Section 3 presents the estimation framework and outlines the matching estimation.
A principle conceptual difference between regular regression estimations and matching estimation is that the latter gives the researcher greater flexibility in choosing how to aggregate heterogeneous effects, especially when using the specific technique coarsened exact matching.
The simplified ML algorithm belongs to the covariance matching estimation techniques (COMET) [11].
The propensity score matching estimation is based on the characteristics of the sub-samples in the first wave of each panel.
Fortunately, the initialization tasks (FB7, FB8 and FB9) can run simultaneously with the matching and estimation tasks.
The last model, which is the main model of interest shows results from the propensity score matching estimation.
We have implemented the matching and estimation tasks on a NEON coprocessor and the initialization tasks on a DSP processor.
The correction process implements three tasks: matching, estimation and initialization.
Using the proposed LAI descriptor, we implement a three-stage profile calibration including matching, estimation, and refinement based on grouping and fast iterative closest point (FICP) algorithm.
In the classical matched estimation framework, the performance of any unbiased estimator can be assessed by comparing its error covariance matrix with the Cramér-Rao bound.
This is the most commonly applied matching algorithm in propensity score matching estimation (Ichino et al., 2008).
