Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Dimensional Expectation-Maximization Algorithm: Statistical
  Optimization and Asymptotic Normality   We provide a general theory of the expectation-maximization (EM) algorithm
for inferring high dimensional latent variable models. In particular, we make
two contributions: (i) For parameter estimation, we propose a novel high
dimensional EM algorithm which naturally incorporates sparsity structure into
parameter estimation. With an appropriate initialization, this algorithm
converges at a geometric rate and attains an estimator with the (near-)optimal
statistical rate of convergence. (ii) Based on the obtained estimator, we
propose new inferential procedures for testing hypotheses and constructing
confidence intervals for low dimensional components of high dimensional
parameters. For a broad family of statistical models, our framework establishes
the first computationally feasible approach for optimal estimation and
asymptotic inference in high dimensions. Our theory is supported by thorough
numerical results.

27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex