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Given that education in our data is categorized in discrete cells (as discussed in the appendix), we continue to adopt a non-parametric approach.
We confirm that this holds in the numerical solution of the problem, as discussed in the Appendix 2. The additional state variable, ({M^{e}_{t}},) is only used when maternity leave is available, which depends on a child being born that year and on the policy regime being analysed.
As discussed in the Appendix, the sequence ({tilde {boldsymbol {x}}^{(n)}}_{nin {mathbb N}}subset mathcal {X}) for some (tilde {boldsymbol {x}}^{(0)}in mathcal {X}) generated by (14) produces a non-increasing sequence ({h tilde {boldsymbol {x}}^{(n)})}_{nin {mathbb N}}) of objective values.
We note that connectivity correlations are a specific property of networks shaped by asymmetric dynamics, and are absent in the case of symmetric dynamics, as discussed in the appendix.
Similar(56)
The weak forms for the penalty, LM and PL formulations can be derived similarly as outlined in Appendix A. In order to solve the mechanical system introduced in Eq. (30) (as well as the others discussed in the Appendix A), we look to use the global Shamanskii Newton Raphson (SNR) method [54].
As discussed in the previous step.
The results of applying similar methods to the theta model are also discussed in the Appendix.
The automated ground-truth image selection approach is discussed in the "Appendix".
Further technical details regarding these methods are discussed in the Appendix of this paper.
These are discussed in the Appendix.
This is discussed in the Appendix.
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