Article
Mathematics
Bo Jiang, Yongge Tian
Summary: This paper provides a comprehensive matrix analysis of the equivalence problems in estimation and inference results between a true multivariate linear model and its misspecified form with an augmentation part. The study covers the matrix derivation of the best linear unbiased estimators and the establishment of necessary and sufficient conditions for the equivalence of different estimators under the model assumptions.
Article
Mathematics
Fan Yuan, Shengguo Li, Hao Jiang, Hongxia Wang, Cheng Chen, Lei Du, Bo Yang
Summary: A novel algorithm is proposed in this paper to reduce a banded symmetric generalized eigenvalue problem to a banded symmetric standard eigenvalue problem, using the sequentially semiseparable (SSS) matrix techniques. The algorithm requires linear storage cost and offers potential for parallelism.
Article
Mathematics, Applied
Oskar Jakub Szymanski, Michal Wojtylak
Summary: The paper presents methods for eigenvalue localization of regular matrix polynomials, with a focus on investigating the stability of matrix polynomials. A stronger notion of hyperstability is introduced and discussed extensively. Matrix versions of the Gauss-Lucas theorem and Szasz inequality are demonstrated. Tools for studying (hyper)stability using multivariate complex analysis methods are provided. Several second- and third-order matrix polynomials with specific semi-definiteness assumptions on coefficients are proven to be stable.
LINEAR ALGEBRA AND ITS APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang
Summary: The article introduces the potential of dimensionality reduction regression methods and their extensions in handling high-dimensional data and improving the interpretability of regression models, validates their effectiveness through simulation studies and real-world applications, and provides suggestions for their value and applications in future omics research.
Article
Computer Science, Artificial Intelligence
Ziheng Li, Feiping Nie, Rong Wang, Xuelong Li
Summary: Matrix completion aims to estimate the missing entries of a low-rank and incomplete data matrix. Existing methods face problems with noise disturbance and the need for presetting a reasonable rank value. Therefore, this paper proposes a robust rank-one matrix completion method that divides the incomplete and noisy data matrix into two parts, approximates the low-rank part using a weighted rank-one matrix pursuit algorithm, and estimates the rank of the matrix using an adaptive weight vector. Experimental results demonstrate the performance of the proposed method for incomplete matrices disturbed by sparse noise.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Interdisciplinary Applications
Raanju R. Sundararajan
Summary: Dimension reduction techniques for multivariate time series transform the observed series into lower-dimensional multivariate subseries using a spectral domain method, allowing for decomposition and reconstruction of the series.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Geosciences, Multidisciplinary
Jingfei He, Yanyan Wang, Yatong Zhou
Summary: This paper proposes a method to recover 3D seismic data using truncated nuclear norm (TNN) in order to better utilize seismic information contained in small singular values. Experimental results demonstrate that the proposed method achieves superior reconstruction results than the traditional MSSA method.
JOURNAL OF APPLIED GEOPHYSICS
(2023)
Article
Statistics & Probability
Opeoluwa F. Oyedele
Summary: Multivariate statistics focuses on exploring relationships between different sets of variables, with regression analysis revealing the impact of predictor variables on response variables. The biplot allows for a visual representation of samples, predictor variables, and response variables in a single graph.
JOURNAL OF APPLIED STATISTICS
(2021)
Article
Plant Sciences
Elzbieta Wojcik-Gront, Dariusz Gozdowski, Adriana Derejko, Rafal Pudelko
Summary: The study aims to determine the most important agronomic and environmental variables in soybean production in Central and Eastern Europe, finding that soybean yield variability is mainly influenced by water availability to plants and physical soil properties.
Article
Computer Science, Information Systems
Dongting Xu, Zhisheng Zhang, Jinfei Shi
Summary: Manufacturers struggle to predict rare events using data from multiple products production lines, and there is little research on quantitatively selecting training data. This study proposes a training data selection method to improve the performance of deep learning models, which can measure the similarities between multivariate time series using categorical variables.
Article
Mathematics
Dijana Mosic, Predrag S. Stanimirovic, Spyridon D. Mourtas
Summary: The purpose of this paper is to investigate the solvability of systems of constrained matrix equations in the form of constrained minimization problems. The main novelty of this paper is unifying the solutions of the considered matrix equations with the corresponding minimization problems. For a specific case, some well-known results are extended and several new results for the weak Drazin inverse are provided. The main characterizations of the Drazin inverse, group inverse, and Moore-Penrose inverse are obtained as consequences.
Article
Engineering, Aerospace
Yun Sun, Qiuwei Yang, Xi Peng
Summary: In this paper, a damage assessment approach using multiple-stage dynamic flexibility analysis is proposed for structural safety monitoring. The approach determines the number of damaged elements, the damage locations, and the damage extents through rank analysis and matrix correlation. Numerical and experimental results demonstrate that the proposed method has strong antinoise ability and high calculation accuracy, making it a promising tool for structural damage assessment.
Review
Instruments & Instrumentation
J. Renwick Beattie, Francis W. L. Esmonde-White
Summary: Spectroscopy rapidly captures a large amount of data, which is processed using principal component analysis to simplify complex spectral datasets into comprehensible information. Despite its wide use, the linear algebra behind principal component analysis is often not well understood by applied scientists and spectroscopists. The process traces the journey of spectra and relies solely on the information within the spectra to provide meaningful interpretation and analysis.
APPLIED SPECTROSCOPY
(2021)
Article
Chemistry, Analytical
Farzaneh Bayati, Daniel Trad
Summary: Addressing insufficient and irregular sampling is a challenge in seismic processing. Rank reduction methods have become popular for denoising and interpolating, but may fail for complex data. We propose an adaptive weighted rank reduction method that selects optimal rank and minimizes residual errors. We tested the method on synthetic and real seismic data.
Article
Statistics & Probability
Wenxing Guo, Narayanaswamy Balakrishnan, Mengjie Bian
Summary: In this work, matrix projections are incorporated into reduced rank regression method to develop estimators for high-dimensional multivariate linear regression model. A consistent estimator for the rank of the coefficient matrix is proposed, and prediction performance bounds are achieved based on mean squared errors. Simulation studies and real data analysis demonstrate that the proposed methods are stable, have good prediction performance, and maintain rank consistency compared to existing methods.
ELECTRONIC JOURNAL OF STATISTICS
(2021)
Article
Agriculture, Dairy & Animal Science
M. Gu, G. Cosenza, G. Gaspa, M. Iannaccone, N. P. P. Macciotta, G. Chemello, L. Di Stasio, A. Pauciullo
JOURNAL OF DAIRY SCIENCE
(2020)
Article
Agriculture, Dairy & Animal Science
Alberto Cesarani, Giustino Gaspa, Yutaka Masuda, Lorenzo Degano, Daniele Vicario, Daniela A. L. Lourenco, Nicolo P. P. Macciotta
JOURNAL OF DAIRY SCIENCE
(2020)
Article
Agriculture, Dairy & Animal Science
Alberto Cesarani, Giustino Gaspa, Alfredo Pauciullo, Lorenzo Degano, Daniele Vicario, Nicolo P. P. Macciotta
Summary: The study of Runs of Homozygosity (ROH) is a useful approach for the characterization of the genome of livestock populations. The research identified a mixed genetic background in the 5 European Simmental populations, with the possible presence of three subgroups. Additionally, a strong relationship between autozygosity and production traits has been detected.
JOURNAL OF ANIMAL BREEDING AND GENETICS
(2021)
Article
Agriculture, Dairy & Animal Science
A. Cesarani, A. Garcia, J. Hidalgo, L. Degano, D. Vicario, N. P. P. Macciotta, D. Lourenco
Summary: The study tested the feasibility and advantages of genomic evaluation for milkability in the Italian Simmental population, showing that genomic information can improve the accuracy of breeding values.
JOURNAL OF DAIRY SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
F. Correddu, A. Cesarani, C. Dimauro, G. Gaspa, N. P. P. Macciotta
Summary: PCA and MFA have been used to analyze the milk FA profile of Sarda breed ewes, with both techniques identifying 9 latent variables explaining 80% of the total variance. MFA was able to identify a clear structure for all extracted latent variables, while PCA structures were more difficult to interpret. The milk FA metabolism pathways were identified, with physiological factors such as days in milk, parity, and lambing month affecting the new variables.
JOURNAL OF DAIRY SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
Laura Falchi, Giustino Gaspa, Alberto Cesarani, Fabio Correddu, Lorenzo Degano, Daniele Vicario, Daniela Lourenco, Nicolo P. P. Macciotta
Summary: The study demonstrated the advantages of using genomic information in calculating breeding values and investigating genetic correlations, showing low heritabilities for BHB using both pedigree-based and genomic approaches, with variations in genetic correlations with milk traits. Genomic EBV showed higher accuracy in predicting cow breeding values compared to EBV, with genomic information providing greater predictive ability. Additionally, a genome-wide association study revealed significant markers on BTA20 linked to genes associated with BHB milk content, highlighting the importance of genomic data in improving breeding strategies.
JOURNAL OF ANIMAL BREEDING AND GENETICS
(2021)
Article
Agriculture, Dairy & Animal Science
Alberto Cesarani, Giustino Gaspa, Fabio Correddu, Corrado Dimauro, Nicolo P. P. Macciotta
Summary: The study analyzed the distribution and features of ROH in 823 Sarda breed ewes farmed at different altitudes, revealing significant effects of altitude and temperature on homozygosity patterns. Differences in ROH distribution and features among sheep farmed at different altitudes were highlighted, confirming the role of environmental adaptability in shaping the genome of this breed.
JOURNAL OF ANIMAL BREEDING AND GENETICS
(2022)
Article
Agriculture, Dairy & Animal Science
F. Correddu, G. Gaspa, A. Cesarani, N. P. P. Macciotta
Summary: Milk coagulation ability is crucial for the sheep dairy industry. This study investigates the causes of noncoagulation of sheep milk and explores the effect of milk physicochemical properties and genetic background on milk coagulation status. The results highlight the importance of protein and chloride content, as well as the somatic cell score, in determining the coagulation status of sheep milk.
JOURNAL OF DAIRY SCIENCE
(2022)
Article
Agriculture, Dairy & Animal Science
Alberto Cesarani, Salvatore Mastrangelo, Michele Congiu, Baldassare Portolano, Giustino Gaspa, Marco Tolone, Nicolo P. P. Macciotta
Summary: This study evaluated the levels of inbreeding in Sarda and Valle del Belice dairy sheep breeds and their impact on milk production traits. The results showed that higher inbreeding coefficients had a negative effect on milk yield.
JOURNAL OF ANIMAL BREEDING AND GENETICS
(2023)
Article
Agriculture, Dairy & Animal Science
Nicolo P. P. Macciotta, Corrado Dimauro, Lorenzo Degano, Daniele Vicario, Alberto Cesarani
Summary: Heat stress during pregnancy can lead to physiological and metabolic changes in the offspring of animals, as a result of epigenetic reprogramming of the genome. This study investigated the transgenerational effects of heat stress in Italian Simmental cows. The birth months of the dam and granddam, as well as the temperature-humidity index during pregnancy, were found to affect breeding values for dairy traits in the daughters and granddaughters. The findings suggest an epigenetic inheritance due to environmental stressors in Italian Simmental cattle.
JOURNAL OF DAIRY SCIENCE
(2023)
Article
Agriculture, Dairy & Animal Science
S. Carta, A. Cesarani, F. Correddu, N. P. P. Macciotta
Summary: This study investigated the factors affecting lactose content in sheep milk and found that it is influenced by several factors, suggesting the possibility of incorporating lactose content into breeding programs.
JOURNAL OF DAIRY SCIENCE
(2023)
Article
Agriculture, Dairy & Animal Science
F. Correddu, S. Carta, M. Congiu, A. Cesarani, C. Dimauro, N. P. P. Macciotta
Summary: Individual methane emissions are considered as a potential breeding goal to improve the sustainability of ruminant farming systems. However, the high costs and logistics of large-scale recording of individual methane emissions pose difficulties. This study aimed to estimate the methane yield and intensity of dairy sheep using equations developed for dairy cattle and to evaluate the factors affecting these measurements. The results provide valuable insights into the phenotypic and genetic background of methane emissions in Sarda dairy sheep.
ITALIAN JOURNAL OF ANIMAL SCIENCE
(2023)
Article
Agriculture, Dairy & Animal Science
Alberto Cesarani, Francesca Corte Pause, Jorge Hidalgo, Andre Garcia, Lorenzo Degano, Daniele Vicario, Nicolo P. P. Macciota, Giuseppe Stradaioli
Summary: This study aimed to estimate genetic parameters and investigate the genomic background of scrotal circumference and semen parameters in Italian Simmental bulls. The heritabilities ranged from 0.07 to 0.50, and a total of 13 SNP were significantly associated with the traits. Genes already associated with reproduction parameters were found near the significant SNP. The results provided preliminary insights into the genetic determinism of semen quality in Italian Simmental bulls.
ITALIAN JOURNAL OF ANIMAL SCIENCE
(2023)
Article
Agriculture, Dairy & Animal Science
E. Manca, A. Cesarani, L. Falchi, A. S. Atzori, G. Gaspa, A. Rossoni, N. P. P. Macciotta, C. Dimauro
Summary: A new index, residual concentrate intake (RCI), was defined to measure individual efficiency in converting concentrate into animal products. By combining multivariate and Bayesian techniques, SNPs associated with RCI were identified, with genes related to feed efficiency and RFI being discovered.
ITALIAN JOURNAL OF ANIMAL SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
Alberto Cesarani, Stefano Biffani, Andre Garcia, Daniela Lourenco, Giacomo Bertolini, Gianluca Neglia, Ignacy Misztal, Nicolo Pietro Paolo Macciotta
Summary: This study aimed to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, analyzing genotyped animals and milk production records. Including genotypes of females can improve breeding values accuracy in the Italian buffalo.
ITALIAN JOURNAL OF ANIMAL SCIENCE
(2021)