Bini Customary law of inheritence : a shift in Igiogbe as a case study

Igiogbe cultural heritage has existed since the founding of Bini kingdom without any controversy; however since the Supreme Court decision in Idehen v Idehen the issue of Igiogbe has assumed new dimensions. Igiogbe - the house in which a Benin man lived and died devolves on his first son absolutely; but since the beginning of 20th century litigation as to the real meaning of Igiogbe and who is entitled to inheritance thereof began to increase. Controversies and increase in litigation over Igiogbe has occasioned a shift in the practice, the Bini’s are not conscious of some of these changes, most of them (Bini’s) still claim Igiogbe practices is rigidly adhered to. This study on Igiogbe inheritance in Bini kingdom is therefore carried out with a view to bringing out the changes in Igiogbe cultural practice using legal and anthropological tools to examine the changes. While laying the foundation for the discussion on the main research object the researcher examined the origin and status of customary law in Nigeria. There after I examined Igiogbe inheritance in Bini kingdom. Igiogbe and the issue of first son were critically analyzed with the aid of the research questions bringing out the changes in Igiogbe concept from traditional practice to modern practice. Study shows Igiogbe practice is still relevant in modern Bini kingdom, however, the shift and changes in practice of this cultural milieu has lead me to ask some fundamental questions which I intend to answer in the broader research work in future.

Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.