Performance of Robust Linear Classifier with Multivariate Binary Variables

Abstract

This paper focuses on the robust classification procedures in two group discriminant analysis with multivariate binary variables. A normal distribution based data set is generated using the R-software statistical analysis system 2.15.3 using Barlett"s approximation to chi-square, the data set was found to be homogenous and was subjected to five linear classifiers namely: maximum likelihood discriminant function, fisher"s linear discriminant function, likelihood ratio function, full multinomial function and nearest neighbour function rule. To judge the performance of these procedures, the apparent error rates for each procedure are obtained for different sample sizes. The results obtained ranked the procedures as follows: fisher"s linear discriminant function, maximum likelihood, full multinomial, likelihood function and nearest neigbour function.

The Curse of Low Soil Fertility and Diminishing Maize Yields in Semi-Arid Kenya:can Pigeonpea Play Saviour?

“…These results agree with those of Tarhalkar and Rao (1981), and Ali (1990) who indicated that intercropping cereals with early-maturing pigeonpea often led to reduction in pigeonpea yield. Similar results were reported by Egbo and Ngumalen (2010) from a two-year study in Nigeria, where intercropping decreased the number of pods per plant, dry pod weight and grain yield of the pigeonpea component, as well as the panicle length, panicle weight and dry grain yield of the cereal component. It is apparent from this study that, irrespective of how much crop residues is returned to the soil, both short and medium duration pigeonpea are not the best candidates for incorporation into maize-based cropping systems in the study area, since doing so depressed their grain and stalk yields significantly.…”