The extension from the EH model is natural. In addition, there has been literature that proves the power of FNN from a theoretical point of view, whereas this paper fails to make a review in this regard. Among other works, Schmidt-Hieber (2020) gave an exact upper bound of the approximation error for FNNs involving the least-square loss. Since the DeepEH optimizes a likelihood-based loss, this paper builds up its asymptotic properties by following assumptions and proofs of Theorems 1 and 2 in Schmidt-Hieber (2020) as well as theories on empirical processes.
Additional Feedback: 1) In the manuscript, P
mostly represents a probability but sometimes for a cumulative distribution function (e.g., Eqs. (3) and (4) and L44, all in Appendix), which leads to confusion. 2). The notation K
is abused too: it is used both for a known kernel function (e.g., L166) and the number of layers (e.g., L176). 3). What is K b
in estimating baseline hazard (L172)?
2). The notation K is abused too: it is used both for a known kernel function (e.g., L166) and the number of layers (e.g., L176).