key idea behind the method is to choose k neighbor prices + a fudge factor gamma, and compute a trimmed mean and standard deviation of the k neighboring prices.

highfrequency is an R package that can be used to 1) clean and aggregate high frequency data, 2) compute realized volatility measures 3) compute liquidity measures. The package is an improved version of two other R packages, RTAQ and realized. The vignette for thepackage explains two models, HAR and HEAVY models. HAR models rely on jump modeling and one needs to have a decent idea of Levy processes to appreciate the HAR variants.