Research Article
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Year 2026, Volume: 55 Issue: 2 , 713 - 733 , 18.02.2026

Priya Priya , Anoop Kumar

https://doi.org/10.15672/hujms.1825896
https://izlik.org/JA75NT42PS

Abstract

References

  • [1] V.D. Naik and P.C Gupta, A note on estimation of mean with known population proportion of an auxiliary character, Jour. Ind. Soc. Agr. Stat. 48 (2), 151-158, 1996.
  • [2] R. Singh, P. Chauhan, N. Sawan and F. Smarandache, Ratio estimators in simple random sampling using information on auxiliary attribute, Pak. J. Stat. Oper. Res. IV (1), 47-53, 2008.
  • [3] A.M. Abd-Elfattah, E.A. El-Sherpieny, S.M. Mohamed, and O.F. Abdou, Improvement in estimating the population mean in simple random sampling using information on auxiliary attribute, Appl. Math. Comput. 215, 4198–4202, 2010.
  • [4] L.K. Grover and P. Kaur, An improved exponential estimator of finite population mean in simple random sampling using an auxiliary attribute, Appl. Math. Comput. 218 (7), 3093-3099, 2011.
  • [5] E. Zamanzade and M. Mahdizadeh, A more efficient proportion estimator in ranked set sampling, Stat Prob Lett. 129, 28-33, 2017.
  • [6] E. Zamanzade and X. Wang, Proportion estimation in ranked set sampling in the presence of tie information, Comput. Stat. 33 (3), 1349-1366, 2018.
  • [7] E. Zamanzade and M. Mahdizadeh, Estimating the population proportion in pair ranked set sampling with application to air quality monitoring, J. Appl. Stat. 45 (3), 426-437, 2018.
  • [8] T. Zaman and C. Kadilar, Novel family of exponential estimators using information of auxiliary attribute, J. Stat. Manag. Syst. 22 (8), 1499-1509, 2019.
  • [9] T. Zaman, Generalized exponential estimators for the finite population mean, Stat. Transit. New Ser. 21 (1), 159-168, 2020.
  • [10] S. Bhushan and A. Kumar, Log type estimators of population mean under ranked set sampling, In Predictive Analytics using Statistics and Big Data: Concepts and Modeling, Bentham Science Publishers, 2020.
  • [11] E. Zamanzade and M. Mahdizadeh, Using ranked set sampling with extreme ranks in estimating the population proportion, Stat. Methods Med. Res. 29 (1), 165-177, 2020.
  • [12] S. Ahmad, E. Zahid, J. Shabbir, M. Aamir, W. Emam, Y. Tashkandy and S. Ahmad, A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute, Heliyon 9 (6), e17269, 2023.
  • [13] H. E. Semary, S. Ahmad, I. Elbatal, C. Chesneau, M. Elgarhy and E. M. Almetwally, New comprehensive class of estimators for population proportion using auxiliary attribute: Simulation and an application, Alex. Eng. J. 99, 130-136, 2024.
  • [14] S. Ahmad and J. Shabbir, Proportion estimation using enhanced class of estimators under simple random sampling: Application with real data sets and simulation, Meas- Interdiscip. Res. 23 (3), 297-309, 2025.
  • [15] F. Smarandache, Neutrosophic set - a generalization of the intuitionistic fuzzy set, Int. J. Pure Appl. Math. 24, 287-297, 2005.
  • [16] Z. Tahir, H. Khan, M. Aslam, J. Shabbir, Y. Mahmood and F. Smarandache, Neutrosophic ratio-type estimators for estimating the population mean, Complex Intell. Syst., 7 (6), 2991-3001, 2021.
  • [17] S. Kumar, S.P. Kour, M. Choudhary and V. Sharma, Determination of population mean using neutrosophic exponential-type estimator, Lobachevskii J. Math. 43 (11), 3359-3367, 2022.
  • [18] S.K. Yadav and F. Smarandache, Generalized neutrosophic sampling strategy for elevated estimation of population mean, Neutrosophic Sets Syst. 53, 1-20, 2023.
  • [19] A.M. Alomair and U. Shahzad, Neutrosophic mean estimation of sensitive and nonsensitive variables with robust Hartley–Ross-Type estimators, Axioms 12 (6), 578, 2023.
  • [20] V.K. Yadav and S. Prasad, Neutrosophic estimators for estimating the population mean in survey sampling, Meas-Interdiscip. Res. 22 (4), 373-397, 2024.
  • [21] M.A. Alqudah, M. Zayed, M. Subzar and S.A. Wani, Neutrosophic robust ratio type estimator for estimating finite population mean, Heliyon 10 (8), 1-12, 2024.
  • [22] R. Singh, A. Kumari, F. Smarandache and S.N. Tiwari, Construction of almost unbiased estimator for population mean using neutrosophic information, Neutrosophic Sets Syst. 76 (1), 2024.
  • [23] S. Al-Marzouki and S. Ahmad, Developing a generalized class of estimators for estimation of population mean using neutrosophic approach, Eur. J. Pure Appl. Math. 18 (3), 6477-6477, 2025.
  • [24] S. Al-Marzouki and S. Ahmad, An enhanced class of estimators for the population mean using neutrosophic statistics: a case study of the Islamabad Stock Exchange, Eur. J. Pure Appl. Math. 18 (3), 6434-6434, 2025.
  • [25] A. Kumar and Priya, Efficient neutrosophic exponential estimators for accurate population mean estimation in simple random sampling, J. Fuzzy Ext. Appl. e214646, 2025.
  • [26] Priya and A. Kumar, Robust neutrosophic exponential estimators of population mean in the presence of uncertainty, Qual. Quant. 59, 3827–3850, 2025.
  • [27] A. Yadav, D.K. Yadav, S.K. Yadav and T. Zaman, A novel neutrosophic estimation method for handling uncertainty in population mean estimation, Qual. Quant., 1-26, 2025.
  • [28] V.K. Yadav and S. Prasad, Neutrosophic estimators in two-phase survey sampling, Neutrosophic Sets Syst. 61 (1), 29, 2023.
  • [29] A. Kumari, R. Singh and F. Smarandache, New modification of ranked set sampling for estimating population mean: neutrosophic median ranked set sampling with an application to demographic data, Int. J. Comput. Intell. Syst. 17 (1), 210, 2024.
  • [30] B. Khanna, Generalized regressed exponential estimator for estimation of mean under neutrosophic ranked set sampling, Natl. Acad. Sci. Lett. 47, 719–725, 2024.
  • [31] A. Kumar and V. Kumar, Estimating population mean of climate data via neutrosophic imputation in simple random sampling, Qual. Quant., 2025.
  • [32] A. Kumar and V. Kumar, Uncertainty based efficient neutrosophic imputation methods for population mean, Natl. Acad. Sci. Lett., 2025.
  • [33] A. Kumar and V. Kumar, Simulation based evaluation of neutrosophic exponential imputations of population mean using neutrosophic ranked set sampling, Frankl. Open 14, 100468, 2025.
  • [34] A. Kumar, Priya and A. M. Alomair, Efficient classes of estimators for estimating indeterminate population mean using neutrosophic ranked set sampling, AIMS Mathematics 10 (4), 8946-8964, 2025.
  • [35] V. Singh, R. Singh and A. Kumari, Application of neutrosophic stratified ranked set sampling: an efficient sampling technique in the estimation of average relative humidity in USA, Int. J. Neutrosophic Sci. 26 (3), 166-190, 2025.
  • [36] A. Kumari, A. Singh and V. Singh, Handling imprecise climate information: a neutrosophic generalized estimator under stratified ranked set sampling for population mean, Int. J. Math. Math. Sci. 2025, 1-23, 4935567, 2025.
  • [37] F. Smarandache, Introduction to neutrosophic statistics, Sitech & Education Publishing, 2014.
  • [38] G.O. Kadilar, A new exponential type estimator for the population mean in simple random sampling, J. Mod. Appl. Stat. Methods. 15 (2), 2016.
  • [39] S. Bhushan, A. Kumar, S. Singh and S.Kumar, An improved class of estimators of population mean under simple random sampling, The Philippine Statistician, 70 (1), 33-47, 2021.

Neutrosophic-based estimators for the population mean incorporating attribute information

Year 2026, Volume: 55 Issue: 2 , 713 - 733 , 18.02.2026

Priya Priya , Anoop Kumar

https://doi.org/10.15672/hujms.1825896
https://izlik.org/JA75NT42PS

Abstract

In the presence of indeterminate, inconsistent, or imprecise data, the conventional estimation procedures often fail to give reliable results. To address this limitation, the present study develops novel neutrosophic estimation procedures for the population mean by incorporating auxiliary attribute information under simple random sampling (SRS). The proposed approach extends the conventional attribute-based estimation procedures to the neutrosophic framework, where uncertainty, truthiness, and falsity are simultaneously represented. We adapted some prominent conventional estimators under neutrosophic environment and developed a new class of neutrosophic estimators with their properties, such as bias and mean square error (MSE) up to first-order approximation. Comparative efficiency analyses are conducted with respect to the adapted estimators to highlight the gain in precision achieved through the proposed estimators. A comprehensive simulation study, along with an empirical illustration on real-world data, demonstrates that the developed neutrosophic estimators consistently outperform the adapted neutrosophic estimators. The developed framework thus provides a robust alternative for mean estimation when uncertain or imprecise attribute information is available.

Keywords

Auxiliary attribute efficiency comparison mean square error neutrosophic estimation population mean estimation uncertainty

Supporting Institution

The first author gratefully acknowledges the UGC–CSIR for providing Junior Research Fellowship (JRF) support under Ref. No. 231610074330, which was instrumental in enabling this research project.

References

  • [1] V.D. Naik and P.C Gupta, A note on estimation of mean with known population proportion of an auxiliary character, Jour. Ind. Soc. Agr. Stat. 48 (2), 151-158, 1996.
  • [2] R. Singh, P. Chauhan, N. Sawan and F. Smarandache, Ratio estimators in simple random sampling using information on auxiliary attribute, Pak. J. Stat. Oper. Res. IV (1), 47-53, 2008.
  • [3] A.M. Abd-Elfattah, E.A. El-Sherpieny, S.M. Mohamed, and O.F. Abdou, Improvement in estimating the population mean in simple random sampling using information on auxiliary attribute, Appl. Math. Comput. 215, 4198–4202, 2010.
  • [4] L.K. Grover and P. Kaur, An improved exponential estimator of finite population mean in simple random sampling using an auxiliary attribute, Appl. Math. Comput. 218 (7), 3093-3099, 2011.
  • [5] E. Zamanzade and M. Mahdizadeh, A more efficient proportion estimator in ranked set sampling, Stat Prob Lett. 129, 28-33, 2017.
  • [6] E. Zamanzade and X. Wang, Proportion estimation in ranked set sampling in the presence of tie information, Comput. Stat. 33 (3), 1349-1366, 2018.
  • [7] E. Zamanzade and M. Mahdizadeh, Estimating the population proportion in pair ranked set sampling with application to air quality monitoring, J. Appl. Stat. 45 (3), 426-437, 2018.
  • [8] T. Zaman and C. Kadilar, Novel family of exponential estimators using information of auxiliary attribute, J. Stat. Manag. Syst. 22 (8), 1499-1509, 2019.
  • [9] T. Zaman, Generalized exponential estimators for the finite population mean, Stat. Transit. New Ser. 21 (1), 159-168, 2020.
  • [10] S. Bhushan and A. Kumar, Log type estimators of population mean under ranked set sampling, In Predictive Analytics using Statistics and Big Data: Concepts and Modeling, Bentham Science Publishers, 2020.
  • [11] E. Zamanzade and M. Mahdizadeh, Using ranked set sampling with extreme ranks in estimating the population proportion, Stat. Methods Med. Res. 29 (1), 165-177, 2020.
  • [12] S. Ahmad, E. Zahid, J. Shabbir, M. Aamir, W. Emam, Y. Tashkandy and S. Ahmad, A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute, Heliyon 9 (6), e17269, 2023.
  • [13] H. E. Semary, S. Ahmad, I. Elbatal, C. Chesneau, M. Elgarhy and E. M. Almetwally, New comprehensive class of estimators for population proportion using auxiliary attribute: Simulation and an application, Alex. Eng. J. 99, 130-136, 2024.
  • [14] S. Ahmad and J. Shabbir, Proportion estimation using enhanced class of estimators under simple random sampling: Application with real data sets and simulation, Meas- Interdiscip. Res. 23 (3), 297-309, 2025.
  • [15] F. Smarandache, Neutrosophic set - a generalization of the intuitionistic fuzzy set, Int. J. Pure Appl. Math. 24, 287-297, 2005.
  • [16] Z. Tahir, H. Khan, M. Aslam, J. Shabbir, Y. Mahmood and F. Smarandache, Neutrosophic ratio-type estimators for estimating the population mean, Complex Intell. Syst., 7 (6), 2991-3001, 2021.
  • [17] S. Kumar, S.P. Kour, M. Choudhary and V. Sharma, Determination of population mean using neutrosophic exponential-type estimator, Lobachevskii J. Math. 43 (11), 3359-3367, 2022.
  • [18] S.K. Yadav and F. Smarandache, Generalized neutrosophic sampling strategy for elevated estimation of population mean, Neutrosophic Sets Syst. 53, 1-20, 2023.
  • [19] A.M. Alomair and U. Shahzad, Neutrosophic mean estimation of sensitive and nonsensitive variables with robust Hartley–Ross-Type estimators, Axioms 12 (6), 578, 2023.
  • [20] V.K. Yadav and S. Prasad, Neutrosophic estimators for estimating the population mean in survey sampling, Meas-Interdiscip. Res. 22 (4), 373-397, 2024.
  • [21] M.A. Alqudah, M. Zayed, M. Subzar and S.A. Wani, Neutrosophic robust ratio type estimator for estimating finite population mean, Heliyon 10 (8), 1-12, 2024.
  • [22] R. Singh, A. Kumari, F. Smarandache and S.N. Tiwari, Construction of almost unbiased estimator for population mean using neutrosophic information, Neutrosophic Sets Syst. 76 (1), 2024.
  • [23] S. Al-Marzouki and S. Ahmad, Developing a generalized class of estimators for estimation of population mean using neutrosophic approach, Eur. J. Pure Appl. Math. 18 (3), 6477-6477, 2025.
  • [24] S. Al-Marzouki and S. Ahmad, An enhanced class of estimators for the population mean using neutrosophic statistics: a case study of the Islamabad Stock Exchange, Eur. J. Pure Appl. Math. 18 (3), 6434-6434, 2025.
  • [25] A. Kumar and Priya, Efficient neutrosophic exponential estimators for accurate population mean estimation in simple random sampling, J. Fuzzy Ext. Appl. e214646, 2025.
  • [26] Priya and A. Kumar, Robust neutrosophic exponential estimators of population mean in the presence of uncertainty, Qual. Quant. 59, 3827–3850, 2025.
  • [27] A. Yadav, D.K. Yadav, S.K. Yadav and T. Zaman, A novel neutrosophic estimation method for handling uncertainty in population mean estimation, Qual. Quant., 1-26, 2025.
  • [28] V.K. Yadav and S. Prasad, Neutrosophic estimators in two-phase survey sampling, Neutrosophic Sets Syst. 61 (1), 29, 2023.
  • [29] A. Kumari, R. Singh and F. Smarandache, New modification of ranked set sampling for estimating population mean: neutrosophic median ranked set sampling with an application to demographic data, Int. J. Comput. Intell. Syst. 17 (1), 210, 2024.
  • [30] B. Khanna, Generalized regressed exponential estimator for estimation of mean under neutrosophic ranked set sampling, Natl. Acad. Sci. Lett. 47, 719–725, 2024.
  • [31] A. Kumar and V. Kumar, Estimating population mean of climate data via neutrosophic imputation in simple random sampling, Qual. Quant., 2025.
  • [32] A. Kumar and V. Kumar, Uncertainty based efficient neutrosophic imputation methods for population mean, Natl. Acad. Sci. Lett., 2025.
  • [33] A. Kumar and V. Kumar, Simulation based evaluation of neutrosophic exponential imputations of population mean using neutrosophic ranked set sampling, Frankl. Open 14, 100468, 2025.
  • [34] A. Kumar, Priya and A. M. Alomair, Efficient classes of estimators for estimating indeterminate population mean using neutrosophic ranked set sampling, AIMS Mathematics 10 (4), 8946-8964, 2025.
  • [35] V. Singh, R. Singh and A. Kumari, Application of neutrosophic stratified ranked set sampling: an efficient sampling technique in the estimation of average relative humidity in USA, Int. J. Neutrosophic Sci. 26 (3), 166-190, 2025.
  • [36] A. Kumari, A. Singh and V. Singh, Handling imprecise climate information: a neutrosophic generalized estimator under stratified ranked set sampling for population mean, Int. J. Math. Math. Sci. 2025, 1-23, 4935567, 2025.
  • [37] F. Smarandache, Introduction to neutrosophic statistics, Sitech & Education Publishing, 2014.
  • [38] G.O. Kadilar, A new exponential type estimator for the population mean in simple random sampling, J. Mod. Appl. Stat. Methods. 15 (2), 2016.
  • [39] S. Bhushan, A. Kumar, S. Singh and S.Kumar, An improved class of estimators of population mean under simple random sampling, The Philippine Statistician, 70 (1), 33-47, 2021.
There are 39 citations in total.

Details

Primary Language English
Subjects Theory of Sampling
Journal Section Research Article
Authors

Priya Priya 0009-0001-2596-0935

Anoop Kumar 0000-0003-2775-6548

Submission Date November 18, 2025
Acceptance Date February 2, 2026
Early Pub Date February 18, 2026
Publication Date February 18, 2026
DOI https://doi.org/10.15672/hujms.1825896
IZ https://izlik.org/JA75NT42PS
Published in Issue Year 2026 Volume: 55 Issue: 2

Cite

APA Priya, P., & Kumar, A. (2026). Neutrosophic-based estimators for the population mean incorporating attribute information. Hacettepe Journal of Mathematics and Statistics, 55(2), 713-733. https://doi.org/10.15672/hujms.1825896
AMA 1.Priya P, Kumar A. Neutrosophic-based estimators for the population mean incorporating attribute information. Hacettepe Journal of Mathematics and Statistics. 2026;55(2):713-733. doi:10.15672/hujms.1825896
Chicago Priya, Priya, and Anoop Kumar. 2026. “Neutrosophic-Based Estimators for the Population Mean Incorporating Attribute Information”. Hacettepe Journal of Mathematics and Statistics 55 (2): 713-33. https://doi.org/10.15672/hujms.1825896.
EndNote Priya P, Kumar A (April 1, 2026) Neutrosophic-based estimators for the population mean incorporating attribute information. Hacettepe Journal of Mathematics and Statistics 55 2 713–733.
IEEE [1]P. Priya and A. Kumar, “Neutrosophic-based estimators for the population mean incorporating attribute information”, Hacettepe Journal of Mathematics and Statistics, vol. 55, no. 2, pp. 713–733, Apr. 2026, doi: 10.15672/hujms.1825896.
ISNAD Priya, Priya - Kumar, Anoop. “Neutrosophic-Based Estimators for the Population Mean Incorporating Attribute Information”. Hacettepe Journal of Mathematics and Statistics 55/2 (April 1, 2026): 713-733. https://doi.org/10.15672/hujms.1825896.
JAMA 1.Priya P, Kumar A. Neutrosophic-based estimators for the population mean incorporating attribute information. Hacettepe Journal of Mathematics and Statistics. 2026;55:713–733.
MLA Priya, Priya, and Anoop Kumar. “Neutrosophic-Based Estimators for the Population Mean Incorporating Attribute Information”. Hacettepe Journal of Mathematics and Statistics, vol. 55, no. 2, Apr. 2026, pp. 713-3, doi:10.15672/hujms.1825896.
Vancouver 1.Priya Priya, Anoop Kumar. Neutrosophic-based estimators for the population mean incorporating attribute information. Hacettepe Journal of Mathematics and Statistics. 2026 Apr. 1;55(2):713-3. doi:10.15672/hujms.1825896

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[1] V.I. Arnold, Singularities of Caustics and Wave Fronts, Mathematics and Its Applications, Kluwer Academic Publishers, 1990.
[2] B. Banaschewski, Extensions of topological spaces, Canad. Math. Bull. 7 (1), 1-22, 1964.
[3] A. Chirvasitu, Centers of categorified endomorphism rings, arXiv: 2009.09195 [math.CT].
[4] H. Ehrig and H. Herrlich, The construct PRO of projection spaces: its internal structure, in: Categorical Methods in Computer Science, Lecture Notes in Computer Science 393, 286-293, Springer-Verlag, Berlin, 1989.
[5] C.M. Hurvich and C.L. Tsai, Regression and time series model selection in small samples, Biometrika 76 (2), 297-307, 1989.
[6] T.Y. Lam and P.P. Nielsen, Jacobson's lemma for Drazin inverses, Ring theory and its applications, 185-195, Contemp. Math. 609, Amer. Math. Soc., Providence, RI, 2014.
[7] K. Rökaeus, Grothendieck rings and motivic integration, Ph.D. thesis, Stockholm University, 2009.

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  • Acknowledgements: Provide details regarding any assistance received during the research or manuscript preparation process. 
  • Author Contributions: Clearly specify the contributions of each author to the work presented in the manuscript. 
  • Conflict of Interest: Declare any potential conflicts of interest related to the research or publication. 
  • Funding: Specify the sources of funding that supported the research, including grant numbers where applicable. The names of funding organizations should be written in full. Example statements: (1) “This work was supported by […] (Grant numbers […] and […]). Author A.B. has received research support from Company A.” or (2) “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” 
  • Data Availability: Describe the accessibility of the data used in the research. Indicate whether the data is publicly available or provide details on how it can be accessed. 


Requirements Upon Acceptance

  • Following the acceptance of a manuscript for publication, authors are required to ensure that their work is fully formatted in accordance with the HJMS Template, which is provided above. Therefore, authors must submit the class file of HJMS template, the tex file of the manuscript alongside all associated figure/table files.
  • Please note that manuscripts not prepared in the specified format, as outlined on this page, will not be considered for publication in the journal.

Hacettepe Journal of Mathematics and Statistics (HJMS) is committed to maintaining the highest standards of publication ethics and to supporting ethical research practices and strictly adheres to the Committee on Publication Ethics (COPE) Code of Conduct for Journal Publishers. We encourage journal editors to follow the COPE Code of Conduct for Journal Editors and to refer reviewers to the COPE Ethical Guidelines for Peer Reviewers as appropriate. In the development of the Policies and Ethics of HJMS, the following sources have been utilized:


Responsibilities of Authors

  • Articles submitted for publication must comply with the highest ethical standards in scientific publishing, including but not limited to avoiding plagiarism, falsification, distortion, republishing, data slicing, unfair authorship, failure to specify supporting organizations, and not adhering to ethical guidelines in animal studies.
  • Authors conducting research that involves qualitative or quantitative approaches requiring data collection from participants (e.g., questionnaires, interviews, focus group work, observation, experimentation, clinical trials on humans or animals) must obtain Ethics Committee Permission. Authors must also obtain and specify permission from the owners for the use of scales, surveys, and photographs.
  • Any changes in authorship, whether prior to or after publication, must be agreed upon by all authors, including those being added or removed. The corresponding author is responsible for obtaining confirmation from all co-authors and providing evidence of this to the editorial office with a full explanation regarding the reason for the change.
  • All authors must agree on the corresponding author and the order of co-authors. They should also verify their affiliations and contact details before submitting the manuscript.
  • If the corresponding author wishes to withdraw a paper from the journal, they must inform the editor with the consent of all other authors.
  • All authors must be able to publicly defend their paper. If requested during the review process, an author must provide the dataset to the reviewers.
  • Authors must declare any competing interests in their cover letters or on the manuscript submission form within the journal’s online peer review system.
  • All manuscripts must be well-written in English. Papers that do not meet the journal’s standards for language and clarity will be returned for revision and may be rejected solely on these grounds.
  • After acceptance, authors must adhere to the writing rules of Hacettepe Journal of Mathematics and Statistics (HJMS) and use the HJMS style files. Authors must not share their paper with the HJMS logo during the review process on any public platform.


Responsibilities of Editors

  • The editor of the journal is solely and independently responsible for deciding which articles submitted to the journal will be published. Based on the review report, the editor holds complete authority to accept, reject, or request modifications to the manuscript.
  • The editor’s decisions are guided by the policies of the journal’s editorial board and are also constrained by legal requirements, including issues related to libel, copyright infringement, and plagiarism.
  • The editor evaluates manuscripts and makes decisions regarding acceptance or rejection based solely on the importance, originality, clarity, and relevance of the paper to the journal’s scope. All research papers must undergo evaluation by at least two external and independent reviewers. If necessary, the editor may seek further opinions.
  • The editor ensures that each manuscript submitted to journal is evaluated solely based on its intellectual content, without regard to the author’s race, gender, sexual orientation, ethnic origin, citizenship, religious beliefs, or political views.
  • The editor is responsible for maintaining the confidentiality of material submitted to journal while it is under review.
  • The editor ensures that peer review is fair, unbiased, and conducted promptly, so authors do not experience unnecessary delays in the publication process.
  • The editor must not use unpublished material disclosed in a submitted manuscript for their own research without the author’s written consent.
  • The editor must take appropriate action if misconduct is reported regarding the integrity of a submitted manuscript or published paper, in collaboration with the publisher. These actions may include contacting the author of the manuscript, addressing the respective complaints or claims, and possibly involving relevant institutions and research bodies.
  • The editor must not participate in decisions concerning papers they have authored themselves, or papers written by family members or colleagues, or those relating to products or services in which they have an interest.
  • Any changes to the authorship list, including additions, deletions, or rearrangements of author names, must be approved by the editor.


Responsibilities of Reviewers

  • Reviewers should refrain from directly contacting the author(s) of the paper they are assigned to review.
  • Reviewers are required to maintain the confidentiality of the ideas and findings presented in the manuscript under review. The paper should not be shared with others and should not be used for the reviewer’s own research purposes. 
  • Reviewers must evaluate the manuscripts objectively and professionally. Reviews should be constructive, well-written, and provide clear, helpful feedback that the authors can use to improve their work.
  • If a selected reviewer feels they are not sufficiently qualified to review the manuscript’s subject matter, they should inform the editor and excuse themselves from the review process.
  • Reviewers are expected to complete the "Evaluation Form for Referees" with detailed and explanatory arguments supporting their final decisions.
  • Reviewers should submit their referee reports within the timeframe provided by the journal. If a reviewer is unable to meet the deadline, they should notify the editor promptly.
  • In the event of a potential conflict of interest, the reviewer must inform the editor as soon as possible.
  • Reviewers should report any ethical concerns they have regarding the paper, such as issues related to duplication, plagiarism, or other ethical violations, to the editor.



Peer-Review Policy


The journal is dedicated to maintaining the highest standards of peer review integrity. The editorial board would like to express sincere gratitude to all past, present, and future reviewers for their invaluable time and significant contribution to the success of the journal. The submitting author, typically the corresponding author, is responsible for the manuscript throughout the submission and peer review process. It is the submitting author’s responsibility to ensure that all eligible co-authors are included in the author list.

1. Initial Evaluation: Upon submission, your manuscript will undergo an initial evaluation by a member of the editorial board. If your manuscript is rejected at this stage, the corresponding author will be promptly notified of the decision. If the manuscript does not meet the journal’s submission requirements, it will be returned for amendments prior to the peer review process.

2. Peer Review Process: Once your manuscript passes the initial evaluation, it will proceed to the peer review stage. You can log in at any time to check the status of your manuscript. We will notify you promptly when a decision has been made.

The editor may consult up to four reviewers, selected from experts in the relevant field of the manuscript. Please note that this journal follows a single-blind review process, meaning the identity of the reviewers is not disclosed to the authors at any time. The review process is strictly confidential.

3. Reviewer Suggestions: At the time of submission, authors are required to provide a suggestion for an Area Editor (via the “Note to Editor”) and propose up to four potential reviewers, including their full names and email addresses. The suggested reviewers should represent a geographically diverse group. It is important to ensure that the proposed reviewers have no prior conflict of interest with the authors (e.g., no former co-authorship, mentoring relationships, or current collaboration). While the editorial office may or may not use the suggested reviewers, this list can help expedite the process.

4. Final Decision: After the review process is complete, the editor will make the final decision regarding acceptance or rejection of the manuscript. This decision will be communicated to the corresponding author, along with the reviewers’ comments and suggestions.

5. Limitations: The authors must notice that only two submissions per author per year will be considered. Once a paper is under review or has been accepted for publication, all co-authors need to wait 6 months from the submission date before submitting another paper to Hacettepe Journal of Mathematics and Statistics.


Copyright

  • Upon acceptance of an article for publication, the copyright of the article is transferred to Hacettepe Journal of Mathematics and Statistics.
  • For any materials previously published elsewhere that require permission for reproduction, the author must obtain and provide the necessary copyright permissions.  
  • The copyright in a published article rests solely with the Faculty of Science of Hacettepe University, and the paper may not be reproduced in whole or in part by any means whatsoever without prior written permission.
Notice

  • Manuscripts must be original and should not have been previously published or simultaneously submitted for publication elsewhere.
  • The author(s) bear full responsibility for the content of their published work, and its publication does not imply endorsement by the editors or the publisher.

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Citation Indexes

TR Dizin
Science Citation Index Expanded (SCI-EXPANDED)
Scopus

Other Indexes

zbMATH
American Mathematical Society

Owner of the Journal

Default avatar
Hatice MERGEN
Animal Cell and Molecular Biology

Editor-in-Chief (Mathematics)

Murat DIKER

Editor-in-Chief (Statistics)

Gamze ÖZEL KADILAR

Production Editors

Talha ARIKAN
Berke KALEBOĞAZ
Orhan Oğulcan TUNCER
İsmail ASLAN
Esra POLAT
Sema YAYLA
Semra TÜRKAN
Uğur SERT
Mustafa Murat ARAT

Area Editors (Mathematics)

Bülent ÜNAL
Algebraic and Differential Geometry
Serap ÖZTOP KAPTANOĞLU
Mathematical Sciences, Lie Groups, Harmonic and Fourier Analysis, Operator Algebras and Functional Analysis, Real and Complex Functions (Incl. Several Variables), Pure Mathematics (Other)
Sibei YANG
Mathematical Sciences, Lie Groups, Harmonic and Fourier Analysis
Sergio ESTRADA
Mathematical Sciences, Category Theory, K Theory, Homological Algebra
Naoki ENDO
Mathematical Sciences
Adam LECKO
Mathematics Education, Real and Complex Functions (Incl. Several Variables)
Ekin ÖZMAN
Algebra and Number Theory
Abdullah ÖZBEKLER
Ordinary Differential Equations, Difference Equations and Dynamical Systems, Dynamical Systems in Applications
Cihan ORHAN
Operator Algebras and Functional Analysis
Rodrigo HERNáNDEZ-GUTIéRREZ
Topology
Mehmetcik PAMUK
Topology
Zeng SHENGDA
Partial Differential Equations, Theoretical and Applied Mechanics in Mathematics, Dynamical Systems in Applications, Calculus of Variations, Mathematical Aspects of Systems Theory and Control Theory, Applied Mathematics (Other)
Angel Del Rio ANGEL DEL RIO
Mathematical Sciences
Xin ZHANG
Mathematical Sciences, Combinatorics and Discrete Mathematics (Excl. Physical Combinatorics)
Azer KHANMAMEDOV
Partial Differential Equations
Changguo SHAO
Group Theory and Generalisations
Yunus E. ZEYTUNCU
Operator Algebras and Functional Analysis

Area Editors (Statistics)

Adil BAYKASOĞLU
Decision Support and Group Support Systems, Machine Learning, Artificial Intelligence, Fuzzy Computation, Evolutionary Computation, Planning and Decision Making, Operations Research, Operations Research İn Mathematics, CAD/CAM Systems, Multiple Criteria Decision Making, Manufacturing Management, Stochastic (Probability ) Process, Manufacturing and Service Systems, Optimization in Manufacturing, Production and Operations Management, Supply Chains
Hamparsum BOZDOGAN
Econometric and Statistical Methods, Computational Statistics, Statistical Analysis, Statistical Data Science
Victor LEIVA
Big Data, Data Mining and Knowledge Discovery, Data Analysis, Artificial Intelligence (Other), Biostatistics, Computational Statistics, Statistical Analysis, Statistical Data Science, Applied Statistics
B M Golam KIBRIA
Statistics
Vali ASIMIT
Econometric and Statistical Methods, Operations Research, Statistical Data Science, Risk Analysis
Narayanaswamy BALAKRISHNAN
Probability Theory
Yiying ZHANG
Probability Theory, Stochastic Analysis and Modelling, Applied Statistics, Stochastic (Probability ) Process, Actuary
İsmihan BAYRAMOĞLU
Statistics, Statistical Analysis, Statistical Theory, Probability Theory
Manuel FRANCO NICOLáS
Probability Theory, Applied Statistics
Debasis KUNDU
Statistical Analysis, Applied Statistics
Refik SOYER
Time-Series Analysis, Stochastic Analysis and Modelling, Risk Analysis, Stochastic (Probability ) Process
Gauss M. CORDEIRO
Statistical Analysis, Applied Statistics
Binyan JIANG
Large and Complex Data Theory, Statistical Data Science
Mustafa Murat ARAT
Machine Learning, Deep Learning, Big Data, Data Analysis, Artificial Intelligence, Natural Language Processing, Artificial Intelligence (Other)

For more information about the journal, please visit: https://dergipark.org.tr/en/pub/hujms