where γ j is a vector of response specific parameters.
The linear model and its conditional mean function look as follows: Y i = β 0 + β 1 X i 1 + ϵ i E Y i X i = β 0 + β 1 X i 1 The notation above suggests that we observe a vector of response variables, Y1…Yn.
Consider a study having N matched sets, such that the j-th member (j = 1,..., n i ) of the i-th set (i = 1,..., N) has P associated continuous exposures X ij = (X ij 1,..., X ijp ) T. In addition, let Y i = (Y i 1,..., Y ini ) T be a vector of response variables associated to the i-th matched set, such that Y ij = 1 for the cases and Y ij = 0 for the controls.
(Pareto-efficient response plans) Let (vec {RP}^{d}) be a vector of proposed response plans, associated with a linearly scaled impact assessment of dimension d.
To estimate this discrete-time duration model, we construct a panel dataset such that the spell length of any given individual determines a vector of binary responses.
The UMA handles the measurements in the subject as a vector of multivariate responses and treats time points as levels of a qualitative factor with no order.
For example, if a large proportion of the sample endorses item A and only a small proportion endorses item B, it is consider inconsistentto have an individual who endorses item B, but not item A. If the items are ordinated from the most prevalent item to the least prevalent, a coherent vector of responses for a given individual is composed of 1s then of 0s, for example (1,1,1,0,0) or (1,0,0,0,0).
Let Y be a n×1 vector of responses which follows the linear model given by Y = 1 n β 0 + X β + e. (1).
Suppose that, the time-domain response of, is a vector of length, and ;, the time-domain response of, is a vector of length.
Each pixel is now represented as an 8D vector of responses at its (x, y) location.
Suppose that, the time-domain impulse response of, is a vector of length, and, the time-domain impulse response of, is a vector of length.
