Term | Description |
---|---|
θ* | the final iteration |
xn | vector of values for the predictors at the nth observation |
v0 | gradient matrix = ( ∂f(xn, θ) / ∂θp ), the P by 1 vector of partial derivatives of f(x0, θ), evaluated at θ* |
Term | Description |
---|---|
tα/2 | upper α/2 point of the t distribution with N – P degrees of freedom |
se fit | standard error of the fit |
n | nth observation |
N | total number of observations |
P | number of free (unlocked) parameters |
fitted value | |
b | (R')-1v0 |
R | the (upper triangular) R matrix from the QR decomposition of Vi for the final iteration |
v0 | gradient matrix = ( ∂f(xn, θ) / ∂θp), the P by 1 vector of partial derivatives of f(x0, θ), evaluated at θ* |
S |
Term | Description |
---|---|
tα/2 | upper α/2 point of the t distribution with N – P degrees of freedom |
se fit | standard error of the fit |
n | nth observation |
N | total number of observations |
P | number of free (unlocked) parameters |
fitted value | |
b | (R')-1v0 |
R | the (upper triangular) R matrix from the QR decomposition of Vi for the final iteration |
v0 | gradient matrix = ( ∂f(xn, θ) / ∂θp), the P by 1 vector of partial derivatives of f(x0, θ), evaluated at θ* |
S |
Term | Description |
---|---|
n | nth observation |
N | total number of observations |
P | number of free (unlocked) parameters |
x0 | vector of values for the predictors |
f(x0, θ*) | |
v0 | gradient matrix = ( ∂f(xn, θ) / ∂θp), the P by 1 vector of partial derivatives of f(x0, θ), evaluated at θ* |
S |