Rajesh Singh

572 total citations
46 papers, 342 citations indexed

About

Rajesh Singh is a scholar working on Statistics and Probability, Epidemiology and Sociology and Political Science. According to data from OpenAlex, Rajesh Singh has authored 46 papers receiving a total of 342 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Statistics and Probability, 4 papers in Epidemiology and 3 papers in Sociology and Political Science. Recurrent topics in Rajesh Singh's work include Survey Sampling and Estimation Techniques (45 papers), Statistical Methods and Bayesian Inference (12 papers) and Statistical Distribution Estimation and Applications (8 papers). Rajesh Singh is often cited by papers focused on Survey Sampling and Estimation Techniques (45 papers), Statistical Methods and Bayesian Inference (12 papers) and Statistical Distribution Estimation and Applications (8 papers). Rajesh Singh collaborates with scholars based in India, Saudi Arabia and Pakistan. Rajesh Singh's co-authors include Prayas Sharma, Housila P. Singh, Florentín Smarandache, Nirmala Sawan, Pankaj Chauhan, Hemant Verma, D. S. Tracy, Carlos N. Bouza, Irfan Ali and Abdullah Ali H. Ahmadini and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Mathematics and Computation and Heliyon.

In The Last Decade

Rajesh Singh

45 papers receiving 319 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rajesh Singh India 10 327 47 30 16 4 46 342
Tanveer A. Tarray India 10 246 0.8× 54 1.1× 13 0.4× 7 0.4× 8 2.0× 47 282
Rajesh Tailor India 11 372 1.1× 31 0.7× 11 0.4× 21 1.3× 1 0.3× 52 381
Shashi Bahl India 5 378 1.2× 17 0.4× 24 0.8× 15 0.9× 1 0.3× 8 381
Stephen A. Sedory United States 10 269 0.8× 64 1.4× 16 0.5× 11 0.7× 46 289
Raghunath Arnab Botswana 10 224 0.7× 39 0.8× 21 0.7× 20 1.3× 3 0.8× 57 252
Ramkrishna S. Solanki India 12 378 1.2× 38 0.8× 28 0.9× 29 1.8× 28 388
Guillaume Basse United States 6 114 0.3× 9 0.2× 21 0.7× 8 0.5× 12 150
Surendra K. Srivastava India 8 377 1.2× 52 1.1× 22 0.7× 10 0.6× 11 387
John C. Wakefield Hong Kong 5 51 0.2× 13 0.3× 7 0.2× 13 0.8× 3 0.8× 14 114
Nirmala Sawan India 3 94 0.3× 6 0.1× 3 0.1× 2 0.1× 1 0.3× 4 95

Countries citing papers authored by Rajesh Singh

Since Specialization
Citations

This map shows the geographic impact of Rajesh Singh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rajesh Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rajesh Singh more than expected).

Fields of papers citing papers by Rajesh Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rajesh Singh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rajesh Singh. The network helps show where Rajesh Singh may publish in the future.

Co-authorship network of co-authors of Rajesh Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Rajesh Singh. A scholar is included among the top collaborators of Rajesh Singh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rajesh Singh. Rajesh Singh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Singh, Rajesh, et al.. (2025). A novel ratio cum product type exponential class of estimators of finite population mean in Adaptive cluster Sampling. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 43(1). e43745–e43745.
3.
Singh, Rajesh, et al.. (2024). Preliminary estimators of population mean using ranked set sampling in the presence of measurement error and non-response error with applications and simulation study. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 42(3). 272–288. 1 indexed citations
4.
Singh, Rajesh, et al.. (2024). On Combining Ratio and Product Type Estimators For Estimation of Finite Population Mean In Adaptive Cluster Sampling Design. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 42(4). 412–420. 3 indexed citations
5.
Singh, Rajesh, et al.. (2022). Almost Unbiased Variance Estimators Under the Simultaneous Influence of Non-response and Measurement Errors. Journal of Statistical Theory and Practice. 16(2). 3 indexed citations
6.
Singh, Ran Vijay, et al.. (2022). On the estimation of finite population variance for a mail survey design in the presence of non-response using new conventional and calibrated estimators. Communication in Statistics- Theory and Methods. 53(3). 848–864. 6 indexed citations
7.
Sharma, Prayas, Hemant Verma, Rajesh Singh, & Carlos N. Bouza. (2019). ESTIMATORS FOR POPULATION VARIANCE USING AUXILIARY INFORMATION ON QUARTILE. 39(4). 528–535. 3 indexed citations
8.
Singh, Rajesh, et al.. (2019). A note on the estimators for coefficient of dispersion using auxiliary information. Communications in Statistics - Simulation and Computation. 49(9). 2347–2356. 3 indexed citations
9.
Singh, Rajesh, et al.. (2018). EFFECT OF MEASUREMENT ERROR AND NON-RESPONSE ON ESTIMATION OF POPULATION MEAN. 39(1). 108–120. 7 indexed citations
10.
Bouza, Carlos N., et al.. (2018). RANKED SET SAMPLING AND OPTIONAL SCRAMBLING RANDOMIZED RESPONSE MODELING. 39(1). 100–107. 2 indexed citations
11.
Singh, Rajesh, et al.. (2016). Estimation of Finite Population Mean Using Auxiliary Attribute in Sample Surveys. 1(2). 39–44. 1 indexed citations
12.
Singh, Rajesh, Hemant Verma, & Prayas Sharma. (2016). Estimation of Population Mean Using Exponential Type Imputation Technique for Missing Observations. Journal of Modern Applied Statistical Methods. 15(1). 358–372. 5 indexed citations
13.
Singh, Rajesh, et al.. (2015). Improved Class of Estimators for Variance under Single And Two Phase Sampling. Journal of Statistics Applications & Probability. 4(1). 161–171. 1 indexed citations
14.
Sharma, Prayas, et al.. (2015). Generalized Class of Estimators for Population Variance Using Auxiliary Attribute. International Journal of Applied and Computational Mathematics. 2(4). 499–508. 9 indexed citations
15.
Sharma, Prayas, et al.. (2015). Generalized Class of Estimators for Population Variance Using Information on Two Auxiliary Variables. International Journal of Applied and Computational Mathematics. 3(2). 651–661. 21 indexed citations
16.
Verma, Hemant, Prayas Sharma, & Rajesh Singh. (2014). Some Ratio-cum-product Type Estimators for Population Mean Under Double Sampling in the Presence of Non-response. Journal of Statistics Applications & Probability. 3(3). 395–401. 2 indexed citations
17.
Sharma, Prayas & Rajesh Singh. (2013). A Generalized Class of Estimators for Finite Population Variance in Presence of Measurement Errors. Journal of Modern Applied Statistical Methods. 12(2). 231–241. 16 indexed citations
18.
19.
Singh, Rajesh, Pankaj Chauhan, Nirmala Sawan, & Florentín Smarandache. (2009). Improvement in Estimating the Population Mean Using Exponential Estimator in Simple Random Sampling. viXra. 3. 13–18. 45 indexed citations
20.
Singh, Rajesh & Housila P. Singh. (1998). Almost unbiased ratio and product-type estimators in systematic sampling. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 22(3). 403–416. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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