In this paper we will precisely analyze the nearest neighbor method for different dissimilarity measures, classical and weighed, for which methods of distinguishing were worked out.We will propose looking for weights in the space of discriminant coordinates.Experimental results based on a number of real data sets are presented and analyzed to illustrate the benefits of the proposed methods.As classical dissimilarity measures we will use the Euclidean metric, Manhattan and post office metric.We gave the first two metrics weights and now these measures are not metrics because the triangle inequality does not hold.Howeover, it does not make them useless for the nearest neighbor classification method.Additionally, we will analyze different methods of tie-breaking.
A linear geostatistical model is considered. Properties of a universal kriging are studied when the locations of observations are measured with errors. Alternative prediction procedures are introduced and their least squares errors are analyzed.
The first-order autoregressive model with uniform innovations is considered.In this paper, we propose a family of BAYES estimators based on a class of prior distributions.We obtain estimators of the parameter which perform better than the maximum likelihood estimator.
Weerahandi (1995b) suggested a generalization of the Fisher’s solution to the Behrens-Fisher problem to the problem of multiple comparisons with unequal variances by the method of generalized p-values. In this paper we present a brief outline of the Fisher’s solution and its generalization as well as the methods to calculate the p-values required for deriving the conservative joint confidence interval estimates for the pairwise mean differences, refered to as the generalized Scheff é int rvals. Further, we present the corresponding tables with critical values for simultaneous comparisons of the mean differences of up to k = 6 normal populations with unequal variances based on independent random samples with very small sample sizes. MSC: primary 62F04; secondary 62E15
