Готові списки джерел за темами / Variance stabilizing transformation

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Добірка наукової літератури з теми "Variance stabilizing transformation"

Автор: Grafiati
Опубліковано: 20 квітня 2024

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Статті в журналах з теми "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 (November 1, 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 (December 16, 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 (January 4, 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 (July 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 (January 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 (April 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 (July 1, 2017): 116. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp116-122.

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Анотація:
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 remove rician noise in MR images, we use forward and inverse variance-stabilizing transformations. Experimental results reveal the efficacy of our approach to rician noise removal while well preserving the image details. Our proposed method shows improved performance over the existing denoising methods in terms of PSNR and SSIM for T1, T2 weighted MR images.
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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 (April 2000): 213–17. http://dx.doi.org/10.1016/s0167-7152(99)00158-3.

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Більше джерел

Дисертації з теми "Variance stabilizing transformation"

1

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.

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Анотація:
La fiabilité d'une mesure fait référence à sa reproductibilité lorsqu'elle est répétée de manière aléatoire sur le même sujet et constitue une propriété métrologique clé pour toutes les mesures. Le coefficient de corrélation intra-classe de concordance à deux voies (ICCa) est un paramètre statistique utilisé pour quantifier la fiabilité inter-observateurs des mesures continues (ou qualitatives ordinales). Il constitue un indice de fiabilité central recommandé par les agences réglementaires. Néanmoins, ses estimateurs sont biaisés et de nombreuses solutions ont été essayées pour améliorer son intervalle de confiance (IC). Les travaux les plus récents indiquent qu'aucune méthode ne fonctionne bien en cas de non-normalité des données, difficilement détectable à partir des observations, et lorsque le nombre de sujets ou d'évaluateurs est limité, ce qui est plutôt le cas en pratique. De plus, aucune transformation de stabilisation de la variance (VST) ni aucun test de comparaison statistique ne sont disponibles pour l'ICCa. L'objectif de cette thèse est donc de contribuer au développement de méthodes palliant le manque d'outils inférentiels pour l'ICCa. Dans un premier temps, nous proposons de nouvelles méthodes asymptotiques pour l'intervalle de confiance de l'ICCa, le calcul de la taille de l'échantillon des sujets et des évaluateurs, et un test de rapport de vraisemblance pour comparer deux ICCa. Ensuite, dans un deuxième travail, nous développons trois VST, améliorant les propriétés de l'intervalle de confiance pour les études de fiabilité inter-évaluateurs de taille modérée, et la synthèse de plusieurs ICCa dans le contexte de méta-analyses. Enfin, dans un troisième travail, des méthodes de rééchantillonnage spécifiques sont proposées, en combinaison avec la meilleure VST, pour améliorer les performances de l'intervalle de confiance de l'ICCa sans l'hypothèse de normalité et avec de petits échantillons. Il s'agit d'un travail de méthodologie biostatistique, avec des évaluations par simulation des méthodes introduites, et des applications à plusieurs jeux de données réelles issues d'études de fiabilité inter-évaluateurs et de méta-analyses
Measurement'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
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2

Chung, Hsiang-Yu, and 鍾翔宇. "A nonparametric variance-stabilizing transformation method in cDNA microarray." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/32065043145865846984.

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Анотація:
碩士
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.
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3

Huang, Chwen, and 黃純. "An Application of Quasi-likelihood Function --- An Alternative of Traditional Variance-stabilizing Transformation." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/80056059713656882039.

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Анотація:
碩士
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.
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Частини книг з теми "Variance stabilizing transformation"

1

Ding, Ling, Huying Zhang, Bijun Li, Jinsheng Xiao, and Jian Zhou. "Image Noise Estimation Based on Principal Component Analysis and Variance-Stabilizing Transformation." In Lecture Notes in Computer Science, 58–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_6.

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2

Pu, Xiaojun, and Michael Tiefelsdorf. "A Variance-Stabilizing Transformation to Mitigate Biased Variogram Estimation in Heterogeneous Surfaces with Clustered Samples." In Advances in Geocomputation, 271–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-22786-3_24.

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3

Politis, Dimitris N. "A normalizing and variance–stabilizing transformation for financial time series." In Recent Advances and Trends in Nonparametric Statistics, 335–47. Elsevier, 2003. http://dx.doi.org/10.1016/b978-044451378-6/50022-3.

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Тези доповідей конференцій з теми "Variance stabilizing transformation"

1

Brahimi, Malek, and Sidi Berri. "The Use of ARMA Models in Earthquake Response Spectra." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89023.

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Анотація:
Structural design spectra are based on smoothed linear response spectra obtained from different events scaled by their peak values. Such an approach does not incorporate other characteristics of the excitation represented by measured data. This study investigate the use of non-stationary models which can be considered characteristic and representative of specific historical earthquakes. An earthquake record is regarded as a sample realization from a population of such samples, which could have been generated by the stochastic process characterized by an Autoregressive Moving Average (ARMA) model. ARMA models are developed for four major earthquakes after processing by a variance stabilizing transformation. Samples of acceleration records are generated for each event. In this earthquake modeling procedure, parameters describing the modulating function of the record and the stabilized series are estimated. Maximum displacement ductility demand and normalized hysteretic energy demand for linear and stiffness softening single degree of freedom system systems are computed for the samples generated for each event. The sensitivity and dependence of demand spectra on earthquake model characteristics are examined to develop a response prediction model. Non linear response analysis of the four events indicates that ARMA (2,1) process using samples of twenty simulated earthquakes provide a reliable description of the information contained within acceleration records. Empirical relationships for displacement ductility and Normalized hysteretic energy demand spectra are developed.
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2

Prucnal, Paul R., and Evan L. Goldstein. "Exact Variance-Stabilizing Transformations for Image-Signal-Dependent Exponential and Rayleigh Noise." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/qlip.1986.tud4.

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Анотація:
One problem presented by the processing of optical signals is that the noise encountered tends to be signal-dependent. Coherent sources, for example, generate photon noise characterized by variance that is proportional to the signal value. Sources emitting chaotic or superposed coherent and chaotic radiation exhibit photon noise variance that is quadratic in the signal value. For film-grain noise, the variance is proportional to some power of the signal. Other imaging devices such as optical disks exhibit noise having a similar signal-dependence.
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3

de DeckerK, A., J. A. Lee, and M. Verlysen. "Variance stabilizing transformations in patch-based bilateral filters for poisson noise image denoising." In 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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