E-Bayesian and Bayesian Estimation for the Lomax Distribution under Weighted Composite LINEX Loss Function

The main contribution of this work is the development of a compound LINEX loss function (CLLF) to estimate the shape parameter of the Lomax distribution (LD). The weights are merged into the CLLF to generate a new loss function called the weighted compound LINEX loss function (WCLLF). Then, the WCLLF is used to estimate the LD shape parameter through Bayesian and expected Bayesian (E-Bayesian) estimation. Subsequently, we discuss six different types of loss functions, including square error loss function (SELF), LINEX loss function (LLF), asymmetric loss function (ASLF), entropy loss function (ENLF), CLLF, and WCLLF. In addition, in order to check the performance of the proposed loss function, the Bayesian estimator of WCLLF and the E-Bayesian estimator of WCLLF are used, by performing Monte Carlo simulations. The Bayesian and expected Bayesian by using the proposed loss function is compared with other methods, including maximum likelihood estimation (MLE) and Bayesian and E-Bayesian estimators under different loss functions. The simulation results show that the Bayes estimator according to WCLLF and the E-Bayesian estimator according to WCLLF proposed in this work have the best performance in estimating the shape parameters based on the least mean averaged squared error.

Testing for the rationality of central bank interest rate forecasts

In this paper, we study the bias in interest rate projections of five central banks, namely the central banks of the Czech Republic, New Zealand, Norway, Sweden, and the USA. We examine whether central bank projections are based on an asymmetric loss function and report evidence that central banks perceive an overprojection of their longer-term interest rate forecasts as twice as costly as an underprojection of the same size. We find that forecast rationality is consistent with biased interest rate projections under the assumption of an asymmetric loss function, which contributes to explaining the behavior of the examined central banks and their forecasts.

El Niño and forecastability of oil-price realized volatility

We forecast monthly realized volatility (RV) of the oil price based on an extended heterogenous autoregressive (HAR)-RV model that incorporates the role of the El Niño Southern Oscillation (ENSO), as captured by the Equatorial Southern Oscillation Index (EQSOI). Based on the period covering 1986 January to 2020 December and studying various rolling-estimation windows and forecast horizons, we find that the EQSOI has predictive value for oil-price RV particularly at forecast horizons from 2 to 4 years, and for rolling-estimation windows of length 4 to 6 years. We show that this result holds not only based on standard tests of out-of-sample predictability, but also under an asymmetric loss function.

A Seismic Image Denoising Method Based on Kernel-prediction CNN Architecture

To filter noises and preserve the details of seismic images, a denoising method based on kernel prediction convolution neural network (CNN) architecture is proposed. The method consists of two convolution layers and a residual connection, containing a source sensing encoder, a spatial feature extractor and a kernel predictor. The scalar kernel was normalized by the softmax function to obtain the denoised images. In addition, to avoid excessive blur at the expense of image details, the authors put forward the concept of asymmetric loss function, which would enable users to control the level of residual noise and make a trade-off between variance and deviation. The experimental results show the proposed method achieved good denoising effect. Compared with some other excellent methods, the proposed method increased the peak signal-to-noise ratio (PSNR) by about 1.0–3.2 dB for seismic images without discontinuity, and about 1.8–3.9 dB for seismic images with discontinuity.

An Efficient Network for Surface Defect Detection

Convolutional neural networks (CNN) have achieved promising performance in surface defect detection recently. Although many CNN-based methods have been proposed, most of them are limited by the few samples available for training, and the imbalance of positive and negative samples. Hence, their detection performance needs to be further improved. To this end, we propose a multi-scale cascade CNN called MobileNet-v2-dense to detect defects more efficiently. Specifically, the multi-scale cascade structure used in our network can help capture the weak defect semantics that may be lost in the deep network. Then, we propose a novel asymmetric loss function to further improve detection performance. Lastly, a two-stage augmentation method effectively enlarges the training dataset. Experimental results show that, compared to the state-of-the-art, the area under the receiver-operating characteristic curve (AUC-ROC) score of our method increased by 0.16.