It is shown that e¢ cient GMM (generalized method of moments) estimation of a linear model corresponds to standard IV (instrumental variables) estimation of this model, after transforming it such (as in GLS) that its resulting disturbances have a scalar covariance matrix, while using as instruments the original instruments linearly transformed by the transpose of the inverse of the matrix used ...

Particle filtering (PF) has been widely used in solving nonlinear/non Gaussian filtering problems. Inferring to the target tracking in a wireless sensor network (WSN), distributed PF (DPF) was used due to the limitation of nodes’ computing capacity. In this paper, a novel filtering method—asynchronous DPF (ADPF) for target tracking in WSN is proposed. There are two keys in the proposed algorith...

In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background....

The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton, and Yaron (1996) is in principle able to reduce the small-sample bias, but it involves high-dime...

In this paper, we investigate two statistical models for the source-filter based single channel speech separation task. We incorporate source-driven aspects by pitch estimation in the model-driven method which models the vocal-tract part as a priori knowledge. This approach results in a speaker independent (SI) source separation method. For modeling the vocal tract filters Gaussian mixture mode...

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is mor...

Timbral modeling is fundamental in content based music similarity systems. It is usually achieved by modeling the short term features by a Gaussian Model (GM) or Gaussian Mixture Models (GMM). In this article we propose to achieve this goal by using the GMM-supervector approach. This method allows to represent complex statistical models by an Euclidean vector. Experiments performed for the musi...

Gaussian mixture model or Gaussian mixture density model(GMM) uses the likelihood function as a measure of fit. We show that just the same algorithm as the GMM can be derived from a modified objective function of Fuzzy c-Means (FCM) clustering with the regularizer by K-L information, only when the parameter λ equals 2. Although the fixed-point iteration scheme of FCM is similar to that of the G...

I propose a nonparametric iid bootstrap that achieves asymptotic refinements for t tests and confidence intervals based on the generalized method of moments (GMM) estimators even when the model is misspecified. In addition, my bootstrap does not require recentering the bootstrap moment function, which has been considered as a critical procedure for bootstrapping GMM. The elimination of the rece...

In this article we present an efficient approach to modeling the acoustic features for the tasks of recognizing various paralinguistic henomena. Instead of the standard scheme of adapting the Universal Background Model (UBM), represented by the Gaussian ixture Model (GMM), normally used to model the frame-level acoustic features, we propose to represent the UBM by building monophone-based Hidde...