ABSTRACT PM10 has been identified as being a common problem in Malaysia and many
other countries all over the world. A Markov chain probability model is found to fit the …

Missing values have always been a problem in analysis. Most exclude the missing values
from the analyses which may lead to biased parameter estimates. Some imputations …

The aim of this study is to determine the behavior of extreme PM 10 levels monitored at three
air monitoring stations in Johor using frequentist and Bayesian technique. Bayesian allows …

Extreme value theory is a very well-known statistical analysis for modeling extreme data in
environmental management. The main focus is to compare the generalized extreme value …

Particulate matter (PM) comprises of a complex mixture of small solid or liquid particles of
organic and inorganic elements that floats freely in air. PM10 is defined as a particulate …

The Multiple-try Metropolis (MTM) algorithm is the new alternatives in the field of Bayesian
extremes for summarizing the posterior distribution. MTM produce efficient estimation …

The main concern in environmental issue is on extreme phenomena (catastrophic) instead
of common events. However, most statistical approaches are concerned primarily with the …

We concentrate on characteristics of minima XN from samples of iid lognormals X1i∼
2pLND. We demonstrate that the distribution of XN for 2≤ N≤ 1,000 may be fitted more …

Dealing with extreme observation events is critical in order to decide the appropriate actions
for future extreme circumstances. Extreme studies on environmental events are among the …

Extreme PM10 concentration from the Air Pollutant Index (API) at thirteen monitoring stations
in Malaysia is modeled using the Generalized Extreme Value (GEV) distribution. The data is …