Springer

 Others

 On Some Nonparametric Classifiers Based On Distribution Functions Of Multivariate Ranks

 Makinde O. S. And Chakraborty B.

 2015

Over the last two decades, multivariate sign and rank based methods have become popular in analysing multivariate data. In this paper, we propose a classification methodology based on the distribution of multivariate rank functions. The proposed method is fully nonparametric in nature. Initially, we consider a theoretical version of the classifier for K populations and show that it is equivalent to the Bayes rule for spherically symmetric distributions with a location shift. Then we present the empirical version of that and show that the apparent misclassification rate of the empirical version of the classifier converges asymptotically to the Bayes risk. We also present an affine invariant version of the classifier and its optimality for elliptically symmetric distributions. We illustrate the performance in comparison to some other depth based classifiers using simulated and real data sets.

 

 Wayne State University

 Journal

 Identification Of Optimal Autoregressive Integrated Moving Average Model On Temperature Data

 Makinde, O.S. And Fasoranbaku, O.A.

 2011

Autoregressive Integrated Moving Average (ARIMA) processes of various orders are presented toidentify an optimal model from a class of models. Parameters of the models are estimated using anOrdinary Least Square (OLS) approach. ARIMA (p, d, q) is formulated for maximum daily temperaturedata in Ondo and Zaira from January 1995 to November 2005. The choice of ARIMA models of orders pand q is intended to retain persistence in a natural process. To determine the performance of models,Normalized Bayesian Information Criterion is adopted. The ARIMA (1, 1, 1) is adequate for modelingmaximum daily temperature in Ondo and Zaira; model parameters are estimated and redundant variablesare removed. Causality and the invertibility behavior of some optimal models are also presented.