Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors1. The abstract has to be reduced, and the obtained results and conclusion lines has to be written.
2. What is the use of Figure 1.
3. how the flexible alternative is evaluated
4. comparison between linear, polynomial and spline has to improve a lot
5. Figures 3 and 4 have to be replaced with a higher resolution. All the equations have to assign a number and have to be cited in the article.
6. Need some more information about P-Spline Estimation.
7. How were the estimated marginal functions calculated.
8. How their optimal values for each sample size, noise and covariate relationship combination were evaluated.
9. Table 2 need some more information, how it was simulated
10. How was cross-validation was done to the penalty parameters?
11. Expand Figure 9 and explain how it was simulated.
12. In discussion section needs some comparison with the existing data. try to cite the latest references.
13. Observed that some grammar mistakes and lines connectivity was not good.
Author Response
Please see the attachment and refer to Reviewer #1 comments.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis work proposes to model a twice penalized P-Spline approximation method to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. This second penalty is created through looking at discrepancies in the marginal value of covariates that exist in both the smaller and larger cohorts. Through a series of data simulations, penalty parameter tunings and model adaptations to consider both a continuous and binary response, we find that our twice penalized approach offers an enhanced model fit over a linear B-Spline model and once penalized P-Spline approximation method.
The authors should discuss some of the experimental findings by the end of the abstract.
In section 1, update the introduction to give some emphasis on the current research questions that this study seeks to address.
explain this part in more details “We now introduce a method of being able to take into account the vertical dataset,”
in equations 1-2, the authors should explain constants presented such as 0.75, 0.45, 0.3.
give more details about the dimensionality reduction methods applied such as PCA, t-sne. Also, it would be better to compare your work with these literature studies “against the following algorithms “Saber, S. and Salem, S. (2023) “High-Performance Technique for Estimating the Unknown Parameters of Photovoltaic Cells and Modules Based on Improved Spider Wasp Optimizer”, Sustainable Machine Intelligence Journal, 5, pp. (2):1–14. doi: 10.61185/SMIJ.2023.55102.”, “Salem, S. (2023) “An Improved Binary Quadratic Interpolation Optimization for 0-1 Knapsack Problems”, Sustainable Machine Intelligence Journal, 4. doi: 10.61185/SMIJ.2023.44101.”
Comments on the Quality of English LanguageModerate editing of English language required
Author Response
Please see the attachment and refer to comments for Reviewer #2.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsArticle is good ,but few small minor correction required. My comments are given on attached file.
Comments for author File: Comments.pdf
Author Response
Please see the attachment and refer to comments under Reviewer #3.
Author Response File: Author Response.pdf
