Leo Odongo

Comparison of crisp and fuzzy classification trees using gini index impurity measure on simulated data

Acidified and non acidified samples of wastewaters from the Etoudi’s slaughterhouse (Yaounde, Cameroon) were analyzed before and after having been exposed to an electric discharge in a cold plasma reactor at different exposure times. Analyses reveal that untreated and non acidified wastewaters (pH = 7.8) contain 230.4 mgL-1 of phosphate (PO4 3-) and 479.6 mgL-1 of nitrate (NO3 -) ions. Exposure of these wastewaters to the gliding discharge (“glidarc”) operated in humid air induces PO4 3- and NO3 - concentrations abatement by 41.55% for phosphates and 86.24% for nitrates within 20 min of exposure for a gas flow rate of 800 Lh-1,which confirms the efficiency of the glidarc treatment in humid air in case of waste treatments. On the other hand, exposure of acidified wastewaters (pH = 2.2) to the glidarc in the same conditions showed that PO4 3- andNO3 - concentrations increase with exposure time; this result is in conformity with oxidation phenomena induced by the glidarc and previously presented by several authors. From these results the efficiency of the “glidarc” technique in degrading phosphates and nitrates in basic medium was proven.

A Poisson-Gamma Model for Zero Inflated Rainfall Data

Rainfall modeling is significant for prediction and forecasting purposes in agriculture, weather derivatives, hydrology, and risk and disaster preparedness. Normally two models are used to model the rainfall process as a chain dependent process representing the occurrence and intensity of rainfall. Such two models help in understanding the physical features and dynamics of rainfall process. However rainfall data is zero inflated and exhibits overdispersion which is always underestimated by such models. In this study we have modeled the two processes simultaneously as a compound Poisson process. The rainfall events are modeled as a Poisson process while the intensity of each rainfall event is Gamma distributed. We minimize overdispersion by introducing the dispersion parameter in the model implemented through Tweedie distributions. Simulated rainfall data from the model shows a resemblance of the actual rainfall data in terms of seasonal variation, means, variance, and magnitude. The model also provides mechanisms for small but important properties of the rainfall process. The model developed can be used in forecasting and predicting rainfall amounts and occurrences which is important in weather derivatives, agriculture, hydrology, and prediction of drought and flood occurrences.

LÉVY PROCESS BASED ORNSTEIN-UHLENBECK TEMPERATURE MODEL WITH TIME VARYING SPEED OF MEAN REVERSION

In this study we develop a Lévy process driven Ornstein-Uhlenbeck daily temperature model. The model takes into account a timedependent speed of mean reversion. It is statistically demonstrated that historical data and temperature differences are not normally distributed and hence we have argued against modeling temperature residuals as a Wiener process rather we have used the normal inverse Gaussian distribution which can ably describe skewed and heavy tailed data. Neural networks have been applied to estimate parameters of the detrended and deseasonalized temperature data because there is no prior knowledge on the nature of the function that describes the speed of mean reversion in the model. Nelson Christopher Dzupire, Philip Ngare and Leo Odongo 200

On Estimation of a Functional of a Probability Density Function

The problem of estimating the integral of the square of a probability density function is considered, It is shown that under some regularity conditions the kernel estimate of this functional is asymptotically normally distributed. An expression for the smoothing parameter that minimizes the mean square error of the estimate is derived. Results of simulation studies are included. AMS (1980) Subject Classification: Primary 62G07 Secondary 60FOS.