Mahmod Othman

Seasonal rainfall forecasting is crucial for agricultural planning and water resource management in Delta State, Nigeria, as the region's economy is highly dependent on climate. This study investigates the trend and appropriate models for forecasting seasonal rainfall patterns in the region. We employed a range of methods, including traditional time series techniques like Holt's Winter exponential smoothing and the Seasonal Autoregressive Integrated Moving Average (SARIMA), alongside more advanced machine learning and deep learning models. Critical data properties such as stationarity and normality of error terms were first assessed. Model performance was then evaluated using standard metrics, including root mean square error, mean absolute error, mean absolute percentage error, and mean square error. The data was found to have a stationary pattern, and among the models explored, the Holt's Winter exponential smoothing model was identified as the best performing.

The Half-Logistic (HL) distribution is a renowned lifetime distribution widely used in various domains, including reliability engineering, biological sciences, and actuarial research. Despite its fundamental simplicity and easy interpretation, the HL distribution has limitations in terms of flexibility for modeling datasets with heavy tails or varying skewness. To address these constraints, this paper introduces a new extension of the HL distribution based on the Odd Beta Prime (OBP) family. The newly developed distribution has two shape parameters and is named the Odd Beta Prime Half-Logistic (OBPHL) distribution. Its several distributional properties, including the probability density function, cumulative distribution function, survival function, hazard function, and quantile function, alongside other various distributional characteristics, such as moments, measures of skewness and kurtosis, entropy measure ( Rényi and Tsallis), and order statistics, are explored. The maximum likelihood estimation technique is used to estimate the parameters of the new model. The performance of the estimators under different sample sizes has been evaluated using a Monte Carlo simulation study. In addition, the proposed OBPHL distribution is applied to two real-world datasets. The results are compared against several classical distributions using standard model selection criteria. The OBPHL distribution consistently outperforms the competing models based on evaluation metrics. This research contributes a new flexible model to the field of statistical distribution theory and demonstrates its efficacy in practical applications.

Inverted distributions, also known as inverse distributions, are essential statistical models for analyzing real-life data in biomedical sciences, engineering, and other fields. In this paper, we use the odd beta prime-G family and the inverted Kumaraswamy distribution to introduce a new inverted distribution called the odd beta prime inverted Kumaraswamy. The new distribution exhibits right-skewed, J-shaped densities and features increasing-constant, concave-convex, and bathtub hazard functions. Some of its statistical properties are explored. The parameters are estimated via the maximum likelihood method. The empirical importance of the new model is proved through its application to COVID-19 mortality data from Italy. Numerical results demonstrate that the proposed model outperforms its competitors. We hope that this proposed distribution can be considered as a viable alternative to some well-established distributions for modeling real-life data across various application areas.

In this article, we pioneer a new Burr X distribution using the odd beta prime generalized (OBP-G) family of distributions called the OBP-Burr X (OBPBX) distribution. The density function of this model is symmetric, left-skewed, right-skewed, and reversed-J, while the hazard function is monotonically increasing, decreasing, bathtub, and N-shaped, making it suitable for modeling skewed data and failure rates. Various statistical properties of the new model are obtained, such as moments, moment-generating function, entropies, quantile function, and limit behavior. The maximum-likelihood-estimation procedure is utilized to determine the parameters of the model. A Monte Carlo simulation study is implemented to ascertain the efficiency of maximum-likelihood estimators. The findings demonstrate the empirical application and flexibility of the OBPBX distribution, as showcased through its analysis of petroleum rock samples and COVID-19 mortality data, along with its superior performance compared to well-known extended versions of the Burr X distribution. We anticipate that the new distribution will attract a wider readership and provide a vital tool for modeling various phenomena in different domains.

The quest for energy efficiency in buildings has placed a demand for designing and modeling energy-efficient buildings. In this study, the thermal energy performance of a tropically adapted passive building was investigated in the warm tropical climate of Malaysia. Two mock-up buildings were built to represent a “green”, made of clay brick double-glazed passive building and a conventional, made of concrete “red” building. The mean indoor temperature of the passive building was found to be always lower than that of the red building throughout the experiment during different weather constellations. Our research builds upon existing work in the field by combining multiple linear regression models and distribution models to provide a comprehensive analysis of the factors affecting the indoor temperature of a building. The results from the fitted multiple linear regression models indicate that walls and windows are critical components that considerably influence the indoor temperature of both passive buildings and red buildings, with the exception of passive buildings during the hot season, where the roof has a greater influence than the window. Furthermore, the goodness-of-fit test results of the mean indoor temperature revealed that the Fréchet and Logistic probability models fitted the experimental data in both cold and hot seasons. It is intended that the findings of this study would help tropical countries to devise comfortable, cost-effective passive buildings that are green and energy efficient to mitigate global warming.

In this work, we propose and study a novel generalization of the Fréchet distribution called the odd beta prime Fréchet (OBPF) distribution. This distribution was an extension of the Fréchet distribution by applying the odd beta prime generalized family of distributions. The proposed model can be expressed as a linear mixture of Fréchet densities. The shapes of the density function possess great flexibility. It can accommodate various hazard shapes, such as increasing, decreasing, and reversed J. Some important statistical properties of the OBPF are derived, including the ordinary and incomplete moments, order statistics, and quantile function. We have used the maximum likelihood estimation method to estimate the model parameters. The application and flexibility of the new distribution are empirically proven using groundwater pollution data sets compared to other competing distributions. The new model can be used instead of existing lifetime distributions and is suitable to fit data with right-skewed and left-skewed behaviors

The current study aimed to identify the most suitable probability distribution function (pdf) for modeling the exchange rates of three countries. Financial data is essential to many people and to the management of a country. Volatility in financial data influences individual and the country's economic growth. This volatility in the exchange rates between the Malaysian Ringgit (MYR), Singapore Dollar (SGD), and Thailand Thai Baht (THB) against British Pound Sterling (GBP) is found to be very high which make it difficult to model and forecast. This is what has necessitated the development of an accurate and reliable approach for assessing and reducing the risks of trading in any of these currencies.

In several applications, fuzzy time series forecasting was utilized to generate predictions about the future value of variables that were of interest. This study focuses on predicting how many tourists will visit Langkawi since a precise estimate of tourism demand would enable the government to decide whether to raise or lower the money allocated to the sector in the future. To be more precise, this study attempts to choose the best model that may be applied to forecast visitors to Langkawi and assist the public and private sectors in managing tourism-related preparations. The data collection contains monthly data from January 2009 to December 2010 and was directly extracted from the Langkawi Development Authority (LADA) website. When estimating visitor arrivals to Langkawi, the suggested fuzzy time series' accuracy was compared to that of the earlier technique. The experimental findings in this study demonstrated that the Fuzzy Time Series approach can anticipate more accurately. The results of this study could serve as inspiration for the public and private sectors to take action to bring more tourists to Langkawi, make their stay pleasant and pleasurable, and improve the possibility that they would visit again and again in the future.

In parametric statistical modeling, it is important to construct new extensions of existing probability distributions (PDs) that can make modeling data more flexible and help stakeholders make better decisions. In the present study, a new family of probability distributions (FPDs) called the odd beta prime generalized (OBP-G) FPDs is proposed to improve the traditional PDs. A new PD called the odd beta prime-logistic (OBP-logistic) distribution has been developed based on the developed OBP-G FPDs. Some desirable mathematical properties of the proposed OBP-logistic distribution, including the moments, moment-generating function, information-generating function, quantile function, stress–strength, order statistics, and entropies, are studied and derived. The proposed OBP-logistic distribution’s parameters are determined by adopting the maximum likelihood estimation (MLE) method. The applicability of the new PD was demonstrated by employing three data sets and these were compared by the known extended logistic distributions, such as the gamma generalized logistic distribution, new modified exponential logistic distribution, gamma-logistic distribution, exponential modified Weibull logistic distribution, exponentiated Weibull logistic distribution, and transmuted Weibull logistic distribution. The findings reveal that the studied distribution provides better results than the competing PDs. The empirical results showed that the new OBP-logistic distribution performs better than the other PDs based on several statistical metrics. We hoped that the newly constructed OBP-logistic distribution would be an alternative to other well-known extended logistic distributions for the statistical modeling of symmetric and skewed data sets.

In this work, a new bivariate inverted Nakagami distribution that can be used to model real-world datasets has been investigated. The newly developed bivariate distribution's cumulative distribution function and probability density function are defined. The bivariate distribution derives from the Farlie Gumbel Morgenstern, and the marginal density functions are also determined. Some fundamental estimation techniques, such as maximum-likelihood estimation and inference functions for margins, are used to derive the parameters of its estimates. Applications to real-world datasets pertaining to kidney infection diseases and the UEFA Champions' League group stage for the seasons 20042005 and 20052006 help to assess the efficacy of the proposed distribution.