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- Study guides, Class notes & Summaries
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1. Problem Setup
In linear multivariate regression, we have the following model:
y
T
i = x
T
i B + ǫ, i = 1, . . . , n, (1)
where yi ∈ R
q×1
is the response vector (q > 1), xi ∈ R
p×1
is the predictor vector, B ∈ R
p×q
is the
coefficient matrix, and ǫi ∈ R
q×1
represents the noise with i.i.d. elements that are independent of
xi
. In this paper, we consider the following extension of this problem:
y
T
i = Ui(x
T
i B + ǫ
T
i
), i = 1, . . . , n, (2)
whe...
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