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Munk, Bissantz, T. Wagner, G. Freitag, On difference based variance estimation in nonparametric regression when the covariate is high dimensional, J. R. Stat.
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A variety of variance estimation techniques were used by the included studies; but details were lacking in most studies (conventional t-statistics based variance estimation methods under-estimate the true variance of microarray data, so several variance estimation methods for microarray data have been developed).
Therefore, designing more efficient and faster algorithms for the proposed LRT is necessary in the future, and developing likelihood-ratio based variance component tests in GLMM with the bias-corrected PQL estimation is another interesting problem.
(model-based variance estimation).
(design-based variance estimation).
Robust variance estimation in GENMOD is based on Zeger and Liang's method [ 5, 6] which accounts only for the within-subject dependencies due to the repeated measurements over time.
Tshrink+ leverages only the variance estimates from external datasets to improve the variance estimation in the study of interest.
Since the variance estimation in the first pass of the thresholding operation is based on the observed noisy coefficients Y (m, n), a considerable amount of unreliable estimates leads to visually annoying noise specks in the reconstruction.
EM for channel and noise variance estimation: In TnDFnD channels, EM-based joint channel and noise variance estimation algorithms are proposed in [14 16].
EM for data detection and noise variance estimation: In TnDFnD channels, an EM-based joint data detection and noise variance estimation algorithm is proposed in [20].
We then reanalyzed the data using Poisson regression with robust variance estimation in STATA 13.0.
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