This paper extends preceding work of robust designs for complete data by incorporating censoring and maximum likelihood estimation.
Some values (e.g. hormones) vary by gender, age, time of day and condition (e.g. pregnancy) so a text on endocrinology should be consulted for complete data.
While there are several techniques for handling missing data in general, the imputation scheme, which replaces missing values with predicted values, is preferred since this scheme can be followed by a standard fusion scheme designed for complete data.
Designs are compared by using relative efficiencies, e.g., between a D-optimal design for homogeneous data and another for heterogeneous data or between a D-optimal design for complete data against another that optimizes designs when data is missing at random.
Figure 8 Steady-state performance of the distributed tracking with consensus algorithm for complete data and noiseless communication case.
It appears that the activity trend for CO oxidation follows this order: Ce100 < Ce80Pr20 < Ce80Zr20 < Ce80Zr10Pr10 (see Table 7 for complete data).
There are some parameter learning algorithms for complete data, including the maximum likelihood estimation method (Darwiche 2009), the Bayesian method, and the gradient descent method (Niculescu et al. 2006).
It can be seen that as the Kalman filter reaches steady state, both the node estimate variance and the Kalman gain converge, as asserted by Theorem 3. Figure 7 Performance of the distributed tracking with consensus algorithm for complete data and noiseless communication case.
The experimental results of through-wall human detection for complete data and compressed data with compression ratios of 1 3 and 1 5 were shown in Figures 1 and 2 and Tables 1 and 2. Figure 1 Through-wall human detection with complete data for brick wall.
"ADH result" denotes the corresponding result obtained by Abadie et al. (2015), "ADH Main Result" their result obtained for complete data Table 2 Results (predictor weights V, donor weights W for main application and in training period, cross-validation criterion) obtained in different ways ADH Min.
With the assumptions above, the covariance matrix (8) in the (j + 1 th consensus iteration after the k th tracking update simplifies to P ¯ ( k, j + 1 ) = A ( j ) P ¯ ( k, j ) A ( j ) T For complete data case, L j) = L j).
