• 543 Citations
  • 13 h-Index
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Fingerprint Dive into the research topics where Kamarulzaman Ibrahim is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 2 Similar Profiles
Ranked Set Sampling Mathematics
Malaysia Mathematics
Simple Random Sampling Mathematics
Pareto Distribution Mathematics
Rainfall Mathematics
Logistics/distribution Mathematics
Extremes Mathematics
rainfall Earth & Environmental Sciences

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Research Output 2003 2019

  • 543 Citations
  • 13 h-Index
  • 64 Article
  • 12 Conference contribution
  • 3 Editorial
  • 1 Chapter
1 Citation (Scopus)
Tail Index
Pareto Distribution
Efficient Estimator
Robust Estimators

Markov chain modeling for air pollution index based on maximum a posteriori method

Alyousifi, Y., Ibrahim, K., Kang, W. & Zin, W. Z. W., 1 Dec 2019, In : Air Quality, Atmosphere and Health. 12, 12, p. 1521-1531 11 p.

Research output: Contribution to journalArticle

Markov Chains
Air Pollution
Markov chain
Air pollution
Markov processes

On the identification of extreme outliers and dragon-kings mechanisms in the upper tail of income distribution

Safari, M. A. M., Masseran, N. & Ibrahim, K., 1 Jan 2019, (Accepted/In press) In : Journal of Applied Statistics.

Research output: Contribution to journalArticle

Box plot
Income Distribution

Pentaabiran Bayesian untuk Model Regresi Linear Prior Normal-Pencong-Alfa

Translated title of the contribution: Bayesian inference for linear regression under alpha-skew-normal priorAlhamide, A. A., Ibrahim, K., Alodat, M. T. & Wan Zin @ Wan Ibrahim, W. Z., 1 Jan 2019, In : Sains Malaysiana. 48, 1, p. 227-235 9 p.

Research output: Contribution to journalArticle

Open Access
Bayesian inference
Linear regression
Prior distribution

Power law behavior and tail modeling on low income distribution

Masseran, N., Yee, L. H., Safari, M. A. M. & Ibrahim, K., 1 Jul 2019, In : Mathematics and Statistics. 7, 3, p. 70-77 8 p.

Research output: Contribution to journalArticle

Open Access
Income Distribution
Power Law