998 Results Found

We are interested in an n by p matrix ${\mathbf{X}}_n$ where the n rows are strictly stationary $\alpha$-mixing random vectors and each of the p columns is an independent and identically distributed random vector; $p=p_n$ goes to infinity as $n\to \infty$, satisfiyin...

One of the tasks of data science is the decomposition of large matrices in order to understand their structures. A special case of this is when we decompose relations, i.e., logical matrices. In this paper, we present a method based on the similarity...

Deriving the form of the optimal solution of a maximum entropy problem, we obtain an infinite family of linear inequalities characterizing the polytope of spin correlation matrices. For n ≤ 6, the facet description of such a polytope is provided thro...

Specifying time-dependent correlation matrices is a problem that occurs in several important areas of finance and risk management. The goal of this work is to tackle this problem by applying techniques of geometric integration in financial mathematic...

Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models...

Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately ad...

Singular value decomposition (SVD) plays a critical role in signal processing, image analysis, and particularly in MIMO channel estimation, where it enables spatial multiplexing and interference mitigation. This study presents a configurable parallel...

In the domain of functional magnetic resonance imaging (fMRI) data analysis, given two correlation matrices between regions of interest (ROIs) for the same subject, it is important to reveal relatively large differences to ensure accurate interpretat...

The problem of the contamination of soils with high background values of heavy metals has attracted increasing attention. In this paper, the contents, spatial distribution characteristics and correlations of five heavy metals in seven types of arable...

In the current study, matrices of losartan potassium were formulated with two different polymers (Ethocel 10 premium and Ethocel 10FP premium), along with a filler and a lubricant, at different drug-to-polymer w/w ratios (10:3, 10:4, and 10:5). The m...

This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times,...

(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending o...

Accounting for realistic observation errors is a known bottleneck in data assimilation, because dealing with error correlations is complex. Following a previous study on this subject, we propose to use multiscale modelling, more precisely wavelet tra...

Sustainable and adjusted soil management practices are crucial for soil quality, namely in terms of the nutrient budget. On the other hand, soil characteristics are interlinked with agricultural sustainability and food supply. In other words, soil qu...

Standard correlation analysis is one of the frequently used methods in financial markets. However, this matrix can give erroneous results in the conditions of chaos, fractional systems, entropy, and complexity for the variables. In this study, we emp...

With the development of new methodologies for faster training on datasets, there is a need to provide an in-depth explanation of the workings of such methods. This paper attempts to provide an understanding for one such correlation filter-based track...

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a key economic region in China facing increasing pressure to balance socioeconomic development with environmental protection and energy conservation. This study examines the interrel...

As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE...

The paper aims to emphasize the advantages of several advanced statistical and data mining techniques when applied to the dense literature on corruption measurements and determinants. For this purpose, we used all seven waves of the World Values Surv...

Carbonized elastomer-based composites (CECs) possess a number of attractive features in terms of thermomechanical and electromechanical performance, durability in aggressive media and facile net-shape formability, but their relatively low ductility a...

We review recent progress in modeling credit risk for correlated assets. We employ a new interpretation of the Wishart model for random correlation matrices to model non-stationary effects. We then use the Merton model in which default events and los...

Antioxidants play a crucial role in mitigating oxidative stress and preventing cellular damage caused by free radicals. This study aimed to compare the effectiveness of three antioxidant assays—DPPH, TEAC, and FRAP—in quantifying the anti...

Motivated by the need for a simple and effective assimilation scheme that could be used in a relocatable ocean model, a new assimilation algorithm called ensemble optimal smoothing (EnOS) was developed. This scheme was a straightforward extension of...

Plenty of humic acid components compositions are contained in dissolved organic matter (DOM) derived from composting. Fluorescence signals were employed to characterize the changes in DOM components in the component process. In the composting process...

The multiscale variability of the ocean circulation due to its nonlinear dynamics remains a big challenge for theoretical understanding and practical ocean modeling. This paper demonstrates how the data-adaptive harmonic (DAH) decomposition and inver...

Casein kinase-1 alpha (CK1α) is a multifunctional protein kinase that belongs to the serine/threonine kinases of the CK1α family. It is involved in various signaling pathways associated with chromosome segregation, cell metabolism, cell c...

Electroencephalography (EEG) provides valuable information about a subject’s mental state; however, developing reliable classification models remains challenging. One major difficulty lies in defining an effective feature representation, as the...

We proposed in this work the introduction of a new vision of stochastic processes through geometry induced by dilation. The dilation matrices of a given process are obtained by a composition of rotation matrices built in with respect to partial corre...

Working memory capacity (WMC) and reasoning abilities—as assessed by figural matrices tests—are substantially correlated. It is controversially discussed whether this correlation is only caused by controlled attention or also by storage capacity. Thi...

In this paper, we combine the periodogram method for perturbed block Toeplitz matrices with the Cholesky decomposition to give a parameter estimation method for any perturbed vector autoregressive (VAR) or vector moving average (VMA) process, when we...

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearran...

For a finite field GF($p^L$) with prime p and $L>1$, one of the standard representations is $L\times L$ matrices over GF(p) so that the arithmetic of GF($p^L$) can be realized by the arithmetic among these matrices over GF(p). Based on the matrix representa...

This article presents biplots derived from factor analysis of correlation matrices for both continuous and ordinal data. It introduces biplots specifically designed for factor analysis, detailing the geometric interpretation for each data type and pr...

Traditional correlation analysis is analyzed separately in the time domain or the frequency domain, which cannot reflect the time-varying and frequency-varying characteristics of non-stationary signals. Therefore, a time–frequency (TF) correlat...

Background: The present report tries to understand the possible relationship between working memory (WM) and intelligence measurements, using the direct scores of the Working Memory Test Battery for Children (WMTBC) and Kaufman’s Brief Intellig...

In this paper, we study the phase structure of the product of D * D order matrices. In each round, we randomly choose a matrix from a finite set of d matrices and multiply it with the product from the previous round. Initially, we derived a functiona...

This study provides fully mathematically and economically feasible solutions to estimating implied correlation matrices in equity markets. Factor analysis is combined with option data to receive ex ante beliefs for cross-sectional correlations. Neces...

Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctua...

Background/Objectives: Fatty acids, the building blocks of lipids, contribute to numerous crucial life processes and are implicated in numerous disease pathologies. Circulating fatty acids can be extracted/trans-esterified to their respective methyl...

Figural matrices tests are among the most popular and well-investigated tests used to assess inductive reasoning abilities. Solving these tests requires the selection of a target that completes a figural matrix among distractors. Despite their genera...

Background: Several studies have established correlations between low serum levels of vitamin D and various pathologies, such as diabetes mellitus and its complications. However, few studies analyze its levels in matrices other than blood plasma, suc...

The study examined the intricate relationships between embankment slope configurations, toe drain designs, and drawdown scenarios. It utilized a unique combination of numerical, physical, and mathematical models. The investigation involved 16 numeric...

Quantum networks offer a realistic and practical scheme for generating multiparticle entanglement and implementing multiparticle quantum communication protocols. However, the correlations that can be generated in networks with quantum sources and loc...

Knowledge spillover and capital agglomeration caused by population migration behavior are of great significance for improving the carrying capacity and adaptability of the urban economy and promoting high-quality economic development. Based on the bi...

Soil pore structure and soil water content are critical regulators of microbial activity and associated carbon dioxide (CO₂) emissions. This study evaluated the impacts of soil bulk density and matric potential on carbon dioxide (CO₂) emissions throu...

By utilizing the properties of positive definite matrices, mathematical expectations, and positive linear functionals in matrix space, the Kantorovich inequality and Wielandt inequality for positive definite matrices and random variables are obtained...

An HPLC-DAD separation method for the simultaneous quantification of ten compounds from Moringa oleifera plant was developed. The method was validated with pure solvent and different matrices of M. oleifera products. This method was found to be linea...

In this work, we present numerical methods appropriate for parameter estimation in high-dimensional statistical modelling. The solution of these problems is not unique and a crucial question arises regarding the way that a solution can be found. A co...

This paper is motivated by the need to stabilise the impact of deep learning (DL) training for medical image analysis on the conditioning of convolution filters in relation to model overfitting and robustness. We present a simple strategy to reduce s...

The S-box is a key component of modern ciphers, determining the quality and performance of the cryptographic algorithms in which it is applied. Many constructions for synthesizing high-quality S-boxes have been established, and those based on Galois...