The grassroots of the presence of missing precipitation data are due to the malfunction of instruments, error of recording and meteorological extremes. Consequently, an effective imputation algorithm is indeed much needed to provide a high quality complete time series in assessing the risk of occurrence of extreme precipitation tragedy. In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The effectiveness of BPCAQ-VB algorithm in missing daily precipitation data treatment is evaluated by using four distinct precipitation time series, including two monitoring stations located in inland and coastal regions of Kuantan district, respectively. The analysis results rendered the BPCA5-VB is superior in missing daily precipitation data treatment for the coastal region time series compared to the single imputation algorithms proposed in previous studies. Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.

One of the most important sides of life is wildlife. There is growing research interest in monitoring wildlife. Line transect sampling is one of the techniques widely used for estimating the density of objects especially for animals and plants. In this research, a parametric estimator for estimation of the population abundance is developed. A new parametric model for perpendicular distances for detection function is utilised to develop the estimator. In this paper, the performance of the parametric model which was developed using a simulation study is presented. The detection function has non-increasing curve and a perfect probability at zero. Theoretically, the parametric model which has been developed is guar-anteed to satisfy the shoulder condition assumption. A simulation study is presented to validate the present model. Relative mean error (RME) and Relative Bias (RB) are used to compare the estimator with well-known existing estimators. The results of the simulation study are discussed, and the performance of the proposed model shows promising statistical properties which outperformed the existing models. Keywords: detection function, line transect data, parametric model