bluuebunny/crossref_metadata_2025 huggingface dataset:
Refinements of Gaussian Tail Inequality
Authors: A. Rather N. , A. Rather T.
Published: July 2019

In this paper, we first prove a theorem which gives considerably better bound for 0 ≤ t ≤ 1/2 than Gaussian tail inequality (or tail bound for normal density) and thus is a refinement of Gaussian tail inequality in this case. Next we present an interesting result which provides a refinement of Gaussian tail inequality for t > √ 3. Besides, we also prove an improvement of Gaussian tail inequality for 0 < t ≤ 1/2. Finally, we present a more general result which includes a variety of interesting results as special cases.
The Transmuted Odd Lindley-G Family of Distributions
Authors: Reyad Hesham , Jamal Farrukh , Othman Soha , G. Hamedani G.
Published: July 2019

We propose a new generator of univariate continuous distributions with two extra parameters called the transmuted odd-Lindley generator which extends the odd Lindely-G family introduced by Gomes-Silva et al. [1]. Some mathematical properties of the new generator such as, the ordinary and incomplete moments, generating function, stress strength model, Rényi entropy, probability weighted moments and order statistics are investigated. Certain characterisations of the proposed family are estimated. We discuss the maximum likelihood estimates and the observed information matrix for the model parameters. The potentiality of the new family is illustrated by means of five applications to real data sets.
Comparison of the Bootstrap and Delta Method Variances of the Variance Estimator of the Bernoulli Distribution
Authors: Ying Zhang Ying- , Zhong Rong Teng- , Man Li Man-
Published: July 2019

It is interesting to calculate the variance of the variance estimator of the Bernoulli distribution. Therefore, we compare the Bootstrap and Delta Method variances of the variance estimator of the Bernoulli distribution in this paper. Firstly, we provide the correct Bootstrap, Delta Method, and true variances of the variance estimator of the Bernoulli distribution for three parameter values in Table 2.1. Secondly, we obtain the estimates of the variance of the variance estimator of the Bernoulli distribution by the Delta Method (analytically), the true method (analytically), and the Bootstrap Method (algorithmically). Thirdly, we compare the Bootstrap and Delta Methodsin terms of the variance estimates, the errors, and the absolute errors in three gures for 101 parameter values in [0, 1], with the purpose to explain the di erences between the Bootstrap and Delta Methods. Finally, we give three examples of the Bernoulli trials to illustrate the three methods.
On the Properties and Applications of Transmuted Odd Generalized Exponential-Exponential Distribution
Authors: Abdullahi Jamila , Kabir Abdullahi Umar , Godfrey Ieren Terna , Adugh Kuhe David , Abubakar Umar Adamu
Published: July 2019

This article proposed a new distribution referred to as the transmuted odd generalized exponential-exponential distribution (TOGEED) as an extension of the popular odd generalized exponential- exponential distribution by using the Quadratic rank transmutation map (QRTM) proposed and studied by [1]. Using the transmutation map, we defined the probability density function (pdf) and cumulative distribution function (cdf) of the transmuted odd generalized Exponential- Exponential distribution. Some properties of the new distribution were extensively studied after derivation. The estimation of the distribution’s parameters was also done using the method of maximum likelihood estimation. The performance of the proposed probability distribution was checked in comparison with some other generalizations of Exponential distribution using a real life dataset.
On the Properties and Applications of Lomax-Exponential Distribution
Authors: Godfrey Ieren Terna , Adugh Kuhe David
Published: July 2019

The Exponential distribution is memoryless and has a constant failure rate which makes it unsuitable for real life problems. This paper introduces a new distribution powered by an exponential random variable which gives a more flexible model for modelling real-life data. This new extension of the Exponential Distribution is called “Lomax-Exponential distribution (LED)”. The extension of the new distribution became possible with the help of a Lomax generator proposed by [1]. This paper derives and studies some expressions for various statistical properties of the new distribution including distribution function, moments, quantile function, survival function and hazard function known as reliability functions. The inference for the Lomax-Exponentially distributed random variable were investigated based on some plots of the distribution and others revealed its behaviour and usefulness in real life situations. The parameters of the distribution are estimated using the method of maximum likelihood estimation. The performance of the new Lomax-Exponential distribution has been tested and compared to the Weibull-Exponential, Transmuted Exponential and the conventional Exponential distribution using three real life data sets.
Modeling of Nigeria’s Economic Growth Rate: A Probability Distribution Fitting Approach
Authors: Charles N Eke, , George Amaeze Osuji, , Dozie Felix Nwosu,
Published: July 2019

This study examined the probability distribution that best described the quarterly economic growth rate of Nigeria between 1960- 2015. The study collected secondary data from Central Bank of Nigeria (CBN) Statistical Bulletin 2015 on Gross Domestic Product to compute the economic growth rate of Nigeria. Six theoretical statistical distributions were fitted via Normal Distribution, Logistic Distribution, Laplace Distribution, Cauchy Distribution, Gumbel (Largest Extreme Value) Distribution and Generalized Logistic Distribution. The Laplace Distribution fitted the data as confirmed by Kolmogorov Simonov goodness of fit test, Akaike Information Criteria and Bayes Information Criteria. The probabilities of economic growth rate behaviours were obtained from the best fit distribution. The analysis showed that the chance of obtaining a negative quarterly economic growth rate is 28%. The chance of an economic recession is 8%. Also, the probability of having a positive single digit quarterly economic growth rate is 46%. In addition, having a double digit positive quarterly economic growth rate is 26%.
Modeling Agricultural Gross Domestic Product of Kenyan Economy Using Time Series
Authors: M. Ngungu Musyoki , Jacob Ong'ala , Noah Wawire
Published: July 2019

The agriculture sector is the mainstay of the Kenyan economy. Thus, the sector has a significant role and contribution to GDP. In this study, Box-Jenkins seasonal ARIMA time series modeling approach is used to develop a model that best describes the quarterly agricultural gross domestic product of Kenyan economy. Agricultural gross domestic product data collected quarterly from 2000-2014 at constant 2001 prices is used for modeling. From the analysis, SARIMA(1, 0, 0)(1, 1, 0)4 was found to be the best model describing the quarterly agricultural gross domestic product of Kenyan economy.
Application of Average fs-Aggregate Algorithm for Multi-criteria Decision Making in Real Life Problem
Authors: Pal Rajesh Kumar
Published: July 2019

Generally in our day to day life we come across the real life situations when right decision making becomes difficult. In different fields like economics, social science, medical science, environment, where such complicacy arises and involves uncertainties. The conventional mathematical methods are unsuitable to solve such problems. To solve this problem, we are to find the various parameters related to the problem to use them to get the accurate result. So fuzzy soft set theory is the best mathematical tool for solving such problems using different techniques. In this research paper, we have taken a multi-criteria real life problem to select a suitable city which suits the best conditions as per the given parameters. For getting the solution, we used average fs-aggregation algorithm for using multi-criteria parameters.
A Comparative Analysis on Some Estimators of Parameters of Linear Regression Models in Presence of Multicollinearity
Authors: Audu Warha, Abdulhamid , Muhammad Yusuf Abbakar , Imam Akeyede,
Published: July 2019

Linear regression is the measure of relationship between two or more variables known as dependent and independent variables. Classical least squares method for estimating regression models consist of minimising the sum of the squared residuals. Among the assumptions of Ordinary least squares method (OLS) is that there is no correlations (multicollinearity) between the independent variables. Violation of this assumptions arises most often in regression analysis and can lead to inefficiency of the least square method. This study, therefore, determined the efficient estimator between Least Absolute Deviation (LAD) and Weighted Least Square (WLS) in multiple linear regression models at different levels of multicollinearity in the explanatory variables. Simulation techniques were conducted using R Statistical software, to investigate the performance of the two estimators under violation of assumptions of lack of multicollinearity. Their performances were compared at different sample sizes. Finite properties of estimators’ criteria namely, mean absolute error, absolute bias and mean squared error were used for comparing the methods. The best estimator was selected based on minimum value of these criteria at a specified level of multicollinearity and sample size. The results showed that, LAD was the best at different levels of multicollinearity and was recommended as alternative to OLS under this condition. The performances of the two estimators decreased when the levels of multicollinearity was increased.
On Estimating Variance Components of Two-Way Nested Random Model with Missing Information
Authors: Mohammed Amany Mousa , El-Sheikh Ahmed Amin , Shehata Alaa Sayed
Published: July 2019

In this paper, the estimators of variance components are derived of two-way nested random model when the problem of missing information exists using combination between Modified Minimum Variance Quadratic Unbiased Estimation (MMIVQUE) and Modified Minimum Variance Quadratic Unbiased Estimation (MMIVQUE (0)) methods that is called MMIV(MIV(0)) method.
On Making an Informed Choice between Two Lomax-based Continuous Probability Distributions Using Lifetime Data
Authors: Ieren Terna Godfrey , Oyamakin Samuel Oluwafemi , Yahaya Abubakar , Chukwu Angela Unna , Umar Adamu Abubakar , Kuje Samson
Published: July 2019

Probability distributions and their generalisations have contributed greatly in the modeling and analysis of random variables. However, due to the increased introduction of new distributions there has been a major problem with choosing and applying the right distribution for a given set of data. In most cases, it is discovered that the data set in question fits two or more probability distributions and hence one must be chosen among others. The Lomax-Weibull and Lomax-Log-Logistic distributions introduced in an earlier study using a Lomax-based generator were found to be positively skewed and may be victims of this situation especially when modelling positively skewed datasets. In this article, we apply the two distributions to some selected datasets to compare their performance and provide useful insight on how to select the most fit among them when dealing with a real-life situation. We used the log-likelihood value, AIC, CAIC, BIC, HQIC, Cramér-Von Mises (W*) and Anderson Darling (A*) statistics as performance evaluation tools for selecting between the two distributions.
Identifying and Predicting Major Factors Affecting the Suicides in Sri Lanka
Authors: Lakmali S. M. M. , Nawarathna L. S.
Published: July 2019

Aims: Identifying factors related to suicide and the prediction of future suicides are very important because suicide becomes a significant factor that engaged with education, social status, age, gender and many other factors. Therefore, the main objective of this study is to find the civil and education factors effecting on suicidal attempts in Sri Lanka and propose a model to predict the future suicides.

Study Design: Statistical analysis with descriptive analysis and proposing models for predicting future suicides.

Place and Duration of Study: Data collected from the Department of Police, Sri Lanka, between January 2006 and December 2016.

Methodology: Data set has separated into two categories namely ‘civil data’ and ‘educational data’. We modeled the data from 2006 to 2011 and the data from 2014 to 2016 were used for model validation purposes. Quasi Poisson and negative binomial regression models were fitted to identify the major factors affecting suicide in both categories. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values were used to select the best model. Further, the Mean Absolute Percentage Deviation (MAPD) and Symmetric Mean Absolute Percent Error (SMAPE) were calculated to find the prediction accuracy of the proposed models.

Results: For both regression models, the variables age, gender and level of education are significant for the models fitted for educational data, and civil status and gender are significant in the civil status dataset. According to the analysis, highest suicides were recorded for the age groups 21-30 and over 61 males, minimally-educated and married people. By considering the MAPD values, the prediction accuracy of both Quasi Poisson models and Negative binomial models were above 99%. But the negative binomial model is the best model because of the comparable high accuracy than the other model. A considerable reduction in suicides was obtained in 2010, due to the peaceful situation in Sri Lanka after the civil war. It is observed that by paying special attention to teenagers, old-aged and married people can reduce the number of suicides.
Assessment of Under Five Child Mortality in Tanzania Mainland Based on Principal Component Analysis
Authors: Christopher Privatus
Published: July 2019

Deaths of children younger than 5 years has been a global problem for long time. This study is focused on evaluating diseases that caused under five child mortality in Tanzania in 2013. Diseases that causes child mortality were collected from 25 regions and analysed for 42 disease variables. The data obtained were standardized and subjected to principal component analysis (PCA) to define the diseases responsible for the variability in child mortality. PCA produced seven significant main components that explain 73:40% of total variance of the original data set. The results reveal that Thyroid Diseases, Snake and Insect Bites, Vitamin A Deficiency /Xerophthalmia, Eye Infections, Schistosomiasis (SS), Intestinal Worms, Ear Infections, Haematological Diseases, Diabetes Mellitus, Ill Defined Symptoms no Diagnosis, Poisoning, Anaemia, HIV/AIDS, Burns, Rheumatic Fever, Bronchial Asthma, Peri-natal conditions and Urinary tract infection are most significant diseases in assessing under five child mortality in Tanzania mainland. This study suggest that PCA technique is useful tool for identification of important diseases that causes death of children less than five years.
Analyzing and Forecasting HIV Data Using Hybrid Time Series Models
Authors: Xie Liming
Published: July 2019

In real work, we often confront complete linear and nonlinear time series data. But some time series are not pure linear and nonlinear, or complicated one, we need apply two or more models to analyze and predict them. It is necessary to explore and find some novel time series hybrid methods to solve it. Human Immunodeficiency and Virus (HIV) is one of intractable and trouble diseases in the world. Thus, the author of this article wants to analyze and probe into some novel time series methods to get breaking breach in the epidemiology that find some rules in the incidence, distribution, pathogen, and control of HIV in a population.  In this article, to find the best model, auto.arima function is applied to the original time series data to determine autoregressive integrated moving average, ARIMA(0,0,0); ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH), that is, ARIMA-GARCH (1,1) model is used to analyze numbers of people living with HIV for the data of HIV in the world such some important parameters as mu, ar1, ar2, omega, alpha 1, or beta 1 and some specific tests, for example, Jarque-Bera Test, Shapiro-Wilk Test, Ljung-Box Test, etc. Using ARIMA (0, 0, 0) and SARIMA (0,2), seasonal ARIMA, to predict the future values and trends after 2015. Both suggest identical results.
Determinants and Management of Patient Waiting Time in the General Outpatient Department in Kibabii University Health Clinic, Kenya
Authors: Muen Sevu , Wanyonyi Samson W. , Marangu Davis Mwenda , Mong’are Obare D.
Published: July 2019

The purpose of this paper is to determine the time that a patient can spend waiting for service in Kibabii University Healthcare Clinic in Bungoma County (Kenya). The main objective was to provide necessary information to service facility managers, stakeholders, hospital staffs and other related institutions with the knowledge to improve the queuing system or to curb long waiting time of patients seeking services which can cause deterioration of the disease and sudden demise. This project also aims at providing suggestions to various factors identified to be the causes of long waiting time in the outpatient department at Kibabii University healthcare to help the smooth running of the clinic. The ODK tool was used in data collection procedure to capture the opinions of the respondents. The results from the ODK Tool was exported to XLS which is an export feature of data to excel, later data was exported to SPSS for analysis from excel.
Statistical Analysis Based on Lake Michigan Fish Acoustic Data Using LASSO Method
Authors: Xie Liming
Published: July 2019

LASSO method is one of the most popular and more extensive regressions. It has been applied to many fields. However, it is rare seen to research with complicated big data in biology. This paper is to apply LASSO method to Lake Michigan Fish acoustic data. The main techniques include: Elastic Net selection, which tests validation from the average square error (ASE) to predict the error for the model by computing separately for each of these subsets; defaulting group LASSO to test multiple parameters by splitting a couple constituent parameters, such as successive intervals, multiple continuous depth layers, to estimate the Schwarz Bayesian information criterion (SBC) to find the lowest value for the model; The adaptive LASSO selection, which is applied to each of the parameters in constructing the LASSO constraint for weights, that is, the response y has mean zero and the regressor x are scaled to have mean zero and common standard deviation. The empirical results show that the fish density (Y) has strong relationships with area backscattering coefficient (PRC_ABC), secondly, significant interactions with PRC_ABC and Exclude below line depth mean), among PRC_ABC, fish density in the intervals and layers of acoustic survey transect of Lake Michigan.
Bayesian Estimation of the Scale Parameter of the Weimal Distribution
Authors: Mabur Tajan Mashingil , Omale Aisha , Lawal Ahmed , Dewu Mustapha Mohammed , Mohammed Sa’ad
Published: July 2019

This article aims at estimating the scale parameter of the Weimal distribution using Bayesian method and comparing the estimators obtained to the estimator of the scale parameter obtained from the method of maximum likelihood. Under Bayesian approach, the estimators are obtained by using uniform prior and Jeffrey’s prior with the adoption of the precautionary, quadratic and square error loss functions. A derivation and discussion2ws under maximum likelihood estimation is also done. The above methods of estimation employed in this paper are compared based on their mean square errors (MSEs) through a simulation study carried out in R statistical software with different sample sizes. The results indicate that the most appropriate method for the scale parameter is precautionary loss function under either uniform or Jeffrey’s prior irrespective of the sample sizes allocated and the values taken by the other parameters.
Unrestricted Vector Autoregressive Modelling of the Interaction among Oil Price, Exchange Rate and Inflation in Nigeria (1981–2017)
Authors: Tuaneh G. L. , Wiri L.
Published: July 2019

The interdependence among oil prices, exchange rates and inflation rates, and their response to shocks, was a cause of concern. Unrestricted Vector Autoregression (UVAR) was employed to analyse this interactions as well as to investigate the pattern of causality among the study variable. Annual data spanning from 1981 to 2017 was sourced from the Statistical Bulletin of the Central Bank of Nigeria. Pre-estimation analysis showed that all variables were integrated of order one 1(1), and there no cointegrating relationship. The inverse root of AR characteristic polynomial showed a stable VAR model. All lag length selection criteria chose a lag length of 1. The UVAR estimates and the test of significance particularly the granger causality test indicated significant influence and uni-directional effect from oil price to exchange rates. The Wald statistics, showed significant own shocks, and the impulse response showed that all variables were instantaneously affected by own shocks. Exchange rate was instantaneously affected by oil price; however, it ruled out the response in inflation rate to contemporaneous shocks in oil price. The variance decomposition further showed that at least 93.1%, 97.1% and 92.4% of the impulse response in oil price, exchange rate, and inflation rate respectively were from own shocks in the long run. The post estimation analysis showed that the VAR model was multivariate normal, the residual was homoscedastic, and there was no serial autocorrelation. It was recommended that the government should diversify the national income stream and consider policies that will control inflation.
An Extended Pranav Distribution
Authors: Uwaeme O. R. , Akpan N. P. , Orumie U. C.
Published: July 2019

In this study, we proposed a generalization of the Pranav distribution by Shukla (2018). This new distribution called an extended Pranav distribution is obtained using the exponentiation method. The statistical characteristics of this new distribution such as the moments, moment generating function, reliability function, hazard function, Rényi entropy and order statistics are derived. The graphical illustrations of the shapes of the probability density function, the cumulative distribution function, and hazard rate functions are provided. The maximum likelihood estimates of the parameters were obtained and finally, we examine the performance of this new distribution using some real-life data sets to show its flexibility and better goodness of fit as compared with other distributions.
Modelling the Nigeria Crude Oil Prices Using ARIMA, Pre-intervention and Post-intervention Model
Authors: Leneenadogo Wiri, , Lebari Tuaneh, Godwin
Published: July 2019

The study applied Autoregressive Integrated Moving Average Intervention in modelling crude oil prices in Nigeria spanning the period from January 1986 to June 2017. The time plot of the series showed an abrupt increase in the series and this called for intervention modelling. The data was divided into three set (actual series, pre-intervention and post-intervention series). The Augmented Dickey Fuller (ADF) was used to test for unit root on each of the series and were all found to be non-stationary at levels, they (actual, pre and post- intervention series) were however non stationary at first difference. Eighteen models were estimated and the best model was the pre-intervention model that minimise the Akaike information criterion (AIC) (ARIMA (111)) with AIC of (4.4.578). The plot of the residual correlogram showed adequacy of the model. The model was adequate since there was no spike that cut the level of the correlogram and the histogram of the residual was normally distributed with probability values (0.0000).
Comparison of Different Parametric Methods in Handling Critical Multicollinearity: Monte Carlo Simulation Study
Authors: Maxwell Obubu , Chukwudike C. Nwokike , Chinedu O. Virtus , Valentine C. Okoye , Paul Obite Chukwudi
Published: July 2019

In regression analysis, it is relatively necessary to have a correlation between the response and explanatory variables, but having correlations amongst explanatory variables is something undesired. This paper focuses on five methodologies for handling critical multicollinearity, they include: Partial Least Square Regression (PLSR), Ridge Regression (RR), Ordinary Least Square Regression (OLS), Least Absolute Shrinkage and Selector Operator (LASSO) Regression, and the Principal Component Analysis (PCA). Monte Carlo Simulations comparing the methods was carried out with the sample size greater than or equal to the levels  considered in most cases, the Average Mean Square Error (AMSE) and Akaike Information Criterion (AIC) values were computed. The result shows that PCR is the most superior and more efficient in handling critical multicollinearity problems, having the lowest AMSE and AIC values for all the sample sizes and different levels considered.
Analysis and Modelling of Extreme Rainfall: A Case Study for Dodoma, Tanzania
Authors: Iyamuremye Emmanuel , Wanyonyi Samson W. , Mbete Drinold A.
Published: July 2019

The analysis of climate change, climate variability and their extremes has become more important as they clearly affect the human society and ecology. The impact of climate change is reflected by the change of frequency, duration and intensity of climate extreme events in the environment and on the economic activities. Climate extreme events, such as extreme rainfall threaten to environment, agricultural production and loss of people’s lives. Dodoma daily rainfall data exported from R-Instat software were used after being provided by Tanzania Meteorological Agency. The data were recorded from 1935 to 2011. In this essay, we used climate indices of rainfall to analyse changes in extreme rainfall. We only used 6 rainfall indices related to extremes to describe the change in rainfall extremes. Extreme rainfall indices did not show statistical evidence of a linear trend in Dodoma rainfall extremes for 77 years. Apart from the extreme rainfall indices, this essay utilized two techniques in extreme value theory namely the block maxima approach and peak over threshold approach. The two extreme value approaches were used for univariate sequences of independent identically distributed (iid) random variables. Using Dodomadaily rainfall data, this essay illustrated the power of the extreme value distributions in modelling of extreme rainfall. Annual maxima of Dodoma daily rainfall from 1935 to 2011 were fitted to the Generalized Extreme Value (GEV) model. Gumbel was found to be the best fit of the data after likelihood ratio test of GEV and Gumbel models. The Gumbel model parameters were considered to be stationary and non-stationary in two different models. The stationary Gumbel model was found to be good fit of Dodoma maximum rainfall. Later, the levels at which maximum Dodoma rainfall is expected to exceed once, on average, in a given period of time T = 2, 5, 10, 20, 30, 50 and 100 years, were obtained using stationary Gumbel model. Lastly, the data of exceedances were fitted to     the Generalized Pareto (GP) model under stationary climate assumption.
Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts
Authors: Kuhe David Adugh , Chiawa Moses Abanyam , Nwaosu Sylvester Chigozie , Ikughur Jonathan Atsua
Published: July 2019

This study investigated the impact of volatility shock persistence on the conditional variance in the Nigerian stock returns using symmetric and asymmetric higher order GARCH family models in the presence of random level shifts and non-Gaussian errors. The study utilised Bai and Perron methodology to detect structural breakpoints in the conditional variance of daily stock and volume of trade returns in the Nigerian stock market from 2nd January, 1998 to 22nd March, 2017. The study employed symmetric GARCH (3,2) and GARCH (2,1)-M models to estimate volatility of asset returns, symmetric GARCH (2,2) and GARCH (2,1)-M to model volatility of volume of trade returns and asymmetric EGARCH (2,2), TGARCH (3,2) and PGARCH (2,3) models to measure the volatility of asset returns as well as asymmetric EGARCH (2,1), TGARCH (1,3) and PGARCH (3,2) models to estimate volatility of volume of trade returns. These models were optimally selected using information criteria and log likelihood as the best fitting symmetric and asymmetric GARCH models to estimate the conditional volatility of asset and volume of trade returns in the Nigerian stock market with and without structural breaks. Results revealed that when random level shifts were ignored in volatility models, the shocks persistence were very high with long memory and variance explosion. But when the random level shifts were incorporated into the GARCH models, there was a significant reduction in the volatility shocks persistence and long memory. Moreover, volatility half-lives also declined drastically while accounting for these sudden level shifts in variance. The study found asymmetry without leverage effects as well as a positive risk-return tradeoff for both asset and volume of trade returns in the Nigerian stock market. The Nigeria banking reform of 2004, the Global Financial and Economic Crises, as well as other local events in Nigeria, were found to have negative and significant impacts on the Nigerian stock market. The study provided some policy recommendations.
Comparative Study on the Degree of Randomness of Few Popular Random Number Tables
Authors: Pandit Pramit , Bakshi Bishvajit
Published: July 2019

In the field of statistics as well as in the different branches of experimental sciences, random number tables have been playing a vital role for the purpose of selecting random samples. Among the existing different random number tables, four tables namely, Tippet’s random number table, Fisher and Yates random number table, Kendall and Smith's random number table and random number table of RAND Corporation are of most frequent use. The current study aims at attempting to make a comparative review on the degree on randomness of these four most frequently used random number tables based on  test, run test and deviation test. From the findings based on  test, the highest degree of randomness has been observed in random number table due to RAND Corporation followed by due to Kendall and Smith, Tippet, Fisher and Yates, respectively. In case of run test, the highest degree of randomness has been noticed in random number table due to Fisher and Yates followed by due to Tippet, RAND Corporation, Kendall and Smith, respectively. However, from the findings based on the deviation test, the highest degree of randomness has been observed in random number table due to Kendall and Smith followed by due to Fisher and Yates, RAND Corporation, Tippet, respectively. It can observed that the findings obtained in the studies based on different tests are not alike. Consequently, there is necessity to search for the reasons of the difference between these findings. Moreover, it can also be concluded that attempts should be made by the researchers to construct new random numbers table with enhanced degree of randomness than that of the existing tables.
Measuring the Caesarean Risk Factors in Bangladesh by Using Binary Logistic Regression Model
Authors: Rahaman Sabrina , Hossain Md. Murad , Ankhi Fahima Akter , Roy Madhusudan
Published: July 2019

Caesarean section (CS) has been on the rise worldwide and Bangladesh is no exception. In Bangladesh, the CS rate, which includes both institutional and community-based deliveries, has increased from about 3% in 2000 to about 24% in 2014. Rather than numerous impediments, cesarean conveyances are most basic among woman’s however it is not clinically advocated. For enhancing the maternal wellbeing status, it is basic to decide the risk components of cesarean conveyance. The primary focal point of this examination is to research and recognize the cesarean risk factors in the entire territory of Bangladesh. For this investigation, we have gathered auxiliary information from the Bangladesh Demographic and Health Survey (BDHS) 2014. This dataset has one record for every eligible woman as defined by the household schedule. It contains 17864 data which is collected in the woman's questionnaire plus some variables from the household. For the examination, a chi-square test was performed to identify the significant association between conveyance type (cesarean/non-cesarean) and socio-demographic and financial factor's individual. A binary logistic regression was completed to recognize the most effective factors on cesarean conveyance. We found that 5 factors (i.e respondent age, respondent highest education level, husband’s occupation, type of place of residence, wealth index) were measurably connected with conveyance type out of 13 chance elements. From this investigation, it is obvious to us that the above powerful factors may influences the mother's wellbeing status in Bangladesh.
Statistical Monitoring of Tobacco Moisture Using the Particle Size Distribution Data-a Multidimensional Scaling Audit
Authors: John Chisimkwuo , Omekara Chukwuemeka O.
Published: July 2019

Tobacco manufacturers see the tobacco moisture content as one of the determining factors in the quality of the finished tobacco product. During primary processing stage, the Particle Size Distribution (PSD) of the cut tobacco is a good measure of the tobacco moisture content. This paper presents statistical analyses of a two month PSD data using graphical techniques from noteworthy statistical multidimensional scaling (MDS) approaches in characterizing the tobacco moisture quality ratio. At the end, the evaluation within the investigated months fosters an indicative process audit, control and predictive monitoring that is capable of providing valuable impacts to future production.
The Principal Component Analysis Biplot Predictions versus the Ordinary Least Squares Regression Predictions: The Anthropometric Case Study
Authors: John Chisimkwuo , Omekara Chukwuemeka O. , Okwara Godwin
Published: July 2019

An indicative feature of a principal component analysis (PCA) variant to the multivariate data set is the ability to transform correlated linearly dependent variables to linearly independent principal components. Back-transforming these components with the samples and variables approximated on a single calibrated plot gives rise to the PCA Biplots. In this work, the predictive property of the PCA biplot was augmented in the visualization of anthropometric measurements namely; weight (kg), height (cm), skinfold (cm), arm muscle circumference AMC (cm), mid upper arm circumference MUAC (cm) collected from the students of School of Nursing and Midwifery, Federal Medical Center (FMC), Umuahia, Nigeria. The adequacy and quality of the PCA Biplot was calculated and the predicted samples are then compared with the ordinary least square (OLS) regression predictions since both predictions makes use of an indicative minimization of the error sum of squares. The result suggests that the PCA biplot prediction merits further consideration when handling correlated multivariate data sets as its predictions with mean square error (MSE) of 0.00149 seems to be better when compared to the OLS regression predictions with MSE of 29.452.
Comparative Study of Failure Rate of Bank’s ATM: Log Normal Distribution Approach
Authors: Cynthia Orumie, Ukamaka , Biu E. O.
Published: July 2019

This research determined time to failure rate and number of successful transaction of selected banks in Nigeria, using Log normal distribution. Transformation technique was applied to the log-normal model to obtain a quadratic equation or polynomial regression that assisted in determining the parameters of the log-normal model. In addition, one-way ANOVA was used to test for equality of the average (or mean) time to failure rate and average number of successful service time of the banks. The research fitted the log-normal models of the banks with the help of SPSS 21 statistical software and the result showed that GT-Bank model has the highest variation of 90.3% for number of successful service time (t), while Fidelity bank model has the highest variation of 56.6% for time of failure rate. The one-way ANOVA result of the number of successful service time (min) showed a significant difference. The Tukey comparison tests showed that GT bank is significant at 5% and 10% from other banks. Hence, the number of successful service time (min) were not the same for all the five banks. However, the one-way ANOVA result of the banks in term of number of Time to Failure (t) (min) showed no significant difference among the five banks.
Analysis of Individual Loan Defaults Using Logit under Supervised Machine Learning Approach
Authors: Obare Dominic M. , Njoroge Gladys G. , Muraya Moses M.
Published: July 2019

Financial institutions have a large amount of data on their borrowers, which can be used to predict the probability of borrowers defaulting their loan or not. Some of the models that have been used to predict individual loan defaults include linear discriminant analysis models and extreme value theory models. These models are parametric in nature since they assume that the response being investigated takes a particular functional form. However, there is a possibility that the functional form used to estimate the response is very different from the actual functional form of the response. The purpose of this research was to analyze individual loan defaults in Kenya using the logistic regression model. The data used in this study was obtained from equity bank of Kenya for the period between 2006 to 2016. A random sample of 1000 loan applicants whose loans had been approved by equity bank of Kenya during this period was obtained. Data obtained was on the credit history, purpose of the loan, loan amount, nature of the saving account, employment status, sex of the applicant, age of the applicant, security used when acquiring the loan and the area of residence of the applicant (rural or urban). This study employed a quantitative research design, it deals with individual loans defaults as group characteristics of a borrower. The data was pre-processed by seeding using R- Software and then split into training dataset and test data set. The train data was used to train the logistic regression model by employing Supervised machine learning approach. The R-statistical software was used for the analysis of the data. The test data set was used to do cross-validation of the developed logistic model which later was used for analysis prediction of individual loan defaults. This study focused on the analysis of individual loan defaults in Kenya using the logistic regression model in Machine learning. The logistic regression model predicted 303 defaults from train data set, 122 non-defaults and misclassified loans were 56 and 69. The model had an accuracy of 0.7727 with the train data and 0.7333 with the test data. The logistic regression model showed a precision of 0.8440 and 0.8244 with the train and test data respectively. The performance of the model with both the train and test data was illustrated using a plot of train errors and test errors against sample size on the same axes. The plot showed that the performance of the model increases with an increase in sample size. The study recommended the use of logistic regression in conjunction with supervised machine learning approach in loan default prediction in financial institutions and also more research should be carried out on ensemble methods of loan defaults prediction in order to increase the prediction accuracy.
Empirical Convergence Rate of a Markov Transition Matrix
Authors: Garren Steven T.
Published: July 2019

The convergence rate of a Markov transition matrix is governed by the second largest eigenvalue, where the first largest eigenvalue is unity, under general regularity conditions. Garren and Smith (2000) constructed confidence intervals on this second largest eigenvalue, based on asymptotic normality theory, and performed simulations, which were somewhat limited in scope due to the reduced computing power of that time period. Herein we focus on simulating coverage intervals, using the advanced computing power of our current time period. Thus, we compare our simulated coverage intervals to the theoretical confidence intervals from Garren and Smith (2000).
Multinomial Logistic Modelling of Socio-Economic Factors Influencing Spending Behavior of University Students
Authors: Akelo Gogo Jacqueline , Mbunzi Stephen Muteti , Ngari Cyrus Gitonga
Published: July 2019

This study aims at determining the use of Multinomial Logistic Regression (MLR) model which is one of the important methods for categorical data analysis. This model particularly deals with one nominal or ordinal response variable that has more than two categories. Despite the fact that many researchers have applied this model in data analysis in many areas, for instance behavioral, social, health, and educational, a study on spending habits of University students have never been done. To identify the model by practical way, we conducted a survey research among students from University of Embu. Segment of the population of students in undergraduate level, a sample of 376 was selected. We employed the use stratified random sampling and simple random sampling without replacement in each stratum. The response variable consisted of five categories. Four of explanatory variables were used for building the primary (MLR) model. The model was tested through a set of statistical tests to ensure its appropriateness for the data. From the results, the study reveals that year of study, family financial level, gender and school are significant factors in explaining spending habits of students. Despite the fact that gender is one of the deterministic factors of financial behavior of student, this model identified family level of income as a major deterministic factor. Conclusively, using MLR model accurately defines the relationship between the group of explanatory variables and the response variable. It also identifies the effect of each of the variables, and we can predict the classification of any individual case. The researchers recommend that, the Universities peer counselling department, should hold trainings on the basis of major determinant of financial spending behavior i.e. family financial level.
Odoma Distribution and Its Application
Authors: Odom C. C. , Ijomah M. A.
Published: July 2019

In this study, a new continuous one parameter lifetime distribution is proposed. Its mathematical properties such as moments, order statistics, entropy, survival function, hazard rate function and mean residual life function are derived. The new distribution is applied to real-life data from engineering and the method of maximum likelihood is used to estimate the parameter. The goodness-of-fit of the new distribution shows its better fit to the data than some competing distributions.
Modified Laplace Distribution, Its Statistical Properties and Applications
Authors: Agu F. I. , Onwukwe C. E.
Published: July 2019

Increasing the parameter of a distribution helps to capture the skewness and peakedness characteristic in the data sets. This allows a more realistic modeling of data arising from different real life situations. In this paper, we modified Laplace distribution using the exponentiation method. The study proved that the modified Laplace distribution (MLD) is a probability density function. Some of the basic statistical properties of the modified Laplace distribution are obtained. We applied the proposed modified Laplace distribution on two life datasets and simulated data. Parameters of the distributions were estimated using method of maximum likelihood estimation. The study compared the modified Laplace distribution with Laplace distribution and Generalized error distribution using Schwartz Criteria (SC) measure of fitness. The results obtained revealed that the modified Laplace distribution has a better fit than the Laplace and Generalized error distributions and can be used for more realistic modeling of data arising from different real life situations. The simulation results obtained shows that as the sample size increases, the Biasedness and Root Mean Square Error (RMSE) of the proposed modified Laplace distribution reduces.
Bayesian Models for Zero Truncated Count Data
Authors: Adesina Olumide S. , Agunbiade Dawud A. , Oguntunde Pelumi E. , Adesina Tolulope F.
Published: July 2019

It is important to fit count data with suitable model(s), models such as Poisson Regression, Quassi Poisson, Negative Binomial, to mention but a few have been adopted by researchers to fit zero truncated count data in the past. In recent times, dedicated models for fitting zero truncated count data have been developed, and they are considered sufficient. This study proposed Bayesian multi-level Poisson and Bayesian multi-level Geometric model, Bayesian Monte Carlo Markov Chain Generalized linear Mixed Models (MCMCglmms) of zero truncated Poisson and MCMCglmms Poisson regression model to fit health count data that is truncated at zero. Suitable model selection criteria were used to determine preferred models for fitting zero truncated data. Results obtained showed that Bayesian multi-level Poisson outperformed Bayesian multi-level Poisson Geometric model; also MCMCglmms of zero truncated Poisson outperformed MCMCglmms Poisson.
Small Area Procedures for Estimating Income and Poverty in Egypt
Authors: Saied Mai M. Kamal El , Talat Amal A. , Gohary Mervat M. El
Published: July 2019

In recent years, the demand for small area statistics has greatly increased worldwide. A recent application of small area estimation (SAE) techniques is in estimating local level poverty measures in Third World countries which is necessary to achieve the Millennium Development Goals. The aim of this research is to study SAE procedures for estimating the mean income and poverty indicators for the Egyptian provinces. For this goal the direct estimators of mean income and (FGT) poverty indicators for all the Egyptian provinces are presented. Also this study applies the empirical best/Bayes (EB) and the pseudo empirical best/Bayes (PEB) methods based on the unit level - nested error - model to estimate mean income and (FGT) poverty indicators for the Egyptian border provinces with (2012-2013) income, expenditure and consumption survey (IECS) data. The (MSEs) and coefficient of variations (C.Vs) are calculated for comparative purposes. Finally the conclusions are introduced. The results show that EB estimators for poverty incidence and poverty gap are smaller than PEB for all selected provinces. EB figures indicate that the largest poverty incidence and gap are for the selected municipality at the scope of the border south west of Egypt (New Valley). The PEB figures indicate that the largest poverty incidence and gap are for the selected municipality at the scope of the border north east of Egypt (North Sinai). As expected, estimated C.Vs for EB of poverty incidence and poverty gap estimators are noticeably larger than those of PEB estimators in all selected provinces.