Application of Dynamic Linear Model on Data Corresponding to Chronic Asthma Disease (Published)
The linear Gaussian state space model, also known as dynamic linear model in the Bayesian literature, has become one of the standard parametric modeling forms with parameters changing over time in the time series analysis. It provides a unified and flexible framework for describing, modeling and forecasting a wide array of time series and other types of longitudinal data. There are several studies which have been concerned with describing the seasonal pattern of admission to hospital for children with asthma, and have also explained the relationship between unexpected medical contacts and the end of the summer holidays. In this paper we are interested to use asthma chronic disease data for constructing a dynamic linear model and investigate the behavior of this model also, we are interested to make one step ahead forecasting.
Keywords: Dynamic linear model, Local level model, Seasonal dynamic model., State space model, Time Series
Application of Cube Root Transformation of Error Component of Multiplicative Time Series Model (Published)
This paper makes use of cube transformation of the error component of multiplicative time series model. Data from federal road safety commission (FRSC) Nigeria on road accident were collected and analyzed by fitting the regression line of log mean (logmean) against log standard deviation (logstdev). This gave a fitted slope which agrees with the required value of 0.6666 this gives a transformation of 1-0.666977= 0.333023 (1- which is the cube root transformation. Data were later decomposed into time series components. Recommendations on areas of application of cube root transformation were equally given.
Keywords: Components, Cube Transformation, Logmean, Multiplicative, Time Series
Statistical Analysis of Non-Communicable Diseases in a Health Facility in Takoradi, Ghana (Published)
Current trends indicate that global Non-Communicable Diseases (NCD) accounts for about 60% of deaths and will increase by 17% over the next 10 years with poor and disadvantaged populations disproportionately affected, widening health disparities between and within countries. It is against these challenges that “Statistical Analysis of Non-Communicable diseases” was undertaken. The main objective of this paper was to determine the age groups that are affected most and also to determine the trend of each of the selected Non-Communicable Diseases. To achieve this, a five-year data set was collected from Takoradi Hospital. Results of the analysis of the data depict that, females dominate those who are suffering from Non-Communicable Diseases. Also, people within 20 to 34 year group are mostly affected by Non-Communicable Diseases. It also reveals that the number of cases of the Non-Communicable Diseases analyzed have declining trend with the exception of anemia. It was therefore recommended that, people from all walks of life must give due consideration to their diet and thus eat balanced diet and do regular exercise to keep them healthy.
Keywords: Forecast, Health Facility, Non-Communicable Disease, Takoradi-Ghana., Time Series
On The Tractability of Some Discordancy Statistics for Modelling Outliers in a Univariate Time Series (Published)
This paper compares the tractability of four discordancy statistics for modelling outliers based on extremeness. They are: the Generlaized Extreme Studentized Deviate (ESD), Grubb’s test, Hampel’s method and the quartile method. The last two methods are seen to detect outliers even for datasets that are not approximately normal, although Hampel outperforms the quartile method in some cases. However, a multiplier effect of 2.2 is proposed for the quartile method in addition to the robust statistics for accommodating the outliers.
Keywords: Autocorrelation function (ACF)., Generalized Extreme Studentized Deviate (ESD), Outliers, Time Series