Browsing by Author "Nadeekantha, H. A. D. D."

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A comparison of distance-based and model-based clustering methods
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Nadeekantha, H. A. D. D.; Kavinga, H. W. B.; Gunawardana, A.; Dissanayaka, D. M. P. V.
Most of the statistical techniques assume the homogeneity of the sample data. However, not all the time, real-world samples are homogeneous. The existence of subgroups within a population leads to the non-homogeneity of the sample. In this case, it is not accurate to model the population using a single probability distribution. Hence it is essential to check the homogeneity of the sample. Clustering, an unsupervised learning technique, is being used to discover a population's subgroups and group each observation into a specific cluster. Mainly, clustering algorithms can be divided into two groups, namely model-based and distance-based algorithms. Model-based algorithms assume a probability distribution for clustering, while distance-based algorithms use a distance metric to classify observations into clusters. In the literature, it was suggested that the model-based clustering methods perform better than the distance-based methods using summary statistics and visualizations. In this study, an inference-based procedure has been used to assess the above claim. To compare the performances of model-based and distance-based algorithms, an extensive simulation study was conducted. In the simulation study, two univariate Gaussian mixtures with different parameter settings (mean, standard deviation, and sample size) were combined to generate a non-homogeneous sample. Then, model-based and distance-based algorithms were applied to the same simulated datasets with different cluster structures, knowing the actual cluster memberships. Further, the effect of bimodality conditions of Gaussian mixtures on both clustering methods was checked. To assess the performance of the two methods, identifying the correct number of clusters, Cluster Identification Ability (CIA), and categorizing the observations into the correct cluster memberships (clustering accuracy) were computed. CIA was computed using the percentage of iterations that identified the correct number of clusters, and clustering accuracy was measured using the Adjusted Rand Index (ARI). For most of the simulation settings, both methods required a sample size of less than 200 to achieve high clustering accuracy (approximately mean ARI value of 0.8). For example, a simulation setting with a mean difference of 3.1 and a standard deviation of 0.5 required sample sizes 20 and 10 for the model-based and distance-based methods, respectively. These minimum sample sizes vary depending on the method's high clustering accuracy, and in some cases, those are approximately the same. The inference-based study which is performed using the paired Wilcoxon signed-rank test indicated that the claim “model-based method outperforms distance-based method, or both performs similarly” is valid 82.7% of the time at a 5% level of significance. In conclusion, the CIA and clustering ability of the model-based method increased with the increment of sample size when the bimodality conditions were satisfied by the mixture. For the distance-based method, both abilities decreased as the sample size increased when the bimodality conditions were not satisfied by the sample.
The effect of food commodity price fluctuation on inflation in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Nadeekantha, H. A. D. D.; Lakshitha, W. A. D. M.; Lakshitha, W. A. D. M.; Chandrasekara, N. V.
In Sri Lanka, the intersection of inflation and food price fluctuations holds profound significance, affecting not only the nation's economic stability but also the daily lives of its citizens. While existing research has extensively focused on the impact of rice prices on inflation, no published studies have been found that specifically investigate the influence of fluctuations in vegetable and fish commodity prices on inflation. Hence, there is a research gap to have a comprehensive understanding about price fluctuation on inflation. Thus, the objectives of this research are to primarily consider the effect of price fluctuations in mostly consuming vegetable and fish commodities on inflation using suitable techniques. The study focuses on key commodities, including beetroot, cabbage, potato, and various fish types (Seer, Mullet, Kelawalla, and Hurulla). Monthly data from January 2014 to June 2022, sourced from the Central Bank of Sri Lanka and the Department of Census and Statistics, were utilized for the analysis, with no missing values. To measure inflation, the National Consumer Price Index (NCPI) was used. Since all the time series of monthly observations of fish and vegetable prices and NCPI were non-stationary, the first differencing of logarithm for all the series was used where it proved the stationary by both graphical and theoretical techniques. After investigating the lag structures for fish and vegetable models, the optimum and the better lags were found. The cointegration test for both models proved that there were correlations between several time series in the long run based on the optimal lag length. Hence, two Vector Error Correction (VEC) models were fitted for two groups of food commodity prices namely, Fish and Vegetables where VEC models are well-suited for examining the relationships between food commodity prices and inflation over time. Strong cointegration relationships were identified inside these two groups. According to the VEC Granger causality test, it was found that beetroot, cabbages and potatoes do Granger-cause in NCPI but cabbages and other selected fishes do not Granger-cause in NCPI. To study the impact on inflation, the impulse response function was used. It was found that price shocks of the Hurulla fish type have a significant positive impact on inflation than other fish types of Seer, Mullet, and Kelawalla. Beetroot price shocks have a significantly more positive impact on inflation than other vegetable types of potatoes, tomatoes, and cabbage. The model, which was fitted for fish prices, the percentage of forecasting errors for NCPI increases over time for each type of fish, according to the forecast error variance decompositions. In the model, which was fitted for vegetable prices, the percentage also increases with time, but it remains smaller compared to the fish. Sri Lanka needs effective strategies and policies to mitigate the challenges of unstable inflation, hence the understanding of price fluctuation on inflation empowers policymakers to craft targeted strategies to mitigate the impact of inflation on daily life.