Relevant bibliographies by topics / Variance stabilizing transformation

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Academic literature on the topic 'Variance stabilizing transformation'

Author: Grafiati
Published: 20 April 2024

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Journal articles on the topic "Variance stabilizing transformation"

1

Guerrero, Victor M., and Rafael Perera. "Variance Stabilizing Power Transformation for Time Series." Journal of Modern Applied Statistical Methods 3, no. 2 (2004): 357–69. http://dx.doi.org/10.22237/jmasm/1099267740.

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2

Woodruff, David L., and Gerrit Slevogt. "Variance stabilizing transformation of wind forecast errors." Wind Energy 19, no. 10 (2015): 1845–52. http://dx.doi.org/10.1002/we.1954.

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3

Lin, Simon M., Pan Du, Wolfgang Huber, and Warren A. Kibbe. "Model-based variance-stabilizing transformation for Illumina microarray data." Nucleic Acids Research 36, no. 2 (2008): e11-e11. http://dx.doi.org/10.1093/nar/gkm1075.

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4

Durbin, B. P., J. S. Hardin, D. M. Hawkins, and D. M. Rocke. "A variance-stabilizing transformation for gene-expression microarray data." Bioinformatics 18, Suppl 1 (2002): S105—S110. http://dx.doi.org/10.1093/bioinformatics/18.suppl_1.s105.

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5

Kwan, Andy C. C., and Ah-Boon Sim. "Portmanteau tests of randomness and Jenkins' variance-stabilizing transformation." Economics Letters 50, no. 1 (1996): 41–49. http://dx.doi.org/10.1016/0165-1765(95)00710-5.

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6

Sarno, Emma. "A variance stabilizing transformation for the Gini concentration ratio." Journal of the Italian Statistical Society 7, no. 1 (1998): 77–91. http://dx.doi.org/10.1007/bf03178922.

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7

Routray, Sidheswar, Arun Kumar Ray, and Chandrabhanu Mishra. "MRI Denoising using Sparse Based Curvelet Transform with Variance Stabilizing Transformation Framework." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (2017): 116. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp116-122.

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Abstract:
We develop an efficient MRI denoising algorithm based on sparse representation and curvelet transform with variance stabilizing transformation framework. By using sparse representation, a MR image is decomposed into a sparsest coefficients matrix with more no of zeros. Curvelet transform is directional in nature and it preserves the important edge and texture details of MR images. In order to get sparsity and texture preservation, we post process the denoising result of sparse based method through curvelet transform. To use our proposed sparse based curvelet transform denoising method to remov
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8

Dunning, Mark J., Matthew E. Ritchie, Nuno L. Barbosa-Morais, Simon Tavaré, and Andy G. Lynch. "Spike-in validation of an Illumina-specific variance-stabilizing transformation." BMC Research Notes 1, no. 1 (2008): 18. http://dx.doi.org/10.1186/1756-0500-1-18.

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9

Zhang, Minghui, Fengqin Zhang, Qiegen Liu, and Shanshan Wang. "VST-Net: Variance-stabilizing transformation inspired network for Poisson denoising." Journal of Visual Communication and Image Representation 62 (July 2019): 12–22. http://dx.doi.org/10.1016/j.jvcir.2019.04.011.

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10

Fujisawa, Hironori. "Variance stabilizing transformation and studentization for estimator of correlation coefficient." Statistics & Probability Letters 47, no. 3 (2000): 213–17. http://dx.doi.org/10.1016/s0167-7152(99)00158-3.

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