## Electronic Journal of Statistics

### Computational implications of reducing data to sufficient statistics

Andrea Montanari

#### Abstract

Given a large dataset and an estimation task, it is common to pre-process the data by reducing them to a set of sufficient statistics. This step is often regarded as straightforward and advantageous (in that it simplifies statistical analysis). I show that –on the contrary– reducing data to sufficient statistics can change a computationally tractable estimation problem into an intractable one. I discuss connections with recent work in theoretical computer science, and implications for some techniques to estimate graphical models.

#### Article information

Source
Electron. J. Statist., Volume 9, Number 2 (2015), 2370-2390.

Dates
First available in Project Euclid: 23 October 2015

https://projecteuclid.org/euclid.ejs/1445605703

Digital Object Identifier
doi:10.1214/15-EJS1059

Mathematical Reviews number (MathSciNet)
MR3417186

Zentralblatt MATH identifier
1336.62037

#### Citation

Montanari, Andrea. Computational implications of reducing data to sufficient statistics. Electron. J. Statist. 9 (2015), no. 2, 2370--2390. doi:10.1214/15-EJS1059. https://projecteuclid.org/euclid.ejs/1445605703

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