The key novelty in our proposed BIC is that the training matrix [T.sub.S] is postmultiplied by the specific precoding matrix [P.sub.i] ; i.e., [X.sub.i] = [T.sub.S][P.sub.i], i = 1,2 , which means that each

row vector [x.sup.T.sub.i] (n) of [X.sub.i] is the linear combination of the

row vectors of [P.sub.i].

where [X.sub.u] is a scalar representing unobservable variables that are potentially correlated with prenatal smoking (e.g., general "health mindedness" of the mother), e is the regression error term, [X.sub.0] = [PARITY WHITE MALE] is a

row vector of regressors that are uncorrelated with [X.sub.u], and e, and the [[beta].sub.e] are the regression parameters.

1) Create N x d data matrix with one

row vector [x.sub.n] per data input

Firstly, we construct the comparison matrix of a

row vector r = ([r.sub.1], [r.sub.2],..., [r.sub.N]) [member of] [R.sup.N] and define the comparison matrix [C.sub.r] = [([c.sub.ij]).sub.NxN] of r by

represents the system variable, and the

row vector represents sampled data at a sampling instant.

x [F.sub.m+1] [right arrow] R, where [F.sub.1] denotes the feature vector (i.e., location information) in the target domain, [F.sub.i] (2 [less than or equal to] i [less than or equal to] m + 1) denotes the feature vector (i.e., the corresponding

row vector) in the (i - 1)-th auxiliary domain, and R denotes the rating value.

where y is the

row vector of observed variable, x stands for multiplication, x is the

row vector of latent variables, and E is the isotropic error term [13].

(iv) [x.sub.s][n] is a

row vector that stores the signal samples of the surveillance antenna channels at the time instant n, [x.sub.s][n] [member of] [C.sup.1xM].

The term ([mathematical expression not reproducible], is the ith column or

row vector of the weighted square root of the scaled covariance matrix ([n.sub.a] + [lambda]) [P.sup.a.sub.k].

Each

row vector represents a fracture that contains the four variables, and each column vector represents one of the variables that contains m (or n) rows.

where a column vector [d.sub..j] and a

row vector [d.sub.i.] have appropriate sizes.

The term energy indicates the norm of a

row vector in X corresponding to the realization in D.