Literatura académica sobre el tema "Variance stabilizing transformation"
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Artículos de revistas sobre el tema "Variance stabilizing transformation"
Guerrero, Victor M. y Rafael Perera. "Variance Stabilizing Power Transformation for Time Series". Journal of Modern Applied Statistical Methods 3, n.º 2 (1 de noviembre de 2004): 357–69. http://dx.doi.org/10.22237/jmasm/1099267740.
Texto completoWoodruff, David L. y Gerrit Slevogt. "Variance stabilizing transformation of wind forecast errors". Wind Energy 19, n.º 10 (16 de diciembre de 2015): 1845–52. http://dx.doi.org/10.1002/we.1954.
Texto completoLin, Simon M., Pan Du, Wolfgang Huber y Warren A. Kibbe. "Model-based variance-stabilizing transformation for Illumina microarray data". Nucleic Acids Research 36, n.º 2 (4 de enero de 2008): e11-e11. http://dx.doi.org/10.1093/nar/gkm1075.
Texto completoDurbin, B. P., J. S. Hardin, D. M. Hawkins y D. M. Rocke. "A variance-stabilizing transformation for gene-expression microarray data". Bioinformatics 18, Suppl 1 (1 de julio de 2002): S105—S110. http://dx.doi.org/10.1093/bioinformatics/18.suppl_1.s105.
Texto completoKwan, Andy C. C. y Ah-Boon Sim. "Portmanteau tests of randomness and Jenkins' variance-stabilizing transformation". Economics Letters 50, n.º 1 (enero de 1996): 41–49. http://dx.doi.org/10.1016/0165-1765(95)00710-5.
Texto completoSarno, Emma. "A variance stabilizing transformation for the Gini concentration ratio". Journal of the Italian Statistical Society 7, n.º 1 (abril de 1998): 77–91. http://dx.doi.org/10.1007/bf03178922.
Texto completoRoutray, Sidheswar, Arun Kumar Ray y Chandrabhanu Mishra. "MRI Denoising using Sparse Based Curvelet Transform with Variance Stabilizing Transformation Framework". Indonesian Journal of Electrical Engineering and Computer Science 7, n.º 1 (1 de julio de 2017): 116. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp116-122.
Texto completoDunning, Mark J., Matthew E. Ritchie, Nuno L. Barbosa-Morais, Simon Tavaré y Andy G. Lynch. "Spike-in validation of an Illumina-specific variance-stabilizing transformation". BMC Research Notes 1, n.º 1 (2008): 18. http://dx.doi.org/10.1186/1756-0500-1-18.
Texto completoZhang, Minghui, Fengqin Zhang, Qiegen Liu y Shanshan Wang. "VST-Net: Variance-stabilizing transformation inspired network for Poisson denoising". Journal of Visual Communication and Image Representation 62 (julio de 2019): 12–22. http://dx.doi.org/10.1016/j.jvcir.2019.04.011.
Texto completoFujisawa, Hironori. "Variance stabilizing transformation and studentization for estimator of correlation coefficient". Statistics & Probability Letters 47, n.º 3 (abril de 2000): 213–17. http://dx.doi.org/10.1016/s0167-7152(99)00158-3.
Texto completoTesis sobre el tema "Variance stabilizing transformation"
Bourredjem, Abderrahmane. "Contribution à l'inférence sur le coefficient de corrélation intraclasse de concordance dans les études de fiabilité inter-juges". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCK078.
Texto completoMeasurement's reliability refers to its reproducibility when it is randomly repeated on the same subject and is a key metrological property for each measurement validation. The two-way intra-class correlation coefficient of agreement (ICCa) is a statistical parameter used to quantify the inter-rater reliability of continuous (or ordinal qualitative) measurements. It constitute a central reliability index recommended by the regulatory agencies. Nevertheless, its estimators are biased and a lot of solutions have been tried facing to its confidence interval (CI) problem. The latest works indicate that no method works well with a hard-to-detect violation of normality and when the number of subjects OR raters is limited, which is rather the case in practice. Furthermore, no variance stabilizing transformation (VST) nor statistical comparative test are available for the ICCa. The aim of this thesis is therefore to contribute to the development of methods that remedy the lack of the inferential tools for the ICCa. At a first step, we propose new asymptotic methods for the ICCa confidence interval, the calculation of the needed sample size of subjects and raters, and a likelihood ratio test to compare two ICCa. Then, in a second work, we develop three VSTs, improving the properties of the confidence interval for inter-rater reliability studies of moderate sample size, and the synthesis of several ICCa in the context of meta-analyses. Finally, in a third work, dedicated resampling methods are proposed, in combination with the best VST, to improve the ICCa confidence interval performances in case of non-normality with small sample size. It is above all a work of biostatistical methodology, with simulation evaluations of the introduced methods, and applications to several real data sets from inter-rater reliability studies and meta-analyses
Chung, Hsiang-Yu y 鍾翔宇. "A nonparametric variance-stabilizing transformation method in cDNA microarray". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/32065043145865846984.
Texto completo國立成功大學
統計學系碩博士班
94
For cDNA microarray data, the variance of gene is usually not the same and depends on its mean. Durbin et al. (2002) and Inoue et al. (2004) established the one-color gene expression model and obtain the relationship between variance of gene and its mean. They then derive the variance-stabilizing transformation function to stabilize the variance of the genes. In this article, we consider the two-color design and use nonparametric regression approach to stabilize the variance of gene expression level. We first, by applying lowess method, find the relationship between variance and mean of gene expression from scatter plot of variance versus mean, then use exponential function to approximate the relationship between variance and mean in a small region. Simulation study and real data analysis show that the performance of the suggested method is comparable to the parametric variance stabilization approach when the variance function is known.
Huang, Chwen y 黃純. "An Application of Quasi-likelihood Function --- An Alternative of Traditional Variance-stabilizing Transformation". Thesis, 1997. http://ndltd.ncl.edu.tw/handle/80056059713656882039.
Texto completo國立臺灣大學
農藝學系
85
To define a likelihood we have to specify the form of distribution of the observations. However, to define a quasi- likelihood function we need only to specify the relationship between mean and variance of the distribution concerned. Quasi- likelihood can be used for estimation and it enlarges the scope of the analysis of data. For the data set which do not satisfy the assumptions of ordinary analysis, traditional transformations such as square root, logarithmic and angular transformation are used to achieve thenormality and stabilize the variances. This thesis investigates the possibility to use an alternative, that is, using a generalized linear models with some given variance functions. Also the similarities and differences between these two approaches were studied by numerical examples. We consider six numerical examples for illustrating the applications of quasi-likelihood functions and for comparing the two different approaches. First three data sets are of the form of one-way tables and second three data sets are of theform of two-way tables. For each data set, we compute two measures and for comparing the effectiveness in estimation and model fitting, res of these two approaches. In the generalized linear model considered, the link functions which playing the role of achieving the additivity have different forms from the traditional transformations. However, the results obtained from this study show that these two approaches are quite similar in effectiveness in estimation and model fitting. This kind of similarity suggests that both approaches might be equivalent and the equivalence might be proved mathematically.
Capítulos de libros sobre el tema "Variance stabilizing transformation"
Ding, Ling, Huying Zhang, Bijun Li, Jinsheng Xiao y Jian Zhou. "Image Noise Estimation Based on Principal Component Analysis and Variance-Stabilizing Transformation". En Lecture Notes in Computer Science, 58–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_6.
Texto completoPu, Xiaojun y Michael Tiefelsdorf. "A Variance-Stabilizing Transformation to Mitigate Biased Variogram Estimation in Heterogeneous Surfaces with Clustered Samples". En Advances in Geocomputation, 271–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-22786-3_24.
Texto completoPolitis, Dimitris N. "A normalizing and variance–stabilizing transformation for financial time series". En Recent Advances and Trends in Nonparametric Statistics, 335–47. Elsevier, 2003. http://dx.doi.org/10.1016/b978-044451378-6/50022-3.
Texto completoActas de conferencias sobre el tema "Variance stabilizing transformation"
Brahimi, Malek y Sidi Berri. "The Use of ARMA Models in Earthquake Response Spectra". En 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89023.
Texto completoPrucnal, Paul R. y Evan L. Goldstein. "Exact Variance-Stabilizing Transformations for Image-Signal-Dependent Exponential and Rayleigh Noise". En Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/qlip.1986.tud4.
Texto completode DeckerK, A., J. A. Lee y M. Verlysen. "Variance stabilizing transformations in patch-based bilateral filters for poisson noise image denoising". En 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5334715.
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