Artificial neural networks have emerged as a good candidate to mathematical wear models, due to their capabilities of handling nonlinear behavior, learning from experimental data and generalization. In the present work the potential of using neural networks for the prediction of abrasive wear properties of unfilled and graphite filled carbon fabric reinforced epoxy composite under various testi...

K E Y W O R D S AND PHRASES Minimax decision rule; squared error loss; Dirichlet process; compact sets; Bayes rules; isotonic regression. l . INTRODUCTION In an often cited paper, BOHLMANN (1976) has considered linear minimax estimators for the mean of a univariate distribution in a nonparametr ic setting under squared error loss. To be precise, BOHLMANN assumes that Fo(x ) is a family of C D F...

The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...

Abstract In this paper, we consider two iterative algorithms for the phase retrieval problem: well-known Error Reduction method and Amplitude Flow algorithm, which performs minimization of amplitude-based squared loss via gradient descent. We show that can be interpreted as a scaled applied to minimize same loss, allows establish its convergence properties. Moreover, class measurement scenarios...

Linearity, sometimes jointly with constant variance, is routinely assumed in the context of sufficient dimension reduction. It is well understood that, when these conditions do not hold, blindly using them may lead to inconsistency in estimating the central subspace and the central mean subspace. Surprisingly, we discover that even if these conditions do hold, using them will bring efficiency l...

Abstract Although ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify needed weighting dependence on circumstances. The goal present contribution therefore to develop and conditions need be satisfied order solution mean squared error (MSE) superior ionosphere-...

Prediction on the basis of censored data is very important topic in many fields including medical and engineering sciences. In this paper, based on progressive Type-II right censoring scheme, we will discuss Bayesian two-sample prediction. A general form for lifetime model including some well known and useful models such asWeibull and Pareto is considered for obtaining prediction bounds ...