Mean squared error bounds for the Rangan–Goyal soft thresholding algorithm

Root Mean Squared Error

• Predictive Accuracy Measures. These measures evaluate how close the recommender system came to predicting actual rating/utility values. • Classification Accuracy Measures. These measures evaluate the frequency with which a recommender system makes correct/incorrect decisions regarding items. • Rank Accuracy Measures. These measures evaluate the correctness of the ordering of items performed b...

Competitive Mean-Squared Error Beamforming

Beamforming methods are used extensively in a variety of different areas, where one of their main goals is to estimate the source signal amplitude s(t) from the array observations y(t) = s(t)a + i(t) + e(t), t = 1,2,..., where a is the steering vector, i(t) is the interference, and e(t) is a Gaussian noise vector [1, 2]. To estimate s(t), we may use a beamformer with weights w so that s(t) = w*...

Optimal Mean Squared Error Imaging

The problem of forming images that are optimal with respect to a Mean Square Error (MSE) criterion, based on nite data, is considered. First, it is shown that the MSE criterion is consistent with the general goal of classifying images, in that decreasing the MSE guarantees a decrease in the probability of misclassifying an image. The problem of choosing sampling locations for image formation th...

On the pointwise mean squared error of a multidimensional term-by-term thresholding wavelet estimator

Mean Squared Error of Empirical Predictor

The term “empirical predictor” refers to a two-stage predictor of a linear combination of fixed and random effects. In the first stage, a predictor is obtained but it involves unknown parameters; thus, in the second stage, the unknown parameters are replaced by their estimators. In this paper, we consider mean squared errors (MSE) of empirical predictors under a general setup, where ML or REML ...