Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Estimation of Partially Linear Models for Spatial Data over
  Complex Domain   In this paper, we study the estimation of partially linear models for spatial
data distributed over complex domains. We use bivariate splines over
triangulations to represent the nonparametric component on an irregular
two-dimensional domain. The proposed method is formulated as a constrained
minimization problem which does not require constructing finite elements or
locally supported basis functions. Thus, it allows an easier implementation of
piecewise polynomial representations of various degrees and various smoothness
over an arbitrary triangulation. Moreover, the constrained minimization problem
is converted into an unconstrained minimization via a QR decomposition of the
smoothness constraints, which allows for the development of a fast and
efficient penalized least squares algorithm to fit the model. The estimators of
the parameters are proved to be asymptotically normal under some regularity
conditions. The estimator of the bivariate function is consistent, and its rate
of convergence is also established. The proposed method enables us to construct
confidence intervals and permits inference for the parameters. The performance
of the estimators is evaluated by two simulation examples and by a real data
analysis.

15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas