Javid Shabbir

4.7k total citations · 1 hit paper
240 papers, 3.0k citations indexed

About

Javid Shabbir is a scholar working on Statistics and Probability, Artificial Intelligence and Epidemiology. According to data from OpenAlex, Javid Shabbir has authored 240 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 224 papers in Statistics and Probability, 35 papers in Artificial Intelligence and 16 papers in Epidemiology. Recurrent topics in Javid Shabbir's work include Survey Sampling and Estimation Techniques (207 papers), Statistical Methods and Bayesian Inference (56 papers) and Statistical Distribution Estimation and Applications (46 papers). Javid Shabbir is often cited by papers focused on Survey Sampling and Estimation Techniques (207 papers), Statistical Methods and Bayesian Inference (56 papers) and Statistical Distribution Estimation and Applications (46 papers). Javid Shabbir collaborates with scholars based in Pakistan, United States and Saudi Arabia. Javid Shabbir's co-authors include Sat Gupta, Fariha Sohil, Abdul Haq, Sohaib Ahmad, Zawar Hussain, Sushil K. Gupta, R Dawson, Alamgir Khalil, Muhammad Aamir and Bal Kishan Dass and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Javid Shabbir

216 papers receiving 2.9k citations

Hit Papers

An introduction to statistical learning with applications... 2021 2026 2022 2024 2021 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Javid Shabbir Pakistan 23 2.0k 340 164 162 108 240 3.0k
Xavier Bry France 10 1.4k 0.7× 273 0.8× 106 0.6× 119 0.7× 138 1.3× 37 2.4k
Howard D. Bondell United States 23 894 0.5× 492 1.4× 368 2.2× 214 1.3× 131 1.2× 80 3.2k
Paul Gustafson Canada 31 1.5k 0.8× 370 1.1× 136 0.8× 429 2.6× 278 2.6× 194 3.6k
Gerhard Tutz Germany 9 417 0.2× 372 1.1× 91 0.6× 108 0.7× 53 0.5× 16 2.6k
C. B. Dean Canada 25 774 0.4× 281 0.8× 78 0.5× 209 1.3× 43 0.4× 78 2.5k
Daniel B. Hall United States 27 679 0.3× 269 0.8× 68 0.4× 100 0.6× 64 0.6× 79 2.8k
Christian Heumann Germany 23 315 0.2× 127 0.4× 88 0.5× 129 0.8× 51 0.5× 126 2.1k
M. J. Crowder United Kingdom 18 1.1k 0.6× 215 0.6× 60 0.4× 93 0.6× 452 4.2× 42 2.9k
Farid Kianifard United States 28 273 0.1× 158 0.5× 221 1.3× 253 1.6× 111 1.0× 91 3.8k
Ioannis Ntzoufras Greece 20 894 0.5× 530 1.6× 179 1.1× 70 0.4× 217 2.0× 72 2.7k

Countries citing papers authored by Javid Shabbir

Since Specialization
Citations

This map shows the geographic impact of Javid Shabbir'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 Javid Shabbir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javid Shabbir more than expected).

Fields of papers citing papers by Javid Shabbir

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Javid Shabbir. 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 Javid Shabbir. The network helps show where Javid Shabbir may publish in the future.

Co-authorship network of co-authors of Javid Shabbir

This figure shows the co-authorship network connecting the top 25 collaborators of Javid Shabbir. A scholar is included among the top collaborators of Javid Shabbir 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 Javid Shabbir. Javid Shabbir 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.
Al‐Essa, Laila A., et al.. (2025). An application of modified systematic sampling in auto-correlated populations. Kuwait Journal of Science. 52(3). 100404–100404. 1 indexed citations
2.
Khalil, Alamgir, et al.. (2022). Multivariate ratio exponential estimators of the population mean under stratified double sampling. Mathematical Population Studies. 30(2). 122–141.
3.
Shabbir, Javid, et al.. (2021). On the improvement of paired ranked set sampling to estimate population mean. SHILAP Revista de lepidopterología. 22(3). 193–205. 2 indexed citations
4.
Shabbir, Javid, et al.. (2021). Use of Intuitionistic Fuzzy Numbers in Survey Sampling Analysis with Application in Electronic Data Interchange. Complexity. 2021(1). 4 indexed citations
5.
Khalil, Alamgir, et al.. (2020). A parent-generalized family of chain ratio exponential estimators in stratified random sampling using supplementary variables. Communications in Statistics - Simulation and Computation. 51(8). 4727–4748. 9 indexed citations
6.
Shabbir, Javid, et al.. (2020). An optimal systematic sampling scheme. Journal of Statistical Computation and Simulation. 90(11). 2023–2036. 1 indexed citations
7.
Shabbir, Javid, et al.. (2018). Estimation of Finite Population Mean by Using Minimum and Maximum Values in Stratified Random Sampling. Journal of Modern Applied Statistical Methods. 17(1). 22 indexed citations
8.
Khalil, Alamgir, et al.. (2018). A new improved ratio-product type exponential estimator of finite population variance using auxiliary information. Journal of Statistical Computation and Simulation. 88(16). 3179–3192. 16 indexed citations
9.
Shabbir, Javid, et al.. (2017). An alternative item sum technique for improved estimators of population mean in sensitive surveys. DergiPark (Istanbul University).
10.
Shabbir, Javid, et al.. (2017). Estimators for Population Mean in Adaptive Cluster Sampling. 15(2). 105–110. 2 indexed citations
11.
Shabbir, Javid, et al.. (2015). Estimation of the Finite Population Mean, using Median based Estimators in Stratified Random Sampling. Journal of Statistics Applications & Probability. 4(3). 367–374. 1 indexed citations
12.
Shabbir, Javid, et al.. (2014). Ratio Type Exponential Estimator for the Estimation of Finite Population Variance under Two-stage Sampling. Research Journal of Applied Sciences Engineering and Technology. 7(19). 4095–4099. 3 indexed citations
14.
Hussain, Zawar, et al.. (2012). An Alternative Item Count Technique in Sensitive Surveys. Revista Colombiana de Estadística. 35(1). 39–54. 6 indexed citations
15.
Hussain, Zawar & Javid Shabbir. (2010). On Item Count Technique in Survey Sampling. 2. 161–169. 2 indexed citations
16.
Haq, Abdul & Javid Shabbir. (2010). A family of ratio estimators for population mean in extreme ranked set sampling using two auxiliary variables. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 34(1). 45–64. 7 indexed citations
17.
Hussain, Zawar, Javid Shabbir, & Muhammad Riaz. (2010). Bayesian Estimation Using Warner's Randomized Response Model through Simple and Mixture Prior Distributions. Communications in Statistics - Simulation and Computation. 40(1). 147–164. 15 indexed citations
18.
Hussain, Zawar & Javid Shabbir. (2008). Logit Estimation Using Warner’s Randomized Response Model. Journal of Modern Applied Statistical Methods. 7(1). 140–151. 5 indexed citations
19.
Shabbir, Javid & Sat Gupta. (2007). On Improvement in Variance Estimation Using Auxiliary Information. Communication in Statistics- Theory and Methods. 36(12). 2177–2185. 63 indexed citations
20.
Gupta, Sat & Javid Shabbir. (2005). An Alternative to Warner’s Randomized Response Model. Journal of Modern Applied Statistical Methods. 5(2). 328–331. 5 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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