Figure 9 presents the quality of estimation for the first model; Figure 10, for the BOT model, namely the measurement error with respect to the measurement time.
Although such a "stacked" formulation is not novel (e.g. [20]), it is used in the context of surface normal estimation for the first time.
The estimation procedure followed a two-phase sampling design grounded within a hybrid inferential framework, which combines design-based estimation for the first phase and model-based inference in the second phase.
Due to restricted availability of data regarding the total labour costs, we are bound to reduce the system estimation for the first period to years 2000 2007, which implies loosing a third of the sample period (16 quarters of information).
The quality of estimation for the first model was measured by the MSE between each hidden and estimated trajectories, for the BOT model by the position error (ie. Euclidean distance).
For the estimation of the second order derivatives of v, we need the following lemma.
Recently, Simar and Wilson (2007, 2011) proposed a new estimation methodology for the second stage based on the use of bootstrapping to overcome some drawbacks of these conventional estimation models.
This is thus more efficient to isolate the computationally demanding part of the analysis (recursive haplotype estimation) in a preliminary step that only needs do be done once for each genomic region, and then to adopt a fast deterministic IBD estimation algorithm for the second step that is repeated several times for each locus.
The algorithm relies on a voice activity detection (VAD) mechanism for estimation of the second-order statistics, that is, the algorithm requires another algorithm that detects time instants in the noisy speech signal where the speech is absent.
Second-Hop Channels: for the estimation of the second-hop channel coefficients, the relay phases stay constant for a single channel use and change independently afterwards.
