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Consequently, expression 2 may yield biased variance estimates of adapted multivariable associations.
(As for the simple 2 × 2 table, M - 1 is often replaced by M in most derivations, although this leads to a very slightly biased variance estimate).
ML gives biased variance component estimates and the estimates are also sensitive to influential observations with high leverages (V erbyla 1993).
This implies that standard regression methods for exposure assessment that assume independence are not valid and would be expected to yield biased variance of parameter estimates and inefficient significance tests.
(The traditional test based on Σ(O - E)/ E over all four cells of the table is equivalent to the above but with a slightly biased variance estimate in which (N - 1) is replaced by N).
The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits.
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This may have biased genetic variance estimates in some studies.
Furthermore, Hastie et al.[4] show the interplay between bias, variance and model complexity in detail.
For moderate SNR, the TFSD represents a better compromise in terms of bias, variance and computational time than the CWV.
The probability estimation property of the mean square solution, as well as the bias variance dilemma, is briefly mentioned.
A criterion is used where normalized bias, variance, and mean square error is optimized for the predefined peaked spectrum.
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