We propose a nonparametric discrimination method based on a nonparametric Nadaray-Watson kernel regression type-estimator of the posterior probability that an incoming observed vector is a given class. To overcome the curse of dimensionality of the multivariate kernel density estimate, we introduce a variance stabilizing approach which constructs independent predictor variables. Then, the multi...

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bay...

Generally, multiple statistical analysis methods can be applied for certain kind of data, and conclusion could differ, depending on the selected statistical method. Therefore, it is necessary to fully understand the performance of each statistical method and to examine which method is appropriate to use and to standardize statistical methods for toxicity studies to be carried out routinely. Sev...

Nonparametric regression provides a useful tool for exploring the association between the responses and covariates. In many applications, the actual values of the covariates are not known, but are measured with errors or through surrogates. In this paper, we present nonparametric regression techniques to study the association between the response and the underlying unobserved covariate. Deconvo...

In this research we examine a new method for pricing European call options based on a nonparametric estimate of the probability density of the underlying asset’s returns. Such an approach allows the use of asymmetric and leptokurtic distributions. We estimate the density using a kernel estimation technique applied to random samples drawn from three particular underlying distributions: the Gauss...

A well developed literature exists in relation to modeling and forecasting asset return volatility. Much of this relate to the development of time series models of volatility. This paper proposes an alternative method for forecasting volatility that does not involve such a model. Under this approach a forecast is a weighted average of historical volatility. The greatest weight is given to perio...