Sentence examples for matrix of regression from inspiring English sources

We study the distribution of singular values for the matrix of regression coefficients and for the matrix of predicted responses.

The structural part of the LR model is characterized by a matrix of regression coefficients, (varGamma), and a variance covariance matrix, (sum).

In this paper we consider the problem of estimating the matrix of regression coefficients in a multivariate linear regression model in which the design matrix is near singular.

We consider a multivariate linear response regression in which the number of responses and predictors is large and comparable with the number of observations, and the rank of the matrix of regression coefficients is assumed to be small.

It takes the uncertainty into consideration by including posterior dosage/probability to the design matrix of regression analysis (linear analysis for quantitative trait and logistic regression for qualitative trait).

Let β = (β1,..., β M ) be the d × M matrix of regression coefficients.

Appropriate fixed matrices of regression coefficients, B, contrast matrices, C and U, and Θ0 were chosen to test a within-subject interaction for a test size, α, of 0.05.

The software is implemented in the R and C languages and consists of the following two simple steps: (a) find MLEs of the regression parameter and the cumulative hazard function; (b) compute the variance covariance matrix of the regression parameter estimator by using the generalized missing information principle (GMIP) developed by Kim [Kim, J.S., 2003b.

One classical design criterion is to minimize the determinant of the covariance matrix of the regression estimates, and the designs are called D-optimal designs.

For this, the correlation matrix of logistic regression was applied and this showed that most of the correlations between factors were either absent or very insignificant suggesting that all landslide factors are independent.

We used a sandwich estimator for the estimation of the covariance matrix of the regression coefficients.