Open Access Original Research Article

Modeling the Differences between the Genders in Olympic Weightlifting Performance

Jebessa B. Mijena, Elyse Renshaw

Asian Journal of Probability and Statistics, Page 1-11
DOI: 10.9734/ajpas/2022/v17i430427

This article studies Weightlifting results from the 2000-2016 Olympic Games for both males and females to determine the differences between gender performance. The data showed that there are considerable differences in the competitive level between male and female lifters. This article compares the results of winners in a weight class with a given lifter in the weight class from year to year. This article shows that males in every competition studied had a lower mean percent difference from first than the same comparison for women. This article also compares the winner from each weight class to the weight class above them and observes the place that they would have received. Overall we see that for males, a winner in a weight class would be around 6th place in the weight class above them, while for females, a winner in a weight class would be around 4th in a weight class above them. We observe that as place increases, the difference between male and female results as well as the standard deviation between the results increases. We show that females are less competitive than males as the place that the lifter attained increases. There appears to be no association between weight class and mean percentage difference of the fifth place from first place for females. For males, there appears to be a decrease in the average percentage difference of the fifth place from first place as the weight class increases, meaning that the higher weight classes for males are more competitive than the lower weight classes.

Open Access Original Research Article

Rail Transit Stations Classification Based on Spectral Clustering

Qi Shi, Shaowei Sun, Jingjing Jie

Asian Journal of Probability and Statistics, Page 12-21
DOI: 10.9734/ajpas/2022/v17i430428

To identify the function and positioning of urban rail stations, and provide further guidance for design and construction, a classification method based on spectral clustering algorithm is established. Firstly, based on the principles of comprehensiveness and robustness, 5 initial indicators were selected, including total entry count, total exit count, entrances count, bus connecting lines count, and metro connecting lines count. Secondly, we normalize the original data by Z-score method and extract two main clustering factors through principal component analysis. Finally, we propose a station classification model based on spectral clustering algorithm. The effectiveness of the proposed method is verified in Hangzhou Metro System. The K-means cluster algorithm and spectral cluster methods are employed. The results show that the proposed model can successfully identify the types of urban rail transit stations, clarify the function and orientation of each station.

Open Access Original Research Article

Volatility of Exchange Rate in Nigeria: An Investigation of Risk on Investment

Udokang Anietie Edem, Sanusi Olatoye Akeem, M. Okeniyi Okeyemi, Ojo O. David, Ajiboye Ifeoluwa Mayowa

Asian Journal of Probability and Statistics, Page 22-29
DOI: 10.9734/ajpas/2022/v17i430429

The contribution of investment in currency trading in economic growth of a nation cannot be over emphasized. Hence, the examination of the risk involved in such trading because of volatility in foreign exchange rate. Time series data and model were used in this study. The monthly exchange rate of four major foreign currencies in Nigeria namely the US Dollars (USD), the Great Britain Pounds (GBP), the EURO and CFA Francs against Nigerian Naira (NGN) from January, 2004 to December, 2019 were extracted from website of Central Bank of Nigeria (CBN) with an open access to the public. Due to the volatile nature of the exchange rate, the Generalized Autoregressive Conditional Hetroscedastic (GARCH) model was a suitable model used at order 1 for parsimony to determine volatility used in computing Value at Risk (VaR). It was discovered that the maximum loss (risk), measured by VaR, that can occur at 95% confidence interval for twelve months forecast of  trading with GBP was the highest, among the four currencies, with percentage loss of between 15.5% to 16.2%. While the CFA has the lowest risk with VaR between 0.02% to 0.03%. Based on the findings, the risk of investing in foreign exchange is the highest when trading in GBP with attendant high returns due to large fluctuations up and down of the exchange rate. This study will help investors to study the pattern of risk and returns and decide on the foreign currency to trade on. The government should put policies in place to encourage existing investors and new investors that want to invest on foreign exchange to create employment, since the risk involved can be determined as it is done in this study.

Open Access Original Research Article

Effect of Inspection Error on CUSUM Chart for Truncated Negative Binomial Distribution

U. Mishra, J. R. Singh

Asian Journal of Probability and Statistics, Page 30-37
DOI: 10.9734/ajpas/2022/v17i430430

  In this paper, a mathematical investigation has been done for Cumulative Sum control Chart (CSCC) of Truncated Negative Binomial Distribution (TNBD) under inspection error using the sequential probability ratio test (SPRT). Average Run Length (ARL), d (lead distance) and \(\phi\) (angle of the mask) is calculated for different values of error rates and different values of parameter of the distribution.

Open Access Original Research Article

Modeling and Forecasting of Rainfall Trends based on Historical Data in Bungoma County, Western Kenya using Holt Winters Method

Vanessa A. Otieno, Samson W. Wanyonyi

Asian Journal of Probability and Statistics, Page 38-44
DOI: 10.9734/ajpas/2022/v17i430431

Rainfall is termed as a meteorological phenomenon which is very useful for daily human activities. Most of the population depend on it for domestic purpose and agriculture, hence it is vital for farmers to know rainfall trends and patterns prevailing in their locality. In Kenya, rainfall variability has caused hunger to many people. In agriculture section, water problem is the most critical constraint in food productions. Extreme weather conditions which is occasioned by climatic change and weather variability makes a small scale farmer in Bungoma to face very high risks of reduced productivity. Scarcity of water is a severe environmental constraint to plant productivity hence drought causes loss in crop yield. Majority of the population in Bungoma do depend on agriculture either directly or indirectly therefore analysis of rainfall data for long periods will provide a lot of information about rainfall variability and this will help to better the agricultural activities of Bungoma farmers. The main aim of the study was to model rainfall patterns and hence predict the future rainfall trends in Bungoma region using Holt winters method in the context of the time series. This is because time series analysis plays an important role in modelling, predicting and forecasting meteorological data such as humidity, temperature, rainfall and other environmental variable. Therefore, data for Bungoma monthly and yearly rainfall patterns for the period 1988-2021 was obtained from the Kenya meteorological department. Collected data was analyzed using Holt winters method by R software. Rainfall data was found to be seasonal implying that most of the rainfall occurred in a specific period each year. Forecasted rainfall had increasing and decreasing prediction intervals and this implied that rainfall could either start decreasing or increasing. The data was found to be non-stationary due to presence of seasonality and rainfall trends. Findings of the study will make it possible to facilitate planning and management of water for both domestic and agricultural use in Bungoma region.