This paper introduces the Standardized Drought Analysis Toolbox (SDAT) that offers a generalized framework for deriving nonparametric univariate and multivariate standardized indices. Current indicators suffer from deficiencies including temporal inconsistency, and statistical incomparability. Different indicators have varying scales and ranges and their values cannot be compared with each othe...

The von Mises distribution is often useful for modelling circular data problems. We consider a model for which von Mises data is contaminated with a certain proportion of points uniformly distributed around the circle. Maximum likelihood estimation is used to produce parameter estimates for this mixture model. Computational issues involved with obtaining the maximum likelihood estimates for the...

In this paper, we propose a new diagnostic test for residual cross–section independence in a nonparametric panel data model. The proposed nonparametric cross–section dependence (CD) test is a nonparametric counterpart of an existing parametric CD test proposed in Pesaren (2004) for the parametric case. We establish an asymptotic distribution of the proposed test statistic under the null hypothe...

Abstract The main objective of this study was to examine the relationship between Value at Risk (VaR) and expected returns from 2002 to 2013 in Tehran’s Stock Exchange. In this study parametric value at risk, which considers the distribution of returns as normal and the historical value at risk as abnormal, was used to test the presence of the volatility anomaly in the companies listed i...

This paper is about constructing confidence bands around an ROC curve such that (1 − δ)% of the ROC curves traced by data sets of size r will fall completely within the bands. We introduce to the machine learning community three methods from the medical field that are applicable to generate such bands. We then evaluate these methods on the simple case of “binormal” distributions— the scores for...

Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of...

Suppose the random vector (X,Y ) satisfies the regression model Y = m(X) + σ(X)ε, where m(·) = E(Y |·), σ2(·) = Var(Y |·) and ε is independent of X. The covariate X is d-dimensional (d ≥ 1), the response Y is one-dimensional, and m and σ are unknown but smooth functions. In this paper we study goodness-of-fit tests for the parametric form of the error distribution under this model, without assu...

Parametric tests are widely applied by researchers in every discipline. However, sometimes the appropriateness of their applications is in question. In a parametric test an underlying distribution is assumed, and a sample statistic is obtained to estimate the population parameter. Because this estimation process involves a sample, a sampling distribution, and a population, certain assumptions a...