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This dissertation develops a first-order multivariate Poisson integer-valued autoregressive (MPINAR(1)) model to improve disease outbreak detection in public health surveillance systems. The MPINAR(1) model is proposed as a computationally tractable alternative to the general multivariate...

This dissertation develops a negative binomial integer-valued generalized autoregressive conditional heteroscedastic model of order p, q (negative binomial INGARCH(p, q)) for overdispersed count time series data with potential extreme values. The model is formulated such that its conditional...

Human activities such as burning fossil fuels, cutting down forests and livestock farming have caused an increase in the earth’s surface temperature by 1.0 °C since the preindustrial levels. Warmer temperatures over time are altering global weather patterns. These alterations have been resulting...

Tax revenue forecasting is of critical importance for a government in ensuring adequacy and stability in tax and expenditure policies, as it also contributes to the budget and strategic planning of a country. Henceforth, several tax types need to be projected for the specific fiscal year using...

This study aimed to determine the maximum likelihood (ML) estimates for the Gamma and Exponential distributions under conditions of left-truncated and right-censored data, employing the Expectation-Maximisation (EM) algorithm. Additionally, it sought to compare the performance of the EM algorithm...

Agricultural drought conditions driven by delayed onset of the rainy season is one of the main contributing factors to Acute Food Insecurity (AFI). A number of studies on AFI severity in Zambia have been done at national level but no assessment of severity of AFI at district level has been done....

In this dissertation, we present an integer-valued generalised autoregressive conditional heteroscedastic (INGARCH)(p, q) model based on the Poisson distribution. This model proves to be effective for modelling overdispersed integer-valued time series with conditional heteroscedasticity. The...

Finite mixture models are an important tool in modelling the distribution of a wide class of statistical problems. Mathematically, a g−component finite mixture model of multivariate t-distributions is specified by a convex combination (mixture distribution) of g multivariate t-distributions,...

In this dissertation, we examine the small and large sample properties of the U-statistics (Un) for an estimable parameter : For small sample, Un is found to be MVUE and Su cient. Large sample properties of Un examined are Consistency and Asymptotic normality. We also compare the rate of...

Zambia has been experiencing adverse impacts of climate change. Generation of climate information about changes in future precipitation and temperature is useful in designing adaptive measures. Currently, global climate models (GCMs) are primary tools utilized to simulate the present and future...