The Stacy-G Class: A New Family of Distributions with Regression Modeling and Applications to Survival Real Data

Round 1

Reviewer 1 Report

In this manuscript authors introduced a new G-calss using logarithmic transform. They obtained some structural properties, they estimeted the parameters using maximum likelihood estimation. Finally, two real data sets show the flexibility of this family for modelling right dkew and left skew data. This manuscript is well written and well organized. I have some major comment as below. 1-What is the main difference between the proposed model and Gamma-G class of distribution? 2-Authors should add some related families with logarithmic transform were developed by second author. 3-Please note that foe a=b=1, the stacy-G reduced to baseline G. 4-Add LR test for comparing the proposed model with submodels in application section. 5- Add coverage probability and Length of confidence interval for simulation study. 6-Send all compuattional codes including plots, simulation and applications for checking the results.

Author Response

Dear, We thank you in advance for your comments and criticisms.
Attached is the file with the answers to your questions.

Author Response File: Author Response.pdf

Reviewer 2 Report

Summary:
The paper "The Stacy-G class: A new family of distributions with regression modeling and its applications" defines a new class of distributions by extending the gamma-G family. Specific properties are introduced and the estimation of the parameters by maximum likelihood is illustrated by Monte
Carlo simulation. Applications to real data contrast the Stacy-G class with modeling alternatives for censored and uncensored data.

 

 

Title:
-ok, could be improved by relating more specifically to specific applications

 

Abstract:
-ok, could be improved by relating more specifically to specific applications (e.g., those mentioned in Sec.8)

 

Introduction:
-l.16: "datasets" instead of "databases"
-l.17-18: stating specific problems/applications here may increase the interest of the reader (see remark on abstract)
-l.20: remove "or so"
-l.20-21: remove line break
-l.32: insert "as" in front of "these"

 

Main part:
-l.87: insert "obtained"
-l.89: insert "moment"
-l.93: as 'optimx' is intended as the successor of the 'optim' function, are there differences or specific reasons why 'optim' is used here?
-l.112: change order of sentences; state that the characteristics differ across individuals and state specific examples for the characteristics in the second sentence.

 

Figures and Tables:
-Fig.1-5: vary line type? This may be beneficial for people with color vision deficiencies
-Fig.5: use different color for GBXII (why introduce an additional color here, which is relatively hard to distinguish from the other color employed in the figure)
-expand captions for the Figures and Tables to be more self-contained

Author Response

Dear, We thank you in advance for your comments and criticisms.
Attached is the file with the answers to your questions.

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear editor and authors,

I reviewed the paper in detail and I think that the manuscript is well organized and written. I do not have particular comments to share with the authors. I think that the paper has a great contribution to the field because it proposes new statistical distributions with extensive analysis.

 

Author Response

Thanks

Reviewer 4 Report

The idea is well described theorotically and analytically.

Author Response

Thanks

Round 2

Reviewer 1 Report

The revised manuscript is very good . I recommend this manuscript for publication in current form.