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Готові списки джерел за темами / Variance stabilizing transformation / Статті в журналах

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Статті в журналах з теми "Variance stabilizing transformation"

Автор: Grafiati

Опубліковано: 20 квітня 2024

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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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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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11

Purohit, Parul V., David M. Rocke, Mark R. Viant, and David L. Woodruff. "Discrimination Models Using Variance-Stabilizing Transformation of Metabolomic NMR Data." OMICS: A Journal of Integrative Biology 8, no. 2 (2004): 118–30. http://dx.doi.org/10.1089/1536231041388348.

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12

Zhang, Wei, Baolin Mao, Xiaozhao Chen, et al. "Low-Dose Computed Tomography Image Denoising with Variance-Stabilizing Transformation and Noise Variance Estimation." Journal of Medical Imaging and Health Informatics 6, no. 5 (2016): 1345–59. http://dx.doi.org/10.1166/jmihi.2016.1924.

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13

Uniejewski, Bartosz, and Rafał Weron. "Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models." Energies 11, no. 8 (2018): 2039. http://dx.doi.org/10.3390/en11082039.

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Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that usin

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14

Francis Harrison, Paul. "Varistran: Anscombe's variance stabilizing transformation for RNA-seq gene expression data." Journal of Open Source Software 2, no. 16 (2017): 257. http://dx.doi.org/10.21105/joss.00257.

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15

INOUE, MASATO, SHIN-ICHI NISHIMURA, GEN HORI, et al. "IMPROVED PARAMETER ESTIMATION FOR VARIANCE-STABILIZING TRANSFORMATION OF GENE-EXPRESSION MICROARRAY DATA." Journal of Bioinformatics and Computational Biology 02, no. 04 (2004): 669–79. http://dx.doi.org/10.1142/s0219720004000806.

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A gene-expression microarray datum is modeled as an exponential expression signal (log-normal distribution) and additive noise. Variance-stabilizing transformation based on this model is useful for improving the uniformity of variance, which is often assumed for conventional statistical analysis methods. However, the existing method of estimating transformation parameters may not be perfect because of poor management of outliers. By employing an information normalization technique, we have developed an improved parameter estimation method, which enables statistically more straightforward outli

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16

Song, Kai-Sheng. "Asymptotic Relative Efficiency and Exact Variance Stabilizing Transformation for the Generalized Gaussian Distribution." IEEE Transactions on Information Theory 59, no. 7 (2013): 4389–96. http://dx.doi.org/10.1109/tit.2013.2249182.

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17

McLean, Angus M., Donald A. Ruggirello, Christopher Banfield, Mario A. Gonzalez, and Meir Bialer. "Application of a Variance-Stabilizing Transformation Approach to Linear Regression of Calibration Lines." Journal of Pharmaceutical Sciences 79, no. 11 (1990): 1005–8. http://dx.doi.org/10.1002/jps.2600791112.

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18

France, R. L. "Aggregation in Littoral Amphipod Populations: Transformation Controversies Revisited." Canadian Journal of Fisheries and Aquatic Sciences 44, no. 8 (1987): 1510–15. http://dx.doi.org/10.1139/f87-182.

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Spatial variability in amphipod (Hyalella azteca) density was examined in 17 Canadian Shield Lakes in south-central Ontario. Aggregation was measured by the exponent of the power relationship between density and variance. The average b index was 1.45, although this value varied depending on lake and habitat type. Whereas this should indicate the use of a fourth-root transformation, neither this nor the logarithmic transformation was as effective as the square-root in stabilizing variance. A literature review suggests that if a universal transformation is sought for benthos data, it should not

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19

Gerke, Oke, and Sören Möller. "Modeling Bland–Altman Limits of Agreement with Fractional Polynomials—An Example with the Agatston Score for Coronary Calcification." Axioms 12, no. 9 (2023): 884. http://dx.doi.org/10.3390/axioms12090884.

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Bland–Altman limits of agreement are very popular in method comparison studies on quantitative outcomes. However, a straightforward application of Bland–Altman analysis requires roughly normally distributed differences, a constant bias, and variance homogeneity across the measurement range. If one or more assumptions are violated, a variance-stabilizing transformation (e.g., natural logarithm, square root) may be sufficient before Bland–Altman analysis can be performed. Sometimes, fractional polynomial regression has been used when the choice of variance-stabilizing transformation was unclear

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20

Adubisi, Obinna. "Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America." Science Journal of Applied Mathematics and Statistics 5, no. 5 (2017): 174. http://dx.doi.org/10.11648/j.sjams.20170505.12.

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21

Ma, Xiaodong, Kâmil Uğurbil, and Xiaoping Wu. "Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation." NeuroImage 215 (July 2020): 116852. http://dx.doi.org/10.1016/j.neuroimage.2020.116852.

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22

Makitalo, Markku, and Alessandro Foi. "A Closed-Form Approximation of the Exact Unbiased Inverse of the Anscombe Variance-Stabilizing Transformation." IEEE Transactions on Image Processing 20, no. 9 (2011): 2697–98. http://dx.doi.org/10.1109/tip.2011.2121085.

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23

Sid Ahmed, Soumia, Zoubeida Messali, Florent Poyer, et al. "Iterative Variance Stabilizing Transformation Denoising of Spectral Domain Optical Coherence Tomography Images Applied to Retinoblastoma." Ophthalmic Research 59, no. 3 (2018): 164–69. http://dx.doi.org/10.1159/000486283.

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24

Reddon, John R. "Fisher’s Tanh−1 Transformation of the Correlation Coefficient and a Test for Complete Independence in a Multivariate Normal Population." Journal of Educational Statistics 12, no. 3 (1987): 294–300. http://dx.doi.org/10.3102/10769986012003294.

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Computer sampling from a multivariate normal spherical population was used to evaluate Type I error rates for a test of P = I based on Fisher’s tanh−1 variance stabilizing transformation of the correlation coefficient. The range of variates considered was 5 to 25 and Type I error rates were estimated for several sample sizes with 2,500 independent replications. Except for small samples the test was well behaved. After the test converges to an acceptable Type I error rate it is preferable to Box’s test of P = I.

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25

Wagner, G. P. "Multivariate mutation-selection balance with constrained pleiotropic effects." Genetics 122, no. 1 (1989): 223–34. http://dx.doi.org/10.1093/genetics/122.1.223.

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Abstract A multivariate quantitative genetic model is analyzed that is based on the assumption that the genetic variation at a locus j primarily influences an underlying physiological variable yj, while influence on the genotypic values is determined by a kind of "developmental function" which is not changed by mutations at this locus. Assuming additivity among loci the developmental function becomes a linear transformation of the underlying variables y onto the genotypic values x, x = By. In this way the pleiotropic effects become constrained by the structure of the B-matrix. The equilibrium

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26

Janczura, Joanna, and Andrzej Puć. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation." Energies 16, no. 2 (2023): 807. http://dx.doi.org/10.3390/en16020807.

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In this paper, we propose dynamic, short-term, financial risk management strategies for small electricity producers and buyers that trade in the wholesale electricity markets. Since electricity is mostly nonstorable, financial risk coming from extremely volatile electricity prices cannot be reduced by using standard finance-based approaches. Instead, a short-term operational planing and a proper trade diversification might be used. In this paper, we analyze the price risk in terms of the Markowitz mean–variance portfolio theory. Hence, it is crucial to forecast properly the variance of electri

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27

Bosse, Nikos I., Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher, and Sebastian Funk. "Scoring epidemiological forecasts on transformed scales." PLOS Computational Biology 19, no. 8 (2023): e1011393. http://dx.doi.org/10.1371/journal.pcbi.1011393.

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Forecast evaluation is essential for the development of predictive epidemic models and can inform their use for public health decision-making. Common scores to evaluate epidemiological forecasts are the Continuous Ranked Probability Score (CRPS) and the Weighted Interval Score (WIS), which can be seen as measures of the absolute distance between the forecast distribution and the observation. However, applying these scores directly to predicted and observed incidence counts may not be the most appropriate due to the exponential nature of epidemic processes and the varying magnitudes of observed

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28

Davies, Georgina, and Noel Cressie. "Analysis of variability of tropical Pacific sea surface temperatures." Advances in Statistical Climatology, Meteorology and Oceanography 2, no. 2 (2016): 155–69. http://dx.doi.org/10.5194/ascmo-2-155-2016.

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Abstract. Sea surface temperature (SST) in the Pacific Ocean is a key component of many global climate models and the El Niño–Southern Oscillation (ENSO) phenomenon. We shall analyse SST for the period November 1981–December 2014. To study the temporal variability of the ENSO phenomenon, we have selected a subregion of the tropical Pacific Ocean, namely the Niño 3.4 region, as it is thought to be the area where SST anomalies indicate most clearly ENSO's influence on the global atmosphere. SST anomalies, obtained by subtracting the appropriate monthly averages from the data, are the focus of th

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29

Fosdick, Bailey K., and Michael D. Perlman. "Variance-stabilizing and Confidence-stabilizing Transformations for the Normal Correlation Coefficient with Known Variances." Communications in Statistics - Simulation and Computation 45, no. 6 (2016): 1918–35. http://dx.doi.org/10.1080/03610918.2014.882948.

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30

Xiaojun, Feng. "Regulating labour dispatch in China: A cat-and-mouse game." China Information 33, no. 1 (2018): 88–109. http://dx.doi.org/10.1177/0920203x18791398.

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This article suggests cautious optimism toward the prevailing Polanyian countermovement discourse by providing a timely and comprehensive examination of the enforcement of the labour dispatch regulation in China. Since the enactment of the regulation, some enterprises have narrowed the remuneration gap between agency workers and formal employees, while others have retained a large gap in overtime pay, bonuses, and welfare benefits between these two groups of workers. The regulation has reduced the number of agency workers, but has invoked the abuse of the more precarious ‘outsourced’ workers a

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31

Kumar, K. Sampath, and C. Arun C. Arun. "Poisson Noise Removal From Flourescence Images Using Optimized Variance-Stabilizing Transformations And Standard Gaussian Denoising Strategies." Indian Journal of Applied Research 1, no. 3 (2011): 79–82. http://dx.doi.org/10.15373/2249555x/dec2011/28.

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32

Lahiri, S. N. "Variance stabilizing transformations, studentization and the bootstrap." Journal of Statistical Planning and Inference 61, no. 1 (1997): 105–23. http://dx.doi.org/10.1016/s0378-3758(97)89715-7.

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33

Durbin, B. P., and D. M. Rocke. "Variance-stabilizing transformations for two-color microarrays." Bioinformatics 20, no. 5 (2004): 660–67. http://dx.doi.org/10.1093/bioinformatics/btg464.

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34

Nazarzadeh, Milad, Zeinab Bidel, Alireza Mosavi Jarahi, et al. "Prevalence of Cannabis Lifetime Use in Iranian High School and College Students." American Journal of Men's Health 9, no. 5 (2014): 397–409. http://dx.doi.org/10.1177/1557988314546667.

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Cannabis is the most widely used substance in the world. This study aimed to estimate the prevalence of cannabis lifetime use (CLU) in high school and college students of Iran and also to determine factors related to changes in prevalence. A systematic review of literature on cannabis use in Iran was conducted according to MOOSE guideline. Domestic scientific databases, PubMed/Medline, ISI Web of Knowledge, and Google Scholar, relevant reference lists, and relevant journals were searched up to April, 2014. Prevalences were calculated using the variance stabilizing double arcsine transformation

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35

Uniejewski, Bartosz, Rafal Weron, and Florian Ziel. "Variance Stabilizing Transformations for Electricity Spot Price Forecasting." IEEE Transactions on Power Systems 33, no. 2 (2018): 2219–29. http://dx.doi.org/10.1109/tpwrs.2017.2734563.

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36

Konishi, Sadanori. "Normalizing and variance stabilizing transformations for intraclass correlations." Annals of the Institute of Statistical Mathematics 37, no. 1 (1985): 87–94. http://dx.doi.org/10.1007/bf02481082.

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37

Celikyurt, Ipek Komsuoglu, Guner Ulak, Oguz Mutlu, Furuzan Yildiz Akar, Faruk Erden, and Sezer Sener Komsuoglu. "Lamotrigine effects sensorimotor gating in WAG/Rij rats." Journal of Neurosciences in Rural Practice 03, no. 02 (2012): 126–30. http://dx.doi.org/10.4103/0976-3147.98207.

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ABSTRACTIntroduction: Prepulse inhibition (PPI) is a measurable form of sensorimotor gating. Disruption of PPI reflects the impairment in the neural filtering process of mental functions that are related to the transformation of an external stimuli to a response. Impairment of PPI is reported in neuropsychiatric illnesses such as schizophrenia, Huntington’s disease, Parkinson’s diseases, Tourette syndrome, obsessive compulsive disorder, and temporal lobe epilepsy with psychosis. Absence epilepsy is the most common type of primary generalized epilepsy. Lamotrigine is an antiepileptic drug that

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38

Rothe, Hendrik. "Improved accuracy in laser triangulation by variance-stabilizing transformations." Optical Engineering 31, no. 7 (1992): 1538. http://dx.doi.org/10.1117/12.57691.

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39

Bar-Lev, Shaul K., and Peter Enis. "On the construction of classes of variance stabilizing transformations." Statistics & Probability Letters 10, no. 2 (1990): 95–100. http://dx.doi.org/10.1016/0167-7152(90)90002-o.

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40

Rocke, D. M., and B. Durbin. "Approximate variance-stabilizing transformations for gene-expression microarray data." Bioinformatics 19, no. 8 (2003): 966–72. http://dx.doi.org/10.1093/bioinformatics/btg107.

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41

Yu, Guan. "Variance stabilizing transformations of Poisson, binomial and negative binomial distributions." Statistics & Probability Letters 79, no. 14 (2009): 1621–29. http://dx.doi.org/10.1016/j.spl.2009.04.010.

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42

Bar-Lev, S. K., and P. Enis. "Variance stabilizing and normalizing transformations for the compound poisson process." Metrika 39, no. 1 (1992): 165–75. http://dx.doi.org/10.1007/bf02613996.

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43

Zhang, Chunlei, Huan Yang, Qinglei Xu, et al. "Comprehensive Genome and Transcriptome Analysis Identifies SLCO3A1 Associated with Aggressive Behavior in Pigs." Biomolecules 13, no. 9 (2023): 1381. http://dx.doi.org/10.3390/biom13091381.

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Copy number variation (CNV) represents a significant reservoir of genetic diversity within the genome and exhibits a strong association with economically valuable traits in livestock. The manifestation of aggressive behavior in pigs has detrimental effects on production efficiency, immune competency, and meat quality. Nevertheless, the impact of CNV on the aggressive behavior of pigs remains elusive. In this investigation, we employed an integrated analysis of genome and transcriptome data to investigate the interplay between CNV, gene expression changes, and indicators of aggressive behavior

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44

Verrel, Julius. "Distributional properties and variance-stabilizing transformations for measures of uncontrolled manifold effects." Journal of Neuroscience Methods 191, no. 2 (2010): 166–70. http://dx.doi.org/10.1016/j.jneumeth.2010.06.016.

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45

Hayakawa, Takesi. "Normalizing and variance stabilizing transformations of multivariate statistics under an elliptical population." Annals of the Institute of Statistical Mathematics 39, no. 2 (1987): 299–306. http://dx.doi.org/10.1007/bf02491469.

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46

Prucnal, Paul R., and Evan L. Goldstein. "Exact variance-stabilizing transformations for image-signal-dependent Rayleigh and other Weibull noise sources." Applied Optics 26, no. 6 (1987): 1038. http://dx.doi.org/10.1364/ao.26.001038.

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47

Chaubey, Yogendra P., Murari Singh, and Debaraj Sen. "Symmetrizing and Variance Stabilizing Transformations of Sample Coefficient of Variation from Inverse Gaussian Distribution." Sankhya B 79, no. 2 (2017): 217–46. http://dx.doi.org/10.1007/s13571-017-0136-z.

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48

BAR-LEV, SHAUL K., and PETER ENIS. "On the classical choice of variance stabilizing transformations and an application for a Poisson variate." Biometrika 75, no. 4 (1988): 803–4. http://dx.doi.org/10.1093/biomet/75.4.803.

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49

Becker, Betsy Jane. "Small-Sample Accuracy of Approximate Distributions of Functions of Observed Probabilities From t Tests." Journal of Educational Statistics 16, no. 4 (1991): 345–69. http://dx.doi.org/10.3102/10769986016004345.

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The observed probability p is the social scientist’s primary tool for evaluating the outcomes of statistical hypothesis tests. Functions of p s are used in tests of “combined significance,” meta-analytic summaries based on sample probability values. This study examines the nonnull asymptotic distributions of several functions of one-tailed sample probability values (from t tests). Normal approximations were based on the asymptotic distributions of z(p), the standard normal deviate associated with the one-sided p value; of ln(p), the natural logarithm of the probability value; and of several mo

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50

Su, Shengchen, Sungyong You, Yanping Wang, Patrick Tamukong, Catherine S. Grasso, and Hyung L. Kim. "Abstract 4178: PAK4 inhibition improves PD1 blockade immunotherapy in prostate cancer." Cancer Research 82, no. 12_Supplement (2022): 4178. http://dx.doi.org/10.1158/1538-7445.am2022-4178.

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Abstract Prostate cancers have low immune infiltration and do not respond to PD1 blockade immunotherapy. The objective of this study was to examine the effect of PAK4 inhibition on anti-tumor immune response in preclinical prostate cancer models. C57BL/6J mice (male, 5-8 weeks old) were injected with wild type (WT) or PAK4 knockdown (KD) syngeneic Rm1 tumor cells (2X105 cell/mouse) and treated with or without αPD1 blockade (200μg, BioXCell). Tumor growth was monitored and analyzed using ANOVA of repeated measures (n=6). In a separate experiment, 1 cm RM1 tumors were collected for RNA sequencin

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