This is a comparative study on mixture distribution; where the study seeks to ascertain whether higher number of k-component mixtures could result to development of models that show better fits. In the performance comparison, special consideration was given to univariate one parameter distributions derived using mixture models, and the results show that distributions of higher k-mixture components relatively have greater propensity to exhibit better fit than the lesser mixture component distributions (k < 3).
This paper examines the application of autoregressive integrated moving average (ARIMA) model and regression model with ARIMA errors for forecasting Nigeria’s GDP. The data used in this study are collected from the official website of World Bank for the period 1990-2019. A response variable (GDP) and four predictor variables are used for the study. The ARIMA model is fitted only to the response variable, while regression with ARIMA errors is fitted on the data as a whole. The Akaike Information Criterion Corrected (AICc) was used to select the best model among the selected ARIMA models, while the best model for forecasting GDP is selected using measures of forecast accuracy. The result showed that regression with ARIMA(2,0,1) errors is the best model for forecasting Nigeria’s GDP.
Tuberculosis (TB) is one of the leading causes of mortality in developing countries in world. It is an airborne disease spread through inhaling. This study investigated the cases of tuberculosis at Nnamdi Azikiwe University Teaching Hospital (NAUTH) Nigeria. The TB data used in this study are secondary data sourced from NAUTH tuberculosis register from January 2005 to December 2021. This study is a retrospective cohort and time series analysis of all the cases of tuberculosis diagnosed and confirmed. The forecast methods used in this study are that of Box-Jenkins approach and Holt-Winters. Out of 395070 presumptive cases, 52311 (11.7%) were diagnosed with tuberculosis, and male had the highest rate. The age group that was most affected was the group 35-44 (24.68%). 8.4% of the tuberculosis diagnosed tested positive. ARIMA (0,0,1) (2,0,1) [12] was selected as the best model, used in forecasting tuberculosis cases for the next four years. Tuberculosis cases predicted showed that for the next four years, there will be a slight decrease.
The aim of this paper is to obtain the best model that will be used to predict Under-Five Mortality Rate (U5MR) between Autoregressive Integrated Moving Average (ARIMA) model and Weighted Markov Chains (WMC). The annual dataset of U5MR in Nigeria for the period 1980-2019 is obtained from the official website of World Bank. The descriptive statistics and the unit root test for the stationarity of data were carried on the data series. ARIMA was modelled to U5MR using the techniques of Box-Jenkins while WMC was modelled using the techniques of k-means cluster analysis, Chi-Square, and Correlation. The best ARIMA model was obtained using Bayesian Information Criterion (BIC) while the best forecast model was obtained using Theil’s U Statistics and Mean Absolute Percentage Error (MAPE). U5MR attained stationarity after third differencing under ARIMA model dynamics. ARIMA(0,3,2) is considered the best ARIMA model with BIC of -2.679, and was selected as the best forecast model with Theil’s U Statistic of 0.000014 and MAPE of 0.174336%. The fitted model was used to make out-sample forecast for the period 2020-2030, which showed a steady decline. The findings of this paper will help in establishment and implementation health policies.
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