Sri Lankan journal of applied statistics最新文献

{"title":"Effectiveness of Using Candlestick Charts to Forecast Ethereum Price Direction: A Machine Learning Approach","authors":"N. I. M. B. Senanayaka, H. A. Pathberiya","doi":"10.4038/sljas.v25i1.8131","DOIUrl":"https://doi.org/10.4038/sljas.v25i1.8131","url":null,"abstract":"Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick charts within a short time horizon. The proposed forecasting algorithm incorporates clustering methods such as fuzzy K-means, K-means and partition around medoids clustering to cluster candlestick chart properties namely upper shadow length, body length and lower shadow length. Classification methods such as random forest, support vector machine and K-nearest neighbour were used to forecast closing price direction using 16 different predictor variable sets including open, high, low and close prices, candlestick chart price direction, USL, BL and LSL. The accuracy for all considered cases was around 50%. Clustering improved the accuracy slightly and including the CPD with the predictor variable sets under consideration can increase the accuracy slightly. However, this approach is performing better in predicting the Down cases to the total number of actual Down cases because there is a higher sensitivity of 81.20% based on the SVM with Open, High, Low and Close at t in the clustering ignored method.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"26 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Level Analysis of Help-Seeking Behaviour of Male Victims of Intimate Partner Violence","authors":"D. A. Candia, P. G. O. Weke, G. O. Muhua, M. M. Manene","doi":"10.4038/sljas.v25i1.8121","DOIUrl":"https://doi.org/10.4038/sljas.v25i1.8121","url":null,"abstract":"Globally, many men are victims of intimate partner violence but not many seek help to stop the violence. This study sought to identify the factors associated with the help-seeking behavior of male victims of intimate partner violence (IPV) in Uganda. This was done using ordinary and mixed-effects regression models with logit, probit, and complementary log-log link functions and secondary data from the 2016 Uganda Demographic and Health Survey. Most males (70.6 percent) never sought help after experiencing intimate partner violence. Marital status, listening to the radio, physically hurt partner, experienced physical injury, number of control issues, father ever beat mother, and severity of violence were significantly associated with the help-seeking behavior of male victims of IPV. There is a need to come up with interventions that encourage males to report IPV, especially among the married, and also sensitize males not to wait until they have experienced physical injuries or any other severe violence before they seek help. Given that the sampling methodology used in demographic and health surveys introduces nesting in the data, researchers should consider using multilevel models.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"23 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mixed Model Approach for Identifying the Impact of Soft Productivity Factors on Employee Turnover among Information Technology Employees in Sri Lanka","authors":"D. Seneviratna, L. Chathuranga, W. M. C. J. T. Kithulwatta, R. K. T. Rathnayaka","doi":"10.4038/sljas.v25i1.8113","DOIUrl":"https://doi.org/10.4038/sljas.v25i1.8113","url":null,"abstract":"Employee turnover has become one of the major issues in the IT industry today; especially the productivity of an organization is deeply related to the employee turnover rate. The objectives of the current study are to investigate the basic factors that affect employee turnover, examine the relationships between selected soft productivity factors with employee turnover, and suggest ways of minimizing employee turnover rates in IT companies. Initially, eleven soft productivity factors are considered. The selected factors are categorized into two different categories workplace environment and employee capability and experience. In the first phase of analysis, the descriptive research approach was followed and data was collected from 150 IT professionals currently employed in IT companies in Sri Lanka, using a structured questionnaire. According to the results, a strong positive relationship can be seen between (0.676) working environment, (0.789) employees’ capabilities and experience, (0.881) collaboration among employees, (0.891) career development opportunities, and (0.774) learning opportunities concerning employee turnover. Further, lack of sufficient telecommunication facilities, tools, and development resources, the friendly working environment also effects on retaining of employees.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"13 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian Network Analysis of Calendar Effects in the Colombo Stock Exchange","authors":"H. K. R. Rathnaweera, Rajitha M. Silva","doi":"10.4038/sljastats.v24i3.8095","DOIUrl":"https://doi.org/10.4038/sljastats.v24i3.8095","url":null,"abstract":"This study applies Bayesian Network analysis to examine the probabilistic causal relationship between calendar effects and stock market anomalies in the Colombo Stock Exchange. While prior research has explored the existence of Calendar Anomalies in the Colombo Stock Exchange, few studies have examined the underlying cause-and-effect relationship between these anomalies and their associated probabilities. This study employs a Bayesian Network model using market data from 2007 to 2020 to investigate this relationship. The results indicate that calendar effects are prevalent in the market, and the analysis identifies a probabilistic causal relationship between abnormal market returns and Day-of-the-Week and Turn-of-the-Month calendar anomalies. The findings of this study enable investors to time their trades by assigning probabilities to positive or negative market returns on specific trading days, maximizing their returns and improving the efficiency of their trades in the Colombo Stock Exchange.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139140909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Analysis of Threshold Autoregressive Model with First Order Autoregressive Innovations","authors":"O. O. Ojo","doi":"10.4038/sljastats.v24i3.8079","DOIUrl":"https://doi.org/10.4038/sljastats.v24i3.8079","url":null,"abstract":"Financial assets exhibit dramatic changes in behaviour. This work examined a two-regime Threshold autoregressive (TAR) models when the innovations follow a first-autoregressive order process. The Bayesian method is proposed to build in the linear first-order autoregressive process with identical distributed innovations. The practical usefulness of this method is demonstrated with simulated and real-life data using U.S.A quarterly real GDP as an example. In simulation experiments and real life example, an increase in first order process parameter, ρ value leads to better estimates in the proposed model. Also, the proposed model was compared with TAR model where the disturbance term does not exhibit regime switching. The proposed model performed well than the traditional TAR model using the simulated and real life data. An increase in first order process parameter, ρ will lead to better estimates and forecast. Hence, the proposed model performed well.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139141991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating COVID-19 Prevalence in Sri Lanka: A Dynamic Sampling Model Approach","authors":"J. D. T. Erandi, U. P. Liyanage, A. Gunawardana","doi":"10.4038/sljastats.v24i3.8114","DOIUrl":"https://doi.org/10.4038/sljastats.v24i3.8114","url":null,"abstract":"Over history, human has had to face various crises and diseases, and among them, the COVID 19 virus stands out as one of the most deathful diseases ever. It has brought numerous challenges to all the fields worldwide. Despite efforts to control its spread, the virus persists globally with varying intensity. Addressing this challenge requires an effective and precise control measure. The progression of the virus in different sub-regions is influenced by factors such as population density, public mobility, and healthcare infrastructure. Consequently, the prevalence of the virus varies across sub-regions. This study proposes an adaptive sampling design that modifies the stratified sampling technique to capture the changing prevalence of COVID-19, considering the dynamic nature of infected populations. This adaptation is essential as the increase of infected cases boosts the virus spread, and the standard sampling techniques do not address such dynamic population conditions in determining the sample size. The study aims to narrow the gap between reported and actual daily infections, providing more accurate estimates of virus distribution. The weighted allocation method incorporates the skewed pattern of coronavirus progression, with weights determined based on the first derivative of reported infected cases. This derivative information is based on the recent dynamics of the infected cases. Thereby larger weights were assigned when the virus progression increased, and smaller weights were assigned when the virus progression decreased. The resulting sample sizes for each sub-region are calculated using the modified stratified sampling method. Further, to illustrate the accuracy of the sampling design, simulated data from different epidemic scenarios, such as community spread, cluster spread, and border spread was used. This simulation allowed us to test the robustness of the techniques for the different states of the virus progression based on the infected cases. The sample size obtained through this dynamic sampling technique exhibits a direct correlation with the fluctuations in the number of infected cases, increasing as the infection cases rise and decreasing as they decline. In conclusion, the study introduces a novel sampling technique that accommodates the dynamic nature of population sizes, and it can be straightforwardly applied for the real-world data as well. Thus, this modified stratified sampling method emerges as a precise approach for capturing the actual prevalence of COVID-19.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" 72","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139139499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporation of Covariates Through Principal Components in Analysis of Covariance: A Simulation Study","authors":"A. S. G. Jayasinghe, S. Samita","doi":"10.4038/sljastats.v24i2.8094","DOIUrl":"https://doi.org/10.4038/sljastats.v24i2.8094","url":null,"abstract":"This study examined the use of the Principal Component Analysis (PCA) approach in incorporating covariates in the Analysis of Covariance (ANCOVA) under different experimental setups. Simulated data were used for the study, and the statistical programs were developed in R statistical software to generate the datasets for different experimental setups by varying (i) number of covariates, (ii) degree of correlation among covariates, (iii) number of treatments, (iv) difference between treatment means, and (v) number of replicates. Thousand simulations were performed for each experimental setup, and the impact of the PCA approach was assessed by means of power of the test through the proportion of rejections of H0: no difference between adjusted treatment means, in 1000 simulations. The use of PCs led to a significant gain in the power of the test in ANCOVA when there is a higher number of interrelated covariates with a limited number of observations. The impact was higher with the increase of number of covariates as well as the correlation between covariates. It can be concluded that by accommodating covariates by means of PCs, the efficiency in ANCOVA can be increased, especially if there are many covariates to be included in the analysis with a limited number of observations.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136343962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Economic Disparity across Sri Lanka’s Districts","authors":"N. R. Abeynayake, A. M. Shafna, Senal A. Weerasooriya","doi":"10.4038/sljastats.v24i2.8086","DOIUrl":"https://doi.org/10.4038/sljastats.v24i2.8086","url":null,"abstract":"Globally, there is an increasing regional economic development disparity. The region’s growth depends on strong economic development. The sustainable development of Sri Lanka could be harmed by the regional economic disparity. An essential consideration in the establishment of regional development policies is the identification and magnitude of regional economic disparities. This study looks at the current state of Sri Lanka’s regional economic development disparity. This study’s methodology in this regard involved using several economic development metrics. Data for the year 2019 was acquired through authoritative sources. The multivariate analysis technique using the principal component analysis (PCA) approach has been adopted, which assigns a weight to each dimension and indicator to create composite indexes. The economic development of the western province was higher than that of the other provinces, but there was also a notable disparity between the districts of the western province, with the Colombo district having the highest economic development index. The resulting indices enable policymakers to prioritize regions for additional efforts while also assessing the state of regional disparities. The new index also makes it feasible to classify local government entities logically to support the government’s numerous policy-making and development initiatives. To calculate an index of economic disparity similar to that, the approach would apply to any nation.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Tidal Elevations at Eastern and Western Coastal Areas of Sri Lanka with Short-term Data","authors":"J. A. R. M. Perera, P. A. D. A. N. Appuhamy, E. M. P. Ekanayake","doi":"10.4038/sljastats.v24i2.8098","DOIUrl":"https://doi.org/10.4038/sljastats.v24i2.8098","url":null,"abstract":"Prediction of tidal heights are increasingly beneficial for multitude of ocean functions such as port development, fishing industry, and safe movement of ships. As the general harmonic technique always needed great volumes of data for predicting tidal heights, Artificial Neural Networks (ANNs) emerged as a viable alternative for addressing diverse problems in the coastal engineering sector in recent decades. However, there has been no previous research to distinguish Harmonic Analysis from ANN models for predicting tidal heights around Sri Lanka by overcoming the rampant issue of data scarcity, which is the focus of the present study. Hourly tidal heights recorded in the Western (Colombo) and Eastern (Trincomalee) coastal areas of Sri Lanka were used in modelling. As tidal elevation is periodic in nature, it was expressed as Fourier Series with its coefficients (constituents) being determined by Harmonic Analysis, while the ANN technique employed the back-propagation procedure to forecast tidal heights. Harmonic Analysis displayed lesser prediction performance even with five months of data at Colombo (MSE=0.030 and MAPE=1.875) and Trincomalee (MSE=0.019 and MAPE=1.052), in contrast to the ANN models with only 7 days of data, which has much lower MSE and MAPE at Colombo (0.006 and 0.096) and (0.003 and 0.052) at Trincomalee respectively. Thus, the ANN model outperformed the Harmonic Analysis in terms of both accuracy and flexibility. Overall, this study demonstrated the potential of ANN modeling as a reliable, economical, and efficient alternative for predicting tidal heights to circumvent the dearth of tidal data on the coastal Sri Lanka.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Goodness of Fit Test for a Survival and Count Bayesian Joint Model: In the Presence of Clusters","authors":"K. U. S. Kumaranathunga, M. Sooriyarachchi","doi":"10.4038/sljastats.v24i1.8090","DOIUrl":"https://doi.org/10.4038/sljastats.v24i1.8090","url":null,"abstract":"Bayesian statistical model fitting was an uncommon approach until recently, causing a lack of assessment techniques for these models. However, with the enhancement of computational facilities and advanced estimation techniques, Bayesian models have become popular. Though there are developed goodness of fit (GOF) tests available for classical multilevel models including joint modelling of mixed responses, there is no suitable model based GOF test to be applied on such a model which is fitted under a Bayesian framework. Therefore, this study focused on developing a suitable GOF test for multilevel Bayesian joint models having survival and count responses which are two frequently occurring data types in many fields. The novel test is developed mainly based on four classical GOF tests, including the well-known Hosmer-Lemeshow test and, the Bayesian concepts such as Bayesian credible intervals and regions. In addition, a simulation study has been used to examine the properties of the GOF test together with an application to a real-life example. The novel test performed well in terms of power and acceptable in terms of Type I error rates. Overall, the test performed well with small sample sizes.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47214647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}