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Regression relations are allowed to vary with time through smooth varying coefficient functions.
The proposed method of using varying coefficient models (VCM) for analysis of surveillance data allows one variable to vary by another therefore creating coefficients that vary with the modifying variable, which can be time.
These equations involve a periodically varying coefficient.
Terms affecting the first order parameter B are described by the varying coefficient terms f 1....f nb.
Therefore, when fitting a varying coefficient model for the age effect on B, it should be monotone increasing.
The main goal of the proposal is to detect deviations from a concurrent varying coefficient model potentially in a subset of the longitudinal predictors with nonzero estimated lags.
Similar(25)
In varying-coefficient models, an important question is to determine whether some of the varying coefficients are actually invariant coefficients.
A new kind of recurrent neural network is presented for solving the Lyapunov equation with time-varying coefficient matrices.
In this paper, we first consider a time-varying coefficient time series model with potential time-varying endogeneity.
A novel representation of nonlinear input function, that is, a linear input with bounded time-varying coefficient, is firstly established.
The system under consideration is subjected to time-varying coefficient uncertainties with bounded covariances and noises with inexplicit statistical characteristics.
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