Generalizations of Mean Square Error Applied to Ridge Regression

Structural Content Laplacian Mean Square Error

Measurement of the quality of image compression is important for image processing application. In this paper, we propose an objective image quality assessment to measure the quality of gray scale compressed image, which is correlation well with subjective quality measurement (MOS) and least time taken. The new objective image quality measurement is developed from a few fundamental of objective ...

A Modified Least Mean Square Method Applied to Frequency Relaying

Optimum thresholding using mean and conditional mean square error

We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity (IA). The nonparametric threshold estimator ˆ IV n of the integrated variance IV := ∫ T 0 σ sds proposed in [6] is constructed using observations on a discrete time grid, and precisely it sums up the squared increments of the process when they are under a thres...

A Coverage Approach to Evaluating Mean Square Error

We propose a new method for evaluating the mean square error (mse) of a possibly biased estimator 1̂ θ , or, rather, the class of estimators to which it belongs. The method uses confidence intervals c of a corresponding unbiased estimator θ̂ and makes its assessment based on the extent to which c includes 1̂ θ . The method does not require an estimate, implicit or explicit, of the bias of 1̂ θ , is...

Partial least square regression applied to the QTLMAS 2010 dataset

BACKGROUND Partial least square regression (PLSR) was used to analyze the data of the QTLMAS 2010 workshop to identify genomic regions affecting either one of the two traits and to estimate breeding values. PLSR was appropriate for these data because it enabled to simultaneously fit several traits to the markers. RESULTS A preliminary analysis showed phenotypic and genetic correlations betwee...