A New Generalized Logarithmic–X Family of Distributions with Biomedical Data Analysis

Round 1

Reviewer 1 Report

Please see the attached file.

Comments for author File: Comments.pdf

Author Response

All the reviewer comments are carefully taken and each reply is available in a attached file

Author Response File: Author Response.docx

Reviewer 2 Report

See the attached file.

Comments for author File: Comments.pdf

Author Response

All the reviewer comments are carefully taken and each reply is available in a attached file

Author Response File: Author Response.docx

Reviewer 3 Report

The authors introduce a new parametric family of probability density that should be more suitable to model times in biomedical context. The theoretical part explaining this new family is very hard to follow, very formal and the advantages of the parametric framework are missing.  The statistical and inferential properties are only apparently simplified by the parametric framework: in fact the MLE estimators are derived formally, but they aren't express in a closed form with respect to the random sample. These proposed models are used to smooth the empirical densities of some real dataset at a very low level of complexity. I don't understand why a statistician/data analyst should choose such a complicated parametric framework and not use the easier and well known non parametric smoothing techniques. I think the paper doesn't deserve to be published.

 

Author Response

All the reviewer comments are carefully taken and each reply is available in a attached file

Author Response File: Author Response.docx

Reviewer 4 Report

REVIEW

Title of the paper: A New Generalized Logarithmic-X Family of Distributions with Biomedical Data Analysis

Manuscript Number: applsci-2233613

General conclusion: Minor Revision.

 

Comments

After carefully reading the proposed paper, this paper contains an interesting proposal; my overall impression is that the manuscript presents some results that could be useful in practice. I have a good opinion about this work and recommend its acceptance after addressing the following aspects:

My comments are:

1.   What is the type of the parameter   in equation 3?

2.    In equation 1, there exist an error in "x ", please rewrite it again. Also this symbol ""exist in line 124, 130, 131, 133, 136, 149, 152, …… .

3.     From Figure 2, we can conclude that this proposed model will be good to describe and fitting the symmetric data, if the authors can add a new symmetric data in the data analysis section, it will be a positive point on this paper.

4.     From Figure 3, The HF shape of the NGLog-Wieb model can either be increasing, decreasing or unimodal. Can the HF shape be bathtub?

5.     More information around Figures 4, 5, 6 and 7 should be reported.

6.     In simulation study, I note that the value of Bias decreases at n = 50 and increases at n = 75, are this correct?

7.     In Table 6, the value of HQIC for Kumar-Weib distribution is less than NGLog-Weib distribution, is this true, and if so, what is the explanation for that?

8.     The Figures in the manuscript are not clear, if it is possible to put the figures in the eps extension.

 

Author Response

All the reviewer comments are carefully taken and each reply is available in a attached file

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

In most tables, accuracy to the nearest thousandth is sufficient. 6 to 8 digits after the decimal point make these tables less readable because there are too many significant figures.

Author Response

Comments:          In most tables, accuracy to the nearest thousandth is sufficient. 6 to 8 digits after the decimal point make these tables less readable because there are too many significant figures.

Response:           thank you very much for your suggestion. We have incorporated your comments and have removed the extended point after the decimal in all the tables.

 

Author Response File: Author Response.docx

Reviewer 2 Report

This version has been significantly improved.

1- The motivations and contributions of this paper should be well addressed in the Introduction section. 

2- In addition, please improve the article by citing recent work on lifetime data analysis, e.g.,

Luo C, Shen L, Xu A. Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints, Reliability Engineering and System Safety. 2022, 218, 108136.

Zhuang L, Xu A, Wang X. A prognostic driven predictive maintenance framework based on Bayesian deep learning, Reliability Engineering and System Safety. 2023, https://doi.org/10.1016/j.ress.2023.109181

Author Response

Response to reviewer-2 Report (Round 2)

Title: A New Generalized Logarithmic-X Family of Distributions with Biomedical Data Analysis

 

Comments and suggestions:

Comment 1:      The motivations and contributions of this paper should be well addressed in the Introduction section. 

Response:       thank you for your suggestion. We have incorporated your comment (Please see the introduction section of the updated manuscript)

Comment 2:      In addition, please improve the article by citing recent work on lifetime data analysis, e.g.,

Luo C, Shen L, Xu A. Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints, Reliability Engineering and System Safety. 2022, 218, 108136.

Zhuang L, Xu A, Wang X. A prognostic driven predictive maintenance framework based on Bayesian deep learning, Reliability Engineering and System Safety. 2023, https://doi.org/10.1016/j.ress.2023.109181

Response:       We appreciate your valuable suggestions. We have cited the relevant references in the updated manuscript.      

 

Author Response File: Author Response.docx

Reviewer 3 Report

I think the authors didn't respond to my criticisms and I still believe that the paper doesn't deserve to be published.

Author Response

All the comments of the reviewer are taken seriously and addressed in an attached file. 

Author Response File: Author Response.docx