Phylogenetic inference refers to the reconstruction of evolutionary relationships among various species that is usually
presented in the form of a tree. This study constructs the phylogenetic tree by using a novel distance-based method known
as Modified one step M-estimator (MOM) method. The branches of the phylogenetic tree constructed were then evaluated
to see their reliability. The performance of the reliability was then compared between the p-value of multiscale bootstrap
(AU value) and bootstrap p-value (BP value). The aim of this study was to compare the performance between the AU value
and BP value for assessing phylogenetic tree of RNA polymerase. The results have shown that multiscale bootstrap analysis
can detect high sampling errors but not in bootstrap analysis. To overcome this problem, the multiscale bootstrap analysis
has reduced the sampling error by increasing the number of replications. The clusters were indicated as significant if AU
values or BP values were 95% or higher. From the analysis, the results showed that the BP and AU values differ at 11th
and 15th branch of the phylogenetic tree. The BP values at both branches were 72 and 85%, respectively, thereby making
the cluster not significant but by looking at the AU values, the two branches were more than 95% and the clusters were
significant. This was due to the biasness in calculation of the probability of bootstrap analysis, therefore, the multiscale
bootstrap analysis has improved the calculation of the probability value compared to the bootstrap analysis.
This paper presents various imputation methods for air quality data specifically in Malaysia. The main objective was to
select the best method of imputation and to compare whether there was any difference in the methods used between stations
in Peninsular Malaysia. Missing data for various cases are randomly simulated with 5, 10, 15, 20, 25 and 30% missing.
Six methods used in this paper were mean and median substitution, expectation-maximization (EM) method, singular
value decomposition (SVD), K-nearest neighbour (KNN) method and sequential K-nearest neighbour (SKNN) method. The
performance of the imputations is compared using the performance indicator: The correlation coefficient (R), the index
of agreement (d) and the mean absolute error (MAE). Based on the result obtained, it can be concluded that EM, KNN
and SKNN are the three best methods. The same result are obtained for all the eight monitoring station used in this study.