Relevant bibliographies by topics / Matrix linear regression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Matrix linear regression'

Author: Grafiati
Published: 31 August 2024
Last updated: 31 July 2025

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Journal articles on the topic "Matrix linear regression"

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Lutay, V. N., and N. S. Khusainov. "The selective regularization of a linear regression model." Journal of Physics: Conference Series 2099, no. 1 (2021): 012024. http://dx.doi.org/10.1088/1742-6596/2099/1/012024.

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Abstract This paper discusses constructing a linear regression model with regularization of the system matrix of normal equations. In contrast to the conventional ridge regression, where positive parameters are added to all diagonal terms of a matrix, in the method proposed only those matrix diagonal entries that correspond to the data with a high correlation are increased. This leads to a decrease in the matrix conditioning and, therefore, to a decrease in the corresponding coefficients of the regression equation. The selection of the entries to be increased is based on the triangular decompo
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Nakonechnyi, Alexander G., Grigoriy I. Kudin, Petr N. Zinko, and Taras P. Zinko. "Perturbation Method in Problems of Linear Matrix Regression." Journal of Automation and Information Sciences 52, no. 1 (2020): 1–12. http://dx.doi.org/10.1615/jautomatinfscien.v52.i1.10.

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Zhang, Jiawei, Peng Wang, and Ning Zhang. "Distribution Network Admittance Matrix Estimation With Linear Regression." IEEE Transactions on Power Systems 36, no. 5 (2021): 4896–99. http://dx.doi.org/10.1109/tpwrs.2021.3090250.

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Ivashnev, L. I. "Methods of linear multiple regression in a matrix form." Izvestiya MGTU MAMI 9, no. 4-4 (2015): 35–41. http://dx.doi.org/10.17816/2074-0530-67011.

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The article contains a summary of three basic and two weighted linear multiple regression tech- niques in matrix form, together with the method of least squares of Gauss constitute a new tool re- gression analysis. The article contains a matrix formula that can be used to obtain equations of line- ar multiple regression and the basic weighted least-squares method to obtain regression equations without constant term and the method of obtaining the regression equations of general form. The article provides an example of use of matrix methods to obtain the coefficients of regression equa- tion of
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Mahaboob, B., J. P. Praveen, B. V. A. Rao, Y. Harnath, C. Narayana, and G. B. Prakash. "A STUDY ON MULTIPLE LINEAR REGRESSION USING MATRIX CALCULUS." Advances in Mathematics: Scientific Journal 9, no. 7 (2020): 4863–72. http://dx.doi.org/10.37418/amsj.9.7.52.

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Aubin, Elisete da Conceição Q., and Gauss M. Cordeiro. "BIAS in linear regression models with unknown covariance matrix." Communications in Statistics - Simulation and Computation 26, no. 3 (1997): 813–28. http://dx.doi.org/10.1080/03610919708813413.

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Bargiela, Andrzej, and Joanna K. Hartley. "Orthogonal linear regression algorithm based on augmented matrix formulation." Computers & Operations Research 20, no. 8 (1993): 829–36. http://dx.doi.org/10.1016/0305-0548(93)90104-q.

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Mikusheva, Anna, and Mikkel Sølvsten. "Linear regression with weak exogeneity." Quantitative Economics 16, no. 2 (2025): 367–403. https://doi.org/10.3982/qe2622.

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This paper studies linear time‐series regressions with many regressors. Weak exogeneity is the most used identifying assumption in time series. Weak exogeneity requires the structural error to have zero conditional expectation given present and past regressor values, allowing errors to correlate with future regressor realizations. We show that weak exogeneity in time‐series regressions with many controls may produce substantial biases and render the least squares (OLS) estimator inconsistent. The bias arises in settings with many regressors because the normalized OLS design matrix remains asym
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Livadiotis, George. "Linear Regression with Optimal Rotation." Stats 2, no. 4 (2019): 416–25. http://dx.doi.org/10.3390/stats2040028.

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The paper shows how the linear regression depends on the selection of the reference frame. The slope of the fitted line and the corresponding Pearson’s correlation coefficient are expressed in terms of the rotation angle. The correlation coefficient is found to be maximized for a certain optimal angle, for which the slope attains a special optimal value. The optimal angle, the value of the optimal slope, and the corresponding maximum correlation coefficient were expressed in terms of the covariance matrix, but also in terms of the values of the slope, derived from the fitting at the nonrotated
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Klen, Kateryna, Vadym Martynyuk, and Mykhailo Yaremenko. "Prediction of the wind speed change function by linear regression method." Computational Problems of Electrical Engineering 9, no. 2 (2019): 28–33. http://dx.doi.org/10.23939/jcpee2019.02.028.

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In the article the approximation of the function of wind speed changes by linear functions based on Walsh functions and the prediction of function values by linear regression method is made. It is shown that under the condition of a linear change of the internal resistance of the wind generator over time, it is advisable to introduce the wind speed change function with linear approximation. The system of orthonormal linear functions based on Walsh functions is given. As an example, the approximation of the linear-increasing function with a system of 4, 8 and 16 linear functions based on the Wa
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Dissertations / Theses on the topic "Matrix linear regression"

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Kuljus, Kristi. "Rank Estimation in Elliptical Models : Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression." Doctoral thesis, Uppsala universitet, Matematisk statistik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9305.

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This thesis deals with univariate and multivariate rank methods in making statistical inference. It is assumed that the underlying distributions belong to the class of elliptical distributions. The class of elliptical distributions is an extension of the normal distribution and includes distributions with both lighter and heavier tails than the normal distribution. In the first part of the thesis the rank covariance matrices defined via the Oja median are considered. The Oja rank covariance matrix has two important properties: it is affine equivariant and it is proportional to the inverse of t
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Shrewsbury, John Stephen. "Calibration of trip distribution by generalised linear models." Thesis, University of Canterbury. Department of Civil and Natuaral Resources Engineering, 2012. http://hdl.handle.net/10092/7685.

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Generalised linear models (GLMs) provide a flexible and sound basis for calibrating gravity models for trip distribution, for a wide range of deterrence functions (from steps to splines), with K factors and geographic segmentation. The Tanner function fitted Wellington Transport Strategy Model data as well as more complex functions and was insensitive to the formulation of intrazonal and external costs. Weighting from variable expansion factors and interpretation of the deviance under sparsity are addressed. An observed trip matrix is disaggregated and fitted at the household, person and trip
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Wang, Shuo. "An Improved Meta-analysis for Analyzing Cylindrical-type Time Series Data with Applications to Forecasting Problem in Environmental Study." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/386.

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This thesis provides a case study on how the wind direction plays an important role in the amount of rainfall, in the village of Somi$acute{o}$. The primary goal is to illustrate how a meta-analysis, together with circular data analytic methods, helps in analyzing certain environmental issues. The existing GLS meta-analysis combines the merits of usual meta-analysis that yields a better precision and also accounts for covariance among coefficients. But, it is quite limited since information about the covariance among coefficients is not utilized. Hence, in my proposed meta-analysis, I take the
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Kim, Jingu. "Nonnegative matrix and tensor factorizations, least squares problems, and applications." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42909.

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Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative value, and it provides a low-rank approximation formed by factors whose elements are also nonnegative. The nonnegativity constraints imposed on the low-rank factors not only enable natural interpretation but also reveal the hidden structure of data. Extending the benefits of NMF to multidimensional arrays, nonnegative tensor factorization (NTF) has been shown to be successful in
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Nasseri, Sahand. "Application of an Improved Transition Probability Matrix Based Crack Rating Prediction Methodology in Florida’s Highway Network." Scholar Commons, 2008. https://scholarcommons.usf.edu/etd/424.

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With the growing need to maintain roadway systems for provision of safety and comfort for travelers, network level decision-making becomes more vital than ever. In order to keep pace with this fast evolving trend, highway authorities must maintain extremely effective databases to keep track of their highway maintenance needs. Florida Department of Transportation (FDOT), as a leader in transportation innovations in the U.S., maintains Pavement Condition Survey (PCS) database of cracking, rutting, and ride information that are updated annually. Crack rating is an important parameter used by FDOT
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Кір’ян, М. П. "Веб-система загальноосвітноьої школи з використанням алгоритму оцінювання та збору статистики". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/76750.

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Виконано дослідження предметної області, проведений аналіз існуючих рішень, здійснений вибір методів рішення поставленої задачі. Розроблена зовнішня та внутрішня структура веб- додатку. Розроблено веб-систему з використанням алгоритму оцінювання та збору статистики на базі загальноосвітньої школи. Реалізовано алгоритм з використанням Баєсової лінійної регресії, оскільки на невеликій кількості даних він надає достатньо високу точність. Розроблено систему з використанням новітніх технологій, які дозволять легко підтримувати та оновлювати систему у майбутньому.
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Bettache, Nayel. "Matrix-valued Time Series in High Dimension." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAG002.

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L'objectif de cette thèse est de modéliser des séries temporelles à valeurs matricielles dans un cadre de grande dimension. Pour ce faire, la totalité de l'étude est présentée dans un cadre non asymptotique. Nous fournissons d'abord une procédure de test capable de distinguer dans le cas de vecteurs ayant une loi centrée stationnaire si leur matrice de covariance est égale à l'identité ou si elle possède une structure de Toeplitz sparse. Dans un second temps, nous proposons une extension de la régression linéaire matricielle de faible rang à une régression à deux paramètres matriciels qui crée
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Žiupsnys, Giedrius. "Klientų duomenų valdymas bankininkystėje." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20110709_152442-86545.

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Darbas apima banko klientų kredito istorinių duomenų dėsningumų tyrimą. Pirmiausia nagrinėjamos banko duomenų saugyklos, siekiant kuo geriau perprasti bankinius duomenis. Vėliau naudojant banko duomenų imtis, kurios apima kreditų grąžinimo istoriją, siekiama įvertinti klientų nemokumo riziką. Tai atliekama adaptuojant algoritmus bei programinę įrangą duomenų tyrimui, kuris pradedamas nuo informacijos apdorojimo ir paruošimo. Paskui pritaikant įvairius klasifikavimo algoritmus, sudarinėjami modeliai, kuriais siekiama kuo tiksliau suskirstyti turimus duomenis, nustatant nemokius klientus. Taip p
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NÓBREGA, Caio Santos Bezerra. "Uma estratégia para predição da taxa de aprendizagem do gradiente descendente para aceleração da fatoração de matrizes." Universidade Federal de Campina Grande, 2014. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/362.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-04-11T14:50:08Z No. of bitstreams: 1 CAIO SANTOS BEZERRA NÓBREGA - DISSERTAÇÃO PPGCC 2014..pdf: 983246 bytes, checksum: 5eca7651706ce317dc514ec2f1aa10c3 (MD5)<br>Made available in DSpace on 2018-04-11T14:50:08Z (GMT). No. of bitstreams: 1 CAIO SANTOS BEZERRA NÓBREGA - DISSERTAÇÃO PPGCC 2014..pdf: 983246 bytes, checksum: 5eca7651706ce317dc514ec2f1aa10c3 (MD5) Previous issue date: 2014-07-30<br>Capes<br>Sugerir os produtos mais apropriados aos diversos tipos de consumidores não é uma tarefa trivial, apesar de ser um fator
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Cavalcanti, Alexsandro Bezerra. "Aperfeiçoamento de métodos estatísticos em modelos de regressão da família exponencial." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05082009-170043/.

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Neste trabalho, desenvolvemos três tópicos relacionados a modelos de regressão da família exponencial. No primeiro tópico, obtivemos a matriz de covariância assintótica de ordem $n^$, onde $n$ é o tamanho da amostra, dos estimadores de máxima verossimilhança corrigidos pelo viés de ordem $n^$ em modelos lineares generalizados, considerando o parâmetro de precisão conhecido. No segundo tópico calculamos o coeficiente de assimetria assintótico de ordem n^{-1/2} para a distribuição dos estimadores de máxima verossimilhança dos parâmetros que modelam a média e dos parâmetros de precisão e dispers
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Books on the topic "Matrix linear regression"

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Puntanen, Simo, George P. H. Styan, and Jarkko Isotalo. Formulas Useful for Linear Regression Analysis and Related Matrix Theory. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32931-9.

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Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Springer Berlin Heidelberg, 2012.

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Formulas Useful For Linear Regression Analysis And Related Matrix Theory Its Only Formulas But We Like Them. Springer, 2012.

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Formulas Useful for Linear Regression Analysis and Related Matrix Theory: It's Only Formulas but We Like Them. Springer London, Limited, 2013.

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Optimization of Objective Functions: Analytics. Numerical Methods. Design of Experiments. Fizmatlit Publisher, 2009.

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Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.

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Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, m
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Marques, Marcia Alessandra Arantes, ed. Estudos Avançados em Ciências Agrárias. Bookerfield Editora, 2022. http://dx.doi.org/10.53268/bkf22040700.

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Esta obra tem por objetivo apresentar produções acadêmicas que possuem em comum a grande área Ciências Agrárias. Permeando por este vasto tema, nas próximas páginas serão apresentados trabalhos que abordam sobre Ciência e Tecnologia de Alimentos, Engenharia Agrícola, bem como na Ciência Animal. Desta forma, para melhor direcionar o fluxo da leitura, o livro está dividido em capítulos, nos quais os primeiros apresentados abordam o tema “Ciência e Tecnologia em Alimentos” e apresenta trabalhos desenvolvidos com ênfase em controle de qualidade, aproveitamento de subprodutos e planejamento experim
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Book chapters on the topic "Matrix linear regression"

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Groß, Jürgen. "Matrix Algebra." In Linear Regression. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1_7.

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Groß, Jürgen. "The Covariance Matrix of the Error Vector." In Linear Regression. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1_5.

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von Frese, Ralph R. B. "Matrix Linear Regression." In Basic Environmental Data Analysis for Scientists and Engineers. CRC Press, 2019. http://dx.doi.org/10.1201/9780429291210-7.

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Brown, Jonathon D. "Simple Linear Regression." In Linear Models in Matrix Form. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_2.

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Brown, Jonathon D. "Polynomial Regression." In Linear Models in Matrix Form. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_10.

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Brown, Jonathon D. "Multiple Regression." In Linear Models in Matrix Form. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_4.

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Lange, Kenneth. "Linear Regression and Matrix Inversion." In Numerical Analysis for Statisticians. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5945-4_7.

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Dinov, Ivo D. "Linear Algebra, Matrix Computing, and Regression Modeling." In The Springer Series in Applied Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17483-4_3.

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Hines, Benjamin, Yuriy Kuleshov, and Guoqi Qian. "Spatial Modelling of Linear Regression Coefficients for Gauge Measurements Against Satellite Estimates." In 2019-20 MATRIX Annals. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62497-2_11.

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Puntanen, Simo, Jarkko Isotalo, and George P. H. Styan. "Formulas Useful for Linear Regression Analysis and Related Matrix Theory." In Formulas Useful for Linear Regression Analysis and Related Matrix Theory. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32931-9_1.

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Conference papers on the topic "Matrix linear regression"

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Chen, Xiaojun, Guowen Yuan, Feiping Nie, and Joshua Zhexue Huang. "Semi-supervised Feature Selection via Rescaled Linear Regression." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/211.

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With the rapid increase of complex and high-dimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods cannot find both global and sparse solution of the projection matrix. In this paper, we propose a novel semi-supervised feature selection method which can learn both global and sparse solution of the project
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Chou, Wu. "Maximum a posterior linear regression with elliptically symmetric matrix variate priors." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-4.

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Dufrenois, F., and J. C. Noyer. "Discriminative Hat Matrix: A new tool for outlier identification and linear regression." In 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033300.

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Youwen Zhu, Zhikuan Wang, Cheng Qian, and Jian Wang. "On efficiently harnessing cloud to securely solve linear regression and other matrix operations." In 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS). IEEE, 2016. http://dx.doi.org/10.1109/iwqos.2016.7590402.

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Krishna, Y. Hari, G. V. Arunamayi, K. Ramesh Babu, S. Nanda Kishore, M. Rajaiah, and B. Mahaboob. "Several matrix algebra applications in linear regression analysis, information theory, ODE and geometry." In FOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN PHYSICAL SCIENCES AND MATERIALS: ICAPSM 2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0216119.

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Jing, Wang, Zhou Huizhi, Liu Dichen, Guo Ke, and Han Xiangyu. "Research on real-time admittance matrix identification based on WAMS and multiple linear regression." In 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2014. http://dx.doi.org/10.1109/appeec.2014.7066186.

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Lopez, Oscar, Daniel Dunlavy, and Richard Lehoucq. "Zero-Truncated Poisson Regression for Multiway Count Data." In Proposed for presentation at the Conference on Random Matrix Theory and Numerical Linear Algebra held June 20-24, 2022 in Seattle, WA. US DOE, 2022. http://dx.doi.org/10.2172/2003556.

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Thiyagarajan, A., and K. Anbazhagan. "Confusion matrix analysis of personal loan fraud detection using novel random forest algorithm and linear regression algorithm." In INTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0173705.

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Thiyagarajan, A., and S. Parthiban. "Confusion matrix analysis of personal loan fraud detection using novel K means algorithm and linear regression algorithm." In 5TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES (ICMS5). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0227923.

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Belvis, Carlo V., Claire Ann R. Javierto, Ma Bernadette O. Santos, Crizelle R. Datinggaling, and Christian Van R. Rivera. "Enhancing Breast Cancer Detection through Machine Learning: A Comparative Analysis of Logistic Regression and Linear Discriminant Analysis." In SRC. University of Bahrain, 2023. https://doi.org/10.12785/src/1570967447.

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This paper intends to analyze classification models that can predict whether breast cancer is cancerous or noncancerous through the attributes of the selected dataset with the application of machine learning algorithms. The high dimensional Breast Cancer Wisconsin (Diagnostic) Dataset is reduced through principal component analysis (PCA). Six major components were extracted with a cumulative variance of 91.063%. The selected classifier models that were evaluated were logistic regression and linear discriminant analysis (LDA). After training and testing, the model’s performance was evaluated us
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Reports on the topic "Matrix linear regression"

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Balk, Ethan, Elyse Couch, Htun Ja Mai, et al. Fiber Intake and Laxation in People With Normal Bowel Function. AHRQ, 2025. https://doi.org/10.23970/ahrqepcsrlaxation.

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Objectives. To support a planned update of the Dietary Recommended Intake of macronutrients, including dietary fiber, we conducted a systematic review of the effect of fiber on laxation outcomes (related to gut motility) in people with normal bowel function. Data sources and review methods. We searched PubMed, Embase, and existing systematic reviews to March 4, 2025 for randomized trials in the general population of people in any life stage (without bowel dysfunction) that compared amounts, types, or forms of fiber. Outcomes of interest were stool consistency, fecal weight, fecal frequency, gu
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Castellano, Mike J., Abraham G. Shaviv, Raphael Linker, and Matt Liebman. Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile soil organic matter fractions. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597926.bard.

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A major goal in Israeli and U.S. agroecosystems is to maximize nitrogen availability to crops while minimizing nitrogen losses to air and water resources. This goal has presented a significant challenge to global agronomists and scientists because crops require large inputs of nitrogen (N) fertilizer to maximize yield, but N fertilizers are easily lost to surrounding ecosystems where they contribute to water pollution and greenhouse gas concentrations. Determination of the optimum N fertilizer input is complex because the amount of N produced from soil organic matter varies with time, space an
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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Abstract:
The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the proj
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