This technical note presents explicit formulas for calculating the response moments of stochastic systems by polynomial dimensional decomposition entailing independent random input with arbitrary probability measures. The numerical results indicate that the formulas provide accurate, convergent, and computationally efficient estimates of the second-moment properties. DOI: 10.1061/ ASCE EM.1943-...

We consider decomposition approaches for the solution of multistage stochastic programs that appear in financial applications. In particular, we discuss the performance of two algorithms that we test on the mean-variance portfolio optimization problem. The first algorithm is based on a regularized version of Benders decomposition, and we discuss its extension to the quadratic case. The second a...

In this paper an inverse QR decomposition based recursive least-squares algorithm for linearly constrained minimum variance filtering is proposed. The proposed algorithm is numerically stable in finite precision environments and is suitable for implementation in systolic arrays or DSP vector architectures. Its performance is illustrated by simulations of a blind receiver for a multicarrier CDMA...

Most analysis of U -statistics assumes that data points are independent or stationary. However, when we analyze network data, these two assumptions do not hold any more. We first define the problem of weighted U -statistics on networked data by extending previous work. We analyze their variance using Hoeffding’s decomposition and also give exponential concentration inequalities. Two efficiently...

This study examines the relationship between share of renewable energy sources in total consumption and economic growth by comparing data Kazakhstan Turkiye using annual for 1991-2019 period. We collected from World Bank website (https://data.worldbank.org/indicator/). Impulse - Response Analysis Variance Decomposition were applied to Vector Autoregressive (VAR) model. Research findings showed ...

Standard factorial designs in psycholinguistics have been complemented recently by large-scale databases providing empirical constraints at the level of item performance. At the same time, the development of precise computational architectures has led modelers to compare item-level performance with item-level predictions. It has been suggested, however, that item performance includes a large am...

Let f be a finite variance function of d independent input variables. Sobol’ indices are used to measure the importance of input variables and subsets of them. They are based on a variance decomposition. A similar problem arises in economics, when the value produced through the joint efforts of a team is to be attributed to individual members of that team. The Shapley value is widely used to so...

We propose a decomposition of posterior variance somewhat in the spirit of an ANOVA decomposition. Terms in this decomposition come in pairs. Given a single parametric model, for instance, one term describes uncertainty arising because the parameter value is unknown while the other describes uncertainty propagated via uncertainty about which prior distribution is appropriate for the parameter. ...

The problem of jointly estimating time delay and frequency of a signal received at two separated sensors is addressed in this paper. These methods require eigenvalue decomposition (EVD) of the covariance matrix of received data. Thus, the computational load and complexity is high especially when the number of samples is large. In addition, the noises field is assumed to be uncorrelated with con...

Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is not satisfied in many applications such as facial image data when variations, e.g. angle and illumination, can significantly influence the images. In this paper, we propose a novel meth...