We evaluate two density estimators based on the quantile variance and quantile-mean covariance estimated by bootstrap. review previous developments estimation related to quantiles. Monte Carlo simulations for different data generating processes, sample sizes, other parameters show that perform well in comparison benchmark non-parametric kernel estimator. Some of explored smoothing techniques pr...

The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix asset returns, estimating optimal solution remains challenge. In presence high-dimensionality in data, sample estimator becomes ill-conditioned leads to suboptimal portfolios out-of-sample. To address this is...

This paper proposes a statistical sensitivity of 2-D separabledenominator digital filters using the state-space approach. The sensitivity in this paper is represented by the controllability and the observability gramians and the state covariance matrix of the filters. The minimum sensitivity structures can be synthesized by giving an assumption to simplify the sensitivities. Numerical examples ...