## Regularization on high dimensional covariance matrices

Motivation I remember one lecture about linear models in the University of Costa Rica. He said before to present some classic method “This only works if you have more data than variables”. In this moment, it seems very reasonable and I could not imagine any real case with more variables ($p$) than data ($n$).

## Paper’s review: Zhu & Fang, 1996. Asymptotics for kernel estimate of sliced inverse regression.

It is already known, that for $latex { Y\in {\mathbb R} }&fg=000000$ and $latex { X \in {\mathbb R}^{p} }&fg=000000$, the regression problem $latex \displaystyle Y = f(\mathbf{X}) + \varepsilon, &fg=000000$ when $latex { p }&fg=000000$ is larger than the data available, it is well-known that the curse of dimensionality problem arises. Richard E. Bellman …

## The return

Photo from Paolo Dala Hola, Hello, Bonjour! It’s good to return to the blogging stream. I’ve been somewhat disconnected these months. A little distracted, with a drought of ideas and a little unmotivated,… I think that it happens, to all of us. At least didn’t happen to me like it did to poor Chuck….