This paper constructs a new estimator for large covariance matrices by drawing bridge between the classic (Stein (1975)) in finite samples and recent progress under large-dimensional asymptotics. The keeps eigenvectors of sample matrix applies shrinkage to inverse eigenvalues. corresponding formula is quadratic: it has two targets weighted quadratic functions concentration (that is, dimension d...

The separation of signal and noise is a central issue in seismic data processing. The noise is both random and coherent in nature, the coherent part often masquerading as signal. In this tutorial, we present some approaches to signal isolation, in which stacking is a central concept. Our methodology is to transform the data to a domain where noise and signal are separable, a goal that we attain...

We consider some inference problems concerning the drift parameters vector of diffusion process. Namely, we consider the case where the parameters vector is suspected to satisfy certain restriction. Under such a design and imprecise prior information, we propose Stein-rule (or shrinkage) estimators which improves over the performance of the classical maximum likelihood estimator (MLE). By using...

This paper considers the problem of estimating a high-dimensional vector of parameters θ ∈ R from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension n exceeds two. The JS-estimator shrinks the observed vector towards th...

Applied researchers often confront two issues when using the fixed effect-two-stage least squares (FE-2SLS) estimator for panel data models. One is that it may lose its consistency due to too many instruments. The other gain of FE-2SLS not exceed loss endogeneity weak. In this paper, an L2Boosting regularization procedure models proposed tackle instruments issue. We then construct a Stein-like ...

Algorithms to solve variational regularization of ill-posed inverse problems usually involve operators that depend on a collection of continuous parameters. When these operators enjoy some (local) regularity, these parameters can be selected using the socalled Stein Unbiased Risk Estimate (SURE). While this selection is usually performed by exhaustive search, we address in this work the problem...

Recently‎, ‎the beta regression model has been used in several fields of science to data the form rate or proportion‎. ‎In this paper‎, ‎we propose some novel and improved methods estimate parameters model‎. ‎We consider a sub-space on coefficients combine unrestricted restricted estimators then we present Stein-type preliminary estimators‎. develop expressions for proposed estimators' asymptot...

In this work, we construct a risk estimator for hard thresholding which can be used as a basis to solve the difficult task of automatically selecting the threshold. As hard thresholding is not even continuous, Stein’s lemma cannot be used to get an unbiased estimator of degrees of freedom, hence of the risk. We prove that under a mild condition, our estimator of the degrees of freedom, although...