Results 241 to 250 of about 1,578,829 (286)

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.
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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 \(X\).
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Multivariate Regression Modeling

Journal of Solar Energy Engineering, 1998
An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M)
S. Katipamula, T. A. Reddy, D. E. Claridge   +2 more
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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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Multivariate Nonlinear Regression

2007
This chapter continues the previous chapter but now we can have two or more independent (predictor, explanatory) variables, the \(\overline{X}_i\). Another change from Chapter 12 is now we will work with fuzzy trapezoidal (shaped) fuzzy numbers instead of fuzzy triangular (shaped) fuzzy numbers.
James J. Buckley, Leonard J. Jowers
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Multivariate Linear Regression

2010
In this chapter we consider the multivariate multiple regression problem. The tests and estimates are again based on identity, spatial sign, and spatial rank scores. The estimates obtained in this way are then the regular LS estimate, the LAD estimate based on the mean deviation of the residuals (from the origin) and the estimate based on mean ...
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Multivariable Linear Regression

2015
INEAR REGRESSION is probably one of the most powerful and useful tools available to the applied statistician. This method uses one or more variables to explain the values of another. Statistics alone cannot prove a cause and effect relationship, but we can show how changes in one set of measurements are associated with changes of the average values in ...
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Multivariate Linear Regression

1998
Regression methods are perhaps the most widely used statistical tools in data analysis. When several response variables are studied simultaneously, we are in the sphere of multivariate regression. The usual description of the multivariate regression model, that relates the set of m multiple responses to a set of n predictor variables, assumes ...
Gregory C. Reinsel, Raja P. Velu
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Multivariate Regression Analysis

2018
This final chapter provides an introduction into multivariate regression modeling. We will cover the logic behind multiple regression modeling and explain the interpretation of a multivariate regression model. We will further cover the assumptions this type of model is based upon. Finally, and using our data, we will provide concrete examples on how to
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
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