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Canonical correlation analysis for functional data

References Krzyśko M. (2009): Podstawy wielowymiarowego wnioskowania statysty- cznego [Foundations of multidimensional statistical inference]. Wydawnictwo Naukowe UAM, Poznan. Leurgans S.E., Moyeed R.A., Silverman B.W. (1993): Canonical correlation analy- sis when the data are curves. Journal of the Royal Statistical Society B 55(3): 725{740. Ramsay J.O., Danzell C.J. (1991): Some tools for functional data analysis. Journal of the Royal Statistical Society B 53: 539-572. Ramsay J

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Entropy as a measure of dependency for categorized data


Data arranged in a two-way contingency table can be obtained as a result of many experiments in the life sciences. In some cases the categorized trait is in fact conditioned by an unobservable continuous variable, called liability. It may be interesting to know the relationship between the Pearson correlation coefficient of these two continuous variables and the entropy function measuring the corresponding relation for categorized data. After many simulation trials, a linear regression was estimated between the Pearson correlation coefficient and the normalized mutual information (both on a logarithmic scale). It was observed that the regression coefficients obtained do not depend either on the number of observations classified on a categorical scale or on the continuous random distribution used for the latent variable, but they are influenced by the number of columns in the contingency table. In this paper a known measure of dependency for such data, based on the entropy concept, is applied.

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Remarks about a construction method for D-optimal chemical balance weighing designs

bipartite weighing designs under the certain condition. Colloquium Biometricum 34a, 17-28. Ceranka B., Graczyk M. (2016). New construction of D-optimal weighing design with non-negative correlations of errors. Colloquium Biometricum 46, 31-45. Ceranka B., Graczyk M. (2017). Some D-optimal chemical balance weighing designs: theory and examples. Biometrical Letters 54(2), 137-154. Ceranka B., Graczyk M. (2018). Regular D-optimal weighing design with non-negative correlations of errors constructed from some block designs. Colloquium Biometricum 48, 1

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Correlation of domination parameters with physicochemical properties of octane isomers

2 113.5 295.0 27.60 102.39 0.7191 1.3698 217.3 37.75 2233MMMM 2 2 2 106.5 270.8 24.50 93.06 0.8242 1.4612 225.6 42.90 Next, we obtain a cross-correlation matrix of domination parameters, which is shown in Table 2 . Table 2 Cross correlation matrix of domination parameters.   γ c γ t γ t ′ $\begin{array}{} \displaystyle \gamma _t^\prime \end{array}$ γ c 1.000     γ t 0.926 1.000   γ t ′ $\begin{array}{} \displaystyle \gamma _t^\prime \end{array}$ 0

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Intrachromosomal regulation decay in breast cancer

. Since the concept of physical chromosomal distance is only reasonable in the context of genes within the same chromosome, we will, from now on, consider chromosome-wise GRPs G k [ I ( i, j )]; here, k ( {1, 2 ,..., 22, x, y} is an ind ex working as the chromosome label. By analyzing G k [ I ( i, j )], it is possible to develop a deeper understanding of the way correlation structure relates to functional features. For instance, in a previous work [ 6 ], we have observed the phenomenon of decay of long-range correlations. The visual inspection of the mutual

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QSPR Analysis of certain Distance Based Topological Indices

is that the structural characteristics of a molecule are responsible for its properties. Topological indices are a convenient means of translating chemical constitution into numerical values which can be used for correlation with physical properties in quantitative structure-property/activity relationship (QSPR/QSAR) studies. The use of graph invariant (topological indices) in QSPR and QSAR studies has become of major interest in recent years. Topological indices have found application in various areas of chemistry, physics, mathematics, informatics, biology, etc

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Finding Hidden Structures, Hierarchies, and Cores in Networks via Isospectral Reduction

inspecting the hierarchies given by Γ deg , Γ page , Γ betw , and Γ close in Table 1 we can see many similarities and some interesting differences when compared to previous results. A less visual but more analytic way to compare our hierarchy with previous results is to use Kendall’s rank correlation coefficient τ, which measure the agreement or disagreement between two rankings of the same set. Here −1 ≤ τ ≤ 1 where τ = 1 means perfect agreement, τ = −1 perfect disagreement, and τ = 0 independence or lack of association of two rankings. Comparing the rankings of the

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Steady flow of a power law fluid through a tapered non-symmetric stenotic tube

formation, ASME J. Biomech. Eng . 111 (1989), pp. 316–324. 10.1115/1.3168385 Nazemi M. Kliestreuer C. Archie J. P. Sorrell F. Y. Fluid flow and plaque formation in an aortic formation ASME J. Biomech. Eng 111 1989 316 324 [3] C. G. Caro, J. M. Fitzerald and R.C. Shroter, Atheroma and arterial wall: shear observations, correlations and proposal of a shear dependent mass transfer mechanism for atherogeneses, ProcR.Soc. Lond Set B 177 (1971), pp. 109–159. 10.1098/rspb.1971.0019 Caro C. G. Fitzerald J. M. Shroter R.C. Atheroma

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Five Years of Phase Space Dynamics of the Standard & Poor’s 500

Correlation: An Introduction to the Cointelation Model". Wilmott 2013 (1): 50-61. 10.1002/wilm.10252 Mahdavi Damghani B. 2013 "The Non-Misleading Value of Inferred Correlation: An Introduction to the Cointelation Model" Wilmott 2013 1 50 61 [16] Kaneko,K., Tsuda, I. (2011) Complex Systems: Chaos and Beyond: A Constructive Approach with Applications Springer Series in Science& Business Media . Kaneko K. Tsuda I. 2011 Complex Systems: Chaos and Beyond: A Constructive Approach with Applications Springer Series in Science& Business

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Determination of robust optimum plot size and shape – a model-based approach

R eferences Bhatti A.U., Mulla D.J., Koehler F.E., Gurmani A.H. (1991): Identifying and removing spatial correlation from yield experiments. Soil Sci. Soc. Am. J. 55: 1523-1528. Cressie N.A.C. (1993): Statistics for Spatial Data. John Wiley, New York. Cressie N., Wikle C.K. (2011): Statistics for Spatio-Temporal Data. Pub. A John Wiley & Sons. Inc. Faground M., Meirvenne M. Van (2002): Accounting for Soil Spatial Autocorrelation in the design of experimental trials. Soil Sci. Soc. Am. J. 66: 1134-1142. Matheron G. (1963): Principles

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