This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned...

In this work, the empirical-Gramian-based model reduction methods: Empirical poor man’s truncated balanced realization, empirical approximate balancing, dominant subspaces, truncation, and gains are compared in a non-parametric two parametric variants, via ten error measures: Approximate Lebesgue \(L_0\), \(L_1\), \(L_2\), \(L_\infty \), Hardy \(H_2\), \(H_\infty Hankel, Hilbert-Schmidt-Hankel,...

Traditionally the forecast evaluation literature has focused on methods for assessing point forecasts and interval forecasts. Recently , however, density forecasting became very active and important area of research in the analysis of economic and financial time series with the introduction of simple and operational framework for density forecast evaluation by Diebold et al (1998). Density fore...

Multi-environment trials play a significant role in selecting the best cultivars to be used at different locations. The objective of this study was to identify grain and forage yields stability of grass pea advanced lines across different locations. The 14 advanced lines of grass pea, developed by the International Center for Agricultural Research in Dry Areas (ICARDA), were tested at three dif...

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software ca...

the aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. in addition, we derive the corrected score test statistic for outliers detection ba...

In this paper we have considered two one parametric generalizations. These two generalizations have in particular the well known measures such as: J-divergence, Jensen-Shannon divergence and arithmetic-geometric mean divergence. These three measures are with logarithmic expressions. Also, we have particular cases the measures such as: Hellinger discrimination, symmetric χ2−divergence, and trian...

We consider a family of Markov chains whose transition dynamics are affected by model parameters. Understanding the parametric dependence of (complex) performance measures of such Markov chains is often of significant interest. The derivatives of the performance measures w.r.t. the parameters play important roles, for example, in numerical optimization of the performance measures, and quantific...

We develop a new and general notion of parametric measure models and statistical models on an arbitrary sample space Ω which does not assume that all measures of the model have the same null sets. This is given by a differentiable map from the parameter manifold M into the set of finite measures or probability measures on Ω, respectively, which is differentiable when regarded as a map into the ...

Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining is often based on dist...