Considerable previous literature has addressed the problem of finding the most desirable estimator of the long memory parameter in a univariate time series. This paper considers three different estimation procedures: (i) the long memory parameter is obtained from a semi parametric Local Whittle estimator, which is used to filter the series before estimation of the short run parameters, (ii) a t...

The purpose of this study is estimation of daily Value at Risk (VaR) for total index of Tehran Stock Exchange using parametric, nonparametric and semi-parametric approaches. Conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated VaR and also to compare the performance of mentioned approaches. In most cases, based on backtesting statistics Results, ...

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in largescale video annotation due to several difficulties, such as large variation of video conte...

We formulate linear dimensionality reduction as a semi-parametric estimation problem, enabling us to study its asymptotic behavior. We generalize the problem beyond additive Gaussian noise to (unknown) nonGaussian additive noise, and to unbiased non-additive models.

We propose a root n consistent estimator for β0 when the qth conditional quantile of Y given X=x and Z=z takes the semi linear form g(x)+z′β0 where g(.) is an unknown real valued function,β0 a finite dimensional parameter and (X,Z)a couple of explanatory variables.Importantly, our estimator attains,under homoscedasticity,the semi parametric efficiency bound.This estimation is conducted in two s...