Results 261 to 270 of about 3,495,066 (309)

Resistant Starch Dynamics in Whole‐Grain Cereals: How Processing and Macromolecular Interactions Shape Quality and Physiological Responses

AgriFood: Journal of Agricultural Products for Food, EarlyView.
This review explores how processing and macromolecular interactions shape resistant starch dynamics in whole‐grain cereals. It highlights how these transformations influence nutritional quality, digestion kinetics, and product properties, offering insights for designing stable health‐oriented whole‐grain foods with optimized functional benefits ...
Yuewen Tan, Wenjie Zhu, Jingxin Li, Yingchao Wang, Ruge Cao   +4 more
wiley   +1 more source

Vendor Types, Attendance, Experience and Sales 2019–2021: Evidence From Five Rural Oregon Farmers Markets

Agribusiness, EarlyView.
ABSTRACT Farmers markets provide a direct‐to‐consumer marketing path for farmers and small businesses, facilitating customer discovery and product refinement. This paper explores farmers markets as a business incubator, with a focus on beginning vendors and resilience to a shock, namely, COVID‐19 market restrictions.
Mallory L. Rahe, Juo‐Han Tsay, Sarah A. Low   +2 more
wiley   +1 more source

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Multivariate Multilinear Regression

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012
Conventional regression methods, such as multivariate linear regression (MLR) and its extension principal component regression (PCR), deal well with the situations that the data are of the form of low-dimensional vector. When the dimension grows higher, it leads to the under sample problem (USP): the dimensionality of the feature space is much higher ...
Ya Su, Xinbo Gao 0001, Xuelong Li 0001, Dacheng Tao   +3 more
openaire   +4 more sources

Bayesian Multivariate Logistic Regression

Biometrics, 2004
SummaryBayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters.
O'Brien, Sean M., Dunson, David B.
openaire   +3 more sources

Calibrated Multivariate Regression Networks

IEEE Transactions on Circuits and Systems for Video Technology, 2020
In this paper, we propose a new multi-layer learning architecture, the calibrated multivariate regression network (CMRN). Compared to previous multivariate models, the CMRN is able to simultaneously handle major challenges in multivariate regression including highly nonlinear input-output relationships, underlying inter-output correlations and ...
Lei Zhang 0093, Ying-Jun Du, Xin Li 0100, Xiantong Zhen   +3 more
openaire   +1 more source

Estimation of Multivariate Regression

Theory of Probability & Its Applications, 2004
Summary: Let \((X,Y)\) be a random vector whose first component takes values in a measurable space \(({\mathfrak{X}},{\mathfrak{A}},\mu)\) with measure \(\mu\), and let \(Y\) be a real-valued random variable. Let \(f(x)={\mathbf E}\{Y\mid X=x\} \) be the regression function of \(Y\) on \(X\).
openaire   +2 more sources

Blocked Arteries and Multivariate Regression

Biometrics, 1992
Ultrasound blood flow waveforms may be used in the diagnosis of arterial occlusive disease in human legs. We develop a statistical model to predict disease severity, conditional on the ultrasound data and some training data. It belongs to the class of models known as seemingly unrelated regressions, for which the Bayesian predictive density function ...
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A Multivariate Version of Isotonic Regression

Biometrika, 1983
A multivariate generalization of isotonic regression is given enabling the study of statistical inference for ordered vector-valued parameters or sets of ordered parameters, including multivariate extensions of Bartholomew's \({\bar \chi}{}^ 2_ k\) and \(\bar E^ 2_ k\).
Sasabuchi, Syoichi, Inutsuka, M., Kulatunga, D. D. S.   +2 more
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Regression Analysis and Multivariate Analysis

Seminars in Reproductive Medicine, 1996
Proper evaluation of data does not necessarily require the use of advanced statistical methods; however, such advanced tools offer the researcher the freedom to evaluate more complex hypotheses. This overview of regression analysis and multivariate statistics describes general concepts. Basic definitions and conventions are reviewed.
A J, Duleba, D L, Olive
openaire   +2 more sources