A penalized likelihood approach to model the annual maximum flow with small sample sizes

Authors

  • Nur Farhanah Kahal Musakkal Universiti Malaysia Sabah
  • Su Na Chin Universiti Malaysia Sabah
  • Khadizah Ghazali Universiti Malaysia Sabah
  • Darmesah Gabda Universiti Malaysia Sabah

DOI:

https://doi.org/10.11113/mjfas.v0n0.620

Keywords:

Generalized extreme value, Penalized likelihood, Extreme value theory, Small sample size

Abstract

The aim of this study is to model the annual maximum flow of several sites in Sabah with small sample sizes using the generalized extreme value (GEV) distribution. Previous studies have shown that the standard method of maximum likelihood estimates would give a poor estimation of the GEV parameters and quantiles for small data set. This study will consider the penalized likelihood estimates as an alternative method to improve the inference over the standard method and retains the modeling flexibility. As comparisons, we will illustrate the results of both methods to model the annual maximum flow in Sabah. The results show the implementation of the penalty function had the same effect to the GEV parameter estimates as suggested by previous studies.

Author Biographies

Nur Farhanah Kahal Musakkal, Universiti Malaysia Sabah

Faculty of Science and Natural Resources (Mathematics Economics)

Su Na Chin, Universiti Malaysia Sabah

Faculty of Science and Natural Resources (Mathematics Economics)

Khadizah Ghazali, Universiti Malaysia Sabah

Faculty of Science and Natural Resources (Mathematics Economics)

Darmesah Gabda, Universiti Malaysia Sabah

Faculty of Science and Natural Resources (Mathematics Economics)

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Published

26-12-2017