Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inference in Gaussian models with missing data using Equalisation
  Maximisation   Equalisation Maximisation (EqM) is an algorithm for estimating parameters in
auto-regressive (AR) models where some fraction of the data is missing. It has
previously been shown that the EqM algorithm is a competitive alternative to
expectation maximisation, estimating models with equal predictive capability at
a lower computational cost.
  The EqM algorithm has previously been motivated as a heuristic. In this
paper, we instead show that EqM can be viewed as an approximation of a proximal
point algorithm. We also derive the method for the entire class of Gaussian
models and exemplify its use for estimation of ARMA models with missing data.
The resulting method is evaluated in numerical simulations, resulting in
similar results as for the AR processes.

37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph