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For convenience (see remark below), e m is substituted by two non-negative decision variables, ε1 and μ1: (19) These decision variables δ = { ε1, μ1} relax the basic assertion = v m, conforming a possibility distribution in (, v m ) associated to some cost index J m.
However, this assertion is a little vague since, on the one hand, it is not clear whether ({x_{n}}) belongs to (S K)) or to ((I-T)(K)); on the other hand, it is also not clear where ((I-T ^{-1}) I-T ^{-1}uous, I-T ^{-1}ks 2.1 and 2.2 below.
See remark 2 (section 5).
See Remark 6. ii).
On one hand, it seems that general logistic regression tended to detect DIF well in most cases (see remark on surprising results below).
Corollary 1.6 (see Remark 3.7 below).
Condition (13) is actually fulfilled in all cases to be treated in this work (see Remark 3 below).
We remark that the above proposition gives sufficient conditions for the boundedness, but they are not necessary in general, see Remark 6.5 below.
We remark that is also possible to quantify the blow-up of the constant c in (33) as α → 0 ; see Remark 7 below.
These theorems are the nonlinear analogs of classical results about fine properties of solutions to linear equations, which are usually derived via linear potential; see Remark 3 below.
We continue by giving two such examples, where the first one (Bennett's inequalities) also has direct applications, e.g., to interpolation theory (see Remark 1.1 below).
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