Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parameter estimation in linear regression driven by a Gaussian sheet   The problem of estimating the parameters of a linear regression model
$Z(s,t)=m_1g_1(s,t)+ \cdots + m_pg_p(s,t)+U(s,t)$ based on observations of $Z$
on a spatial domain $G$ of special shape is considered, where the driving
process $U$ is a Gaussian random field and $g_1, \ldots, g_p$ are known
functions. Explicit forms of the maximum likelihood estimators of the
parameters are derived in the cases when $U$ is either a Wiener or a stationary
or nonstationary Ornstein-Uhlenbeck sheet. Simulation results are also
presented, where the driving random sheets are simulated with the help of their
Karhunen-Lo\`eve expansions.

29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn