Relevant bibliographies by topics / Stratagraphic correlation - Statistical methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Stratagraphic correlation - Statistical methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Stratagraphic correlation - Statistical methods"

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Schmidt, M. E., and K. Steindorf. "Statistical Methods for the Validation of Questionnaires." Methods of Information in Medicine 45, no. 04 (2006): 409–13. http://dx.doi.org/10.1055/s-0038-1634096.

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Summary Objectives: Questionnaires used in epidemiological studies should be validated. However, unclarity exists about the appropriate statistical methods and interpretation of validation studies. Thus, we investigated the theory and practice of statistical evaluation approaches. Methods: Using three platforms, a literature review, own simulations, and a validation study performed by ourselves, we worked out relevant limitations, advantages, and new important aspects of evaluation methods. Results: Our systematic literature review, based on physical activity questionnaires, revealed that correlation coefficients are still the common approach in validation studies, found in 41 of 46 reviewed publications (89.1%). This practice has been criticized in the theoretically oriented literature for more than 20 years. Appropriate evaluation methods as recommended by Bland and Altman were found in only ten publications (21.7 %).We showed that serious bias in questionnaires can be revealed by Bland-Altman plots but may remain undetected by correlation coefficients. With our simulations we refuted the argument that correlation coefficients properly investigate whether a questionnaire ranks the subjects sufficiently well. Further, with Bland-Altman analyses we could evaluate differential errors with respect to case-control status in our validation study. Yet, this was not possible with correlation coefficients, because they generally do not identify systematic bias. In addition, we show a potential pitfall in the interpretation of Bland-Altman plots that might occur in specific rare instances. Conclusions: The commonly used correlation approach can yield misleading conclusions in validation studies. A more frequent and proper use of the Bland-Altman methods would be desirable to improve epidemiological data quality.
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Velu, Raja, and Maurice McInerney. "A Note on Statistical Methods Adjusting for Intraclass Correlation." Biometrics 41, no. 2 (June 1985): 533. http://dx.doi.org/10.2307/2530878.

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Krasnov, A. E., S. A. Krasnikov, and I. N. Kompanets. "Correlation-statistical methods of distinguishing complicated and noisy spectra." Journal of Optics A: Pure and Applied Optics 4, no. 3 (April 8, 2002): 329–37. http://dx.doi.org/10.1088/1464-4258/4/3/320.

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Sarry, Mikhail F. "Analytical methods of calculating correlation functions in quantum statistical physics." Uspekhi Fizicheskih Nauk 161, no. 11 (1991): 47. http://dx.doi.org/10.3367/ufnr.0161.199111b.0047.

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Sarry, Mikhail F. "Analytical methods of calculating correlation functions in quantum statistical physics." Soviet Physics Uspekhi 34, no. 11 (November 30, 1991): 958–79. http://dx.doi.org/10.1070/pu1991v034n11abeh002482.

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Angelsky, O. V., I. I. Magun, and P. P. Maksimyak. "Optical correlation methods in statistical studies of random phase objects." Optics Communications 72, no. 3-4 (July 1989): 153–56. http://dx.doi.org/10.1016/0030-4018(89)90386-6.

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Huzsvai, László, Péter Fejér, Árpád Illés, Csaba Bojtor, Csilla Bojté, Éva Horváth, and Cintia Demeter. "Analysis of sweet corn nutritional values using multivariate statistical methods." Acta Agraria Debreceniensis, no. 1 (June 1, 2021): 103–8. http://dx.doi.org/10.34101/actaagrar/1/8587.

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Processing large amounts of data provided by automated analytical equipment requires carefulness. Most mathematical and statistical methods have strict application conditions. Most of these methods are based on eigenvalue calculations and require variables to be correlated in groups. If this condition is not met, the most popular multivariate methods cannot be used. The best procedure for such testing is the Kaiser-Meyer-Olkin test for Sampling Adequacy. Two databases were examined using the KMO test. One of them resulted from the sweet corn measured in the scone of the study, while the other from the 1979 book of János Sváb. For both databases, MSA (measures sampling adequacy) was well below the critical value, thus they are not suitable e.g. for principal component analysis. In both databases, the values of the partial correlation coefficients were much higher than Pearson’s correlation coefficients. Often the signs of partial coefficients did not match the signs of linear correlation coefficients. One of the main reasons for this is that the correlation between the variables is non-linear. Another reason is that control variables have a non-linear effect on a given variable. In such cases, classical methods should be disregarded and expert models better suited to the problem should be chosen in order to analyse the correlation system.
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Lenzi, Andrea, Loredana Gandini, Francesco Lombardo, Rocco Rago, Donatella Paoli, and Franco Dondero. "Antisperm Antibody Detection: 2. Clinical, Biological, and Statistical Correlation Between Methods." American Journal of Reproductive Immunology 38, no. 3 (September 1997): 224–30. http://dx.doi.org/10.1111/j.1600-0897.1997.tb00303.x.

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Franz, T., and P. Krebs. "Statistical methods towards more efficient infiltration measurements." Water Science and Technology 54, no. 6-7 (September 1, 2006): 153–60. http://dx.doi.org/10.2166/wst.2006.593.

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A comprehensive knowledge about the infiltration situation in a catchment is required for operation and maintenance. Due to the high expenditures, an optimisation of necessary measurement campaigns is essential. Methods based on multivariate statistics were developed to improve the information yield of measurements by identifying appropriate gauge locations. The methods have a high degree of freedom against data needs. They were successfully tested on real and artificial data. For suitable catchments, it is estimated that the optimisation potential amounts up to 30% accuracy improvement compared to non-optimised gauge distributions. Beside this, a correlation between independent reach parameters and dependent infiltration rates could be identified, which is not dominated by the groundwater head.
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Ulbikas, J., A. Čenys, D. Žemaitytė, and G. Varoneckas. "Correlation in the heart rate data." Nonlinear Analysis: Modelling and Control 2 (December 21, 1998): 141–48. http://dx.doi.org/10.15388/na.1998.2.0.15301.

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Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.
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Dissertations / Theses on the topic "Stratagraphic correlation - Statistical methods"

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Xu, Fei. "Correlation-aware statistical methods for sampling-based group by estimates." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024750.

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Azzollini, Yang Wu. "Correlation methods in the statistical analysis of financial trading data." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:e0279f41-c83f-4514-bb69-f49bb4159822.

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This thesis considers problems associated with the statistical analysis of correlation in financial trading data. Sources of data are identified and their characteristics are described. Nowadays most financial transactions are carried out electronically on automated exchanges or electronic communication networks and active participants in the market re- quire sophisticated computing infrastructure to compete effectively. These data are shown to present novel statistical challenges both in retrospectively analysing the vast stores of accumulated historical data and also in online processing of high-bandwidth multiple data streams arriving on millisecond time-scales. We show that computational speed dictates the range of statistical tools that are available for high-speed calculation. The measurement and interpretation of correlation is a dominant concern in the analysis of high-frequency financial data. We compare and develop methods for assessing volatility, cross-asset correlation and lead-lag effects. For volatility estimation we consider a class of estimates that can accommodate noisy irregularly spaced data. We derive explicit expressions for the variance of these estimators and show how the estimators can be modified to obtain infill consistency. We then consider the problem of covariance estimation and develop a new estimator, demonstrating its superior performance. We explore the problems of quantifying lead-lag relationships and show that our new covariance estimator provides a sharper estimate of lead-lag delay. We then develop a method of exploring lead-lag structure in depth and demonstrate how to obtain a maximum likelihood estimator of the delay structure. The final chapter briefly describes ongoing research questions relating to the design of hedging strategies at times of market disruption.
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Abdalmajid, Mohammed Babekir Elmalik. "An application of factor analysis on a 24-item scale on the attitudes towards AIDS precautions using Pearson, Spearman and Polychoric correlation matrices." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8765_1184324798.

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The 24-item scale has been used extensively to assess the attitudes towards AIDS precautions. This study investigated the usefulness and validity of the instrument in a South African setting, fourteen years after the development of the instrument. If a new structure could be found statistically, the HIV/AIDS prevention strategies could be more effective in aiding campaigns to change attitudes and sexual behaviour.

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Masud, Mohammad Shahed. "New statistical methods to derive functional connectivity from multiple spike trains." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/547.

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Analysis of functional connectivity of simultaneously recorded multiple spike trains is one of the major issues in the neuroscience. The progress of the statistical methods to the analysis of functional connectivity of multiple spike trains is relatively slow. In this thesis two statistical techniques are presented to the analysis of functional connectivity of multiple spike trains. The first method is known as the modified correlation grid (MCG). This method is based on the calculation of cross-correlation function of all possible pair-wise spike trains. The second technique is known as the Cox method. This method is based on the modulated renewal process (MRP). The original paper on the application of the Cox method (Borisyuk et al., 1985) to neuroscience data was used to analyse only pairs and triplets of spike trains. This method is further developed in this thesis to support simultaneously recorded of any possible set of multiple spike trains. A probabilistic model is developed to test the Cox method. This probabilistic model is based on the MRP. Due to the common probabilistic basis of the probabilistic model and the Cox method, the probabilistic model is a convenient technique to test the Cox method. A new technique based on a pair-wise analysis of Cox method known as the Cox metric is presented to find the groups of coupled spike trains. Another new technique known as motif analysis is introduced which is useful in identifying interconnections among the spike trains. This technique is based on the triplet-wise analysis of the Cox method. All these methods are applied to several sets of spike trains generated by the Enhanced Leaky and Integrate Fire (ELIF) model. The results suggest that these methods are successful for analysing functional connectivity of simultaneously recorded multiple spike trains. These methods are also applied to an experimental data recorded from cat’s visual cortex. The connection matrix derived from the experimental data by the Cox method is further applied to the graph theoretical methods.
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Sands, Caroline Jane. "Statistical correlation based methods for enhanced interpretation of, and information recovery from, NMR metabolic data sets." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/7061.

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Owing to its ability to capture a systemic and temporal metabolic description of an organism’s response to a treatment, metabonomics is a well-established and valuable approach in elucidating the effects and mechanisms of a given perturbation. However, to optimise information recovery from the complex datasets generated, chemometric methods are essential. The work presented in this thesis focuses on the development of novel methods, and the use of existing methods in new applications to ease data interpretation and enhance information recovery from 1H Nuclear Magnetic Resonance (NMR) metabonomic datasets using correlation based methods. Although the methods here are largely applied to toxicological data, they could be equally valuable in the analysis of any metabonomic dataset, and indeed potentially to other ‘omics’ data presenting similar analytical challenges. The first two methodological approaches relate to novel extensions of Statistical Total Correlation Spectroscopy (STOCSY), a valuable tool in elucidation of both inter- and intra-metabolite spectral intensity correlations in NMR metabonomic datasets. In the first, STOCSY is utilised in STOCSY-editing, a method for the selective identification and downscaling of the peaks from unwanted metabolites such as those arising from xenobiotics. Structurally correlated peaks from drug metabolites are first identified using STOCSY, and the returned correlation information utilised to scale the spectra across these regions, producing a modified set of spectra in which drug metabolite contributions are reduced, endogenous peaks reconstructed and thus, analysis by pattern recognition methods without drug metabolite interferences facilitated. In the second, the STOCSY approach is extended in Iterative-STOCSY, where metabolic associations are followed over several rounds of STOCSY through calculation of correlation coefficients initially from a driver spectral peak of interest, and subsequently from all peaks identified as correlating above a set threshold to peaks picked in the previous round. The condensation of putatively structurally related peaks into single nodes, and representation of the otherwise complex network in a fully interactive plot of node-to-node connections and corresponding spectral data, allows the ready exploration of both inter- and intrametabolite relationships and a more directed approach to the identification of biomarkers of the studied perturbation. Finally various clustering methods are investigated with the aim of providing improved structural (intra-metabolite) versus non-structural (inter-metabolite) assignment. Thus, this thesis presents a framework for the enhanced identification, recovery and characterisation of inter- and intrametabolite relationships and how these are affected by metabonomic perturbation.
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Joshi, Shirish. "Statistical analysis and validation procedures under the common random number correlation induction strategy for multipopulation simulation experiments." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-02132009-170935/.

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Manandhr-Shrestha, Nabin K. "Statistical Learning and Behrens Fisher Distribution Methods for Heteroscedastic Data in Microarray Analysis." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3513.

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The aim of the present study is to identify the di®erentially expressed genes be- tween two di®erent conditions and apply it in predicting the class of new samples using the microarray data. Microarray data analysis poses many challenges to the statis- ticians because of its high dimensionality and small sample size, dubbed as "small n large p problem". Microarray data has been extensively studied by many statisticians and geneticists. Generally, it is said to follow a normal distribution with equal vari- ances in two conditions, but it is not true in general. Since the number of replications is very small, the sample estimates of variances are not appropriate for the testing. Therefore, we have to consider the Bayesian approach to approximate the variances in two conditions. Because the number of genes to be tested is usually large and the test is to be repeated thousands of times, there is a multiplicity problem. To remove the defect arising from multiple comparison, we use the False Discovery Rate (FDR) correction. Applying the hypothesis test repeatedly gene by gene for several thousands of genes, there is a great chance of selecting false genes as di®erentially expressed, even though the signi¯cance level is set very small. For the test to be reliable, the probability of selecting true positive should be high. To control the false positive rate, we have applied the FDR correction, in which the p -values for each of the gene is compared with its corresponding threshold. A gene is, then, said to be di®erentially expressed if the p-value is less than the threshold. We have developed a new method of selecting informative genes based on the Bayesian Version of Behrens-Fisher distribution which assumes the unequal variances in two conditions. Since the assumption of equal variances fail in most of the situation and the equal variance is a special case of unequal variance, we have tried to solve the problem of ¯nding di®erentially expressed genes in the unequal variance cases. We have found that the developed method selects the actual expressed genes in the simulated data and compared this method with the recent methods such as Fox and Dimmic’s t-test method, Tusher and Tibshirani’s SAM method among others. The next step of this research is to check whether the genes selected by the pro- posed Behrens -Fisher method is useful for the classi¯cation of samples. Using the genes selected by the proposed method that combines the Behrens Fisher gene se- lection method with some other statistical learning methods, we have found better classi¯cation result. The reason behind it is the capability of selecting the genes based on the knowledge of prior and data. In the case of microarray data due to the small sample size and the large number of variables, the variances obtained by the sample is not reliable in the sense that it is not positive de¯nite and not invertible. So, we have derived the Bayesian version of the Behrens Fisher distribution to remove that insu±ciency. The e±ciency of this established method has been demonstrated by ap- plying them in three real microarray data and calculating the misclassi¯cation error rates on the corresponding test sets. Moreover, we have compared our result with some of the other popular methods, such as Nearest Shrunken Centroid and Support Vector Machines method, found in the literature. We have studied the classi¯cation performance of di®erent classi¯ers before and after taking the correlation between the genes. The classi¯cation performance of the classi¯er has been signi¯cantly improved once the correlation was accounted. The classi¯cation performance of di®erent classi¯ers have been measured by the misclas- si¯cation rates and the confusion matrix. The another problem in the multiple testing of large number of hypothesis is the correlation among the test statistics. we have taken the correlation between the test statistics into account. If there were no correlation, then it will not a®ect the shape of the normalized histogram of the test statistics. As shown by Efron, the degree of the correlation among the test statistics either widens or shrinks the tail of the histogram of the test statistics. Thus the usual rejection region as obtained by the signi¯cance level is not su±cient. The rejection region should be rede¯ned accordingly and depends on the degree of correlation. The e®ect of the correlation in selecting the appropriate rejection region have also been studied.
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Tansel, Icten. "Differentiation And Classification Of Counterfeit And Real Coins By Applying Statistical Methods." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614417/index.pdf.

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ABSTRACT DIFFERENTIATION AND CLASSIFICATION OF COUNTERFEIT AND REAL COINS BY APPLYING STATISTICAL METHODS Tansel, Iç
ten M.Sc, Archaeometry Graduate Program Supervisor : Assist. Prof. Dr. Zeynep Isil Kalaylioglu Co-Supervisor : Prof. Dr. Sahinde Demirci June 2012, 105 pages In this study, forty coins which were obtained from Museum of Anatolian Civilizations (MAC) in Ankara were investigated. Some of those coins were real (twenty two coins) and the remaining ones (eighteen coins) were fake coins. Forty coins were Greek coins which were dated back to middle of the fifth century BCE and reign of Alexander the Great (323 &ndash
336 BCE). The major aims of this study can be summarized as follow
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Mestres, Adrià Caballé. "Statistical methods for the testing and estimation of linear dependence structures on paired high-dimensional data : application to genomic data." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31331.

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This thesis provides novel methodology for statistical analysis of paired high-dimensional genomic data, with the aimto identify gene interactions specific to each group of samples as well as the gene connections that change between the two classes of observations. An example of such groups can be patients under two medical conditions, in which the estimation of gene interaction networks is relevant to biologists as part of discerning gene regulatory mechanisms that control a disease process like, for instance, cancer. We construct these interaction networks fromdata by considering the non-zero structure of correlationmatrices, which measure linear dependence between random variables, and their inversematrices, which are commonly known as precision matrices and determine linear conditional dependence instead. In this regard, we study three statistical problems related to the testing, single estimation and joint estimation of (conditional) dependence structures. Firstly, we develop hypothesis testingmethods to assess the equality of two correlation matrices, and also two correlation sub-matrices, corresponding to two classes of samples, and hence the equality of the underlying gene interaction networks. We consider statistics based on the average of squares, maximum and sum of exceedances of sample correlations, which are suitable for both independent and paired observations. We derive the limiting distributions for the test statistics where possible and, for practical needs, we present a permuted samples based approach to find their corresponding non-parametric distributions. Cases where such hypothesis testing presents enough evidence against the null hypothesis of equality of two correlation matrices give rise to the problem of estimating two correlation (or precision) matrices. However, before that we address the statistical problem of estimating conditional dependence between random variables in a single class of samples when data are high-dimensional, which is the second topic of the thesis. We study the graphical lasso method which employs an L1 penalized likelihood expression to estimate the precision matrix and its underlying non-zero graph structure. The lasso penalization termis given by the L1 normof the precisionmatrix elements scaled by a regularization parameter, which determines the trade-off between sparsity of the graph and fit to the data, and its selection is our main focus of investigation. We propose several procedures to select the regularization parameter in the graphical lasso optimization problem that rely on network characteristics such as clustering or connectivity of the graph. Thirdly, we address the more general problem of estimating two precision matrices that are expected to be similar, when datasets are dependent, focusing on the particular case of paired observations. We propose a new method to estimate these precision matrices simultaneously, a weighted fused graphical lasso estimator. The analogous joint estimation method concerning two regression coefficient matrices, which we call weighted fused regression lasso, is also developed in this thesis under the same paired and high-dimensional setting. The two joint estimators maximize penalized marginal log likelihood functions, which encourage both sparsity and similarity in the estimated matrices, and that are solved using an alternating direction method of multipliers (ADMM) algorithm. Sparsity and similarity of thematrices are determined by two tuning parameters and we propose to choose them by controlling the corresponding average error rates related to the expected number of false positive edges in the estimated conditional dependence networks. These testing and estimation methods are implemented within the R package ldstatsHD, and are applied to a comprehensive range of simulated data sets as well as to high-dimensional real case studies of genomic data. We employ testing approaches with the purpose of discovering pathway lists of genes that present significantly different correlation matrices on healthy and unhealthy (e.g., tumor) samples. Besides, we use hypothesis testing problems on correlation sub-matrices to reduce the number of genes for estimation. The proposed joint estimation methods are then considered to find gene interactions that are common between medical conditions as well as interactions that vary in the presence of unhealthy tissues.
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Carlén, Emma. "Genetic evaluation of clinical mastitis in dairy cattle /." Uppsala : Dept. of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 2008. http://epsilon.slu.se/200863.pdf.

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Books on the topic "Stratagraphic correlation - Statistical methods"

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A, Turovsky A., and Zaikov Gennadiĭ Efremovich, eds. Correlation analysis in chemistry of solutions. Utrecht: VSP, 2004.

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Selected aspects of theoretical physics and organic chemistry. Hauppauge, N.Y: Nova Science Publishers, 2010.

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G, Piersol Allan, ed. Engineering applications of correlation and spectral analysis. 2nd ed. New York: Wiley, 1993.

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Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. Madison, Wis: University of Wisconsin Press, 1993.

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Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. Madison, Wis: University of Wisconsin Press, 1994.

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Distributions of correlation coefficients. New York: Springer-Verlag, 1989.

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V, Lopatin D., and Solovʹev V. V, eds. Korreli͡a︡t͡s︡ii͡a︡ geofizicheskikh poleĭ. Moskva: IZMIRAN, 1991.

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Driscoll, John C. Spatial correlations in panel data. [Washington, D.C.]: World Bank, Policy Research Dept., Macroeconomics and Growth Division, 1995.

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Ray, Indrajit. Correlation, coalitions, and strategic games. Louvain-la-Neuve: CIACO, 1996.

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Serkerov, S. A. Korreli͡a︡t͡s︡ionnye metody analiza v gravirazvedke i magnitorazvedke. Moskva: "Nedra", 1986.

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Book chapters on the topic "Stratagraphic correlation - Statistical methods"

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Rayat, Charan Singh. "Correlation." In Statistical Methods in Medical Research, 61–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0827-7_8.

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Alvo, Mayer, and Philip L. H. Yu. "Correlation Correlation Analysis of Paired Ranking Data." In Statistical Methods for Ranking Data, 23–53. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1471-5_3.

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Berry, Kenneth J., Kenneth L. Kvamme, Janis E. Johnston, and Paul W. Mielke,. "Correlation and Association." In Permutation Statistical Methods with R, 499–590. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74361-1_10.

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Lee, Hang. "Linear Correlation and Regression." In Foundations of Applied Statistical Methods, 87–104. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02402-8_5.

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Berry, Kenneth J., Janis E. Johnston, and Paul W. Mielke. "Correlation and Regression." In A Primer of Permutation Statistical Methods, 361–407. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20933-9_10.

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Severini, Thomas A. "Using Correlation to Detect Statistical Relationships." In Analytic Methods in Sports, 121–59. 2nd edition. | Boca Raton, Florida : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780367252090-5.

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Selvamuthu, Dharmaraja, and Dipayan Das. "Analysis of Correlation and Regression." In Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control, 193–222. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1736-1_6.

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Reddy, M. Venkataswamy. "Correlation Analysis and Regression Analysis." In Statistical Methods in Psychiatry Research and SPSS, 163–76. Second edition. | Toronto ; New Jersey : Apple Academic Press, 2018.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-12.

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Wieneke, B., and R. Prevost. "DIC Uncertainty Estimation from Statistical Analysis of Correlation Values." In Advancement of Optical Methods in Experimental Mechanics, Volume 3, 125–36. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00768-7_15.

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Cocozza, Rosa, and Antonio De Simone. "Bifactorial Pricing Models: Light and Shadows in Correlation Role." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 99–110. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02499-8_9.

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Conference papers on the topic "Stratagraphic correlation - Statistical methods"

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Tikkanen, Jeff, Sebastian Siatkowski, Nik Sumikawa, Li-C. Wang, and Magdy S. Abadir. "Yield optimization using advanced statistical correlation methods." In 2014 IEEE International Test Conference (ITC). IEEE, 2014. http://dx.doi.org/10.1109/test.2014.7035326.

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Yang, Ting, Guihai Guo, and Yijin Chen. "Application of Statistical Methods: To Analyze Correlation of VRS Errors." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5365869.

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Shi, Xiangqiong, and Dan Schonfeld. "Video Classification and Mining Based on Statistical Methods for Cross-Correlation Analysis." In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/ssp.2007.4301326.

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Relles, Jacob, Muhua Ngan, E. Tlelo-Cuautle, Sheldon X. D. Tan, Chao Hu, Wenjian Yu, and Yici Cai. "Statistical extraction and modeling of 3-D inductance with spatial correlation." In 2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD). IEEE, 2010. http://dx.doi.org/10.1109/sm2acd.2010.5672360.

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Borghetti, Alberto, Carlo Alberto Nucci, Mario Paolone, and Marina Bernardi. "A Statistical Approach for Estimating the Correlation between Lightning and Faults in Power Distribution Systems." In 2006 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2006. http://dx.doi.org/10.1109/pmaps.2006.360211.

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Smyth, Mary, and Kevin McCormack. "Big Data Matching Using the Identity Correlation Approach." In CARMA 2016 - 1st International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/carma2016.2016.2991.

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Abstract The Identity Correlation Approach (ICA) is a statistical technique developed for matching big data where a unique identifier does not exist. This technique was developed to match the Irish Census 2011 dataset to Central Government Administrative Datasets in order to attach a unique identifier to each individual person in the Census dataset (McCormack & Smyth, 20151). The unique identifier attached is the PPS No. (Personal Public Service No.2). By attaching the PPS No. to the Census dataset, each individual can be linked to datasets held centrally by Public Sector Organisations. This expands the range of variables for statistical analysis at individual level. Statistical techniques developed here were undertaken for a major European Structure of Earnings Survey (SES) compiled by the CSO using administrative data only, and thus eliminating the need for an expensive business survey to be conducted (NES, 20073,4,5). A description of how the Identity Correlation Approach was developed is given in this paper. Data matching results and conclusions are presented here in relation to the Structure of Earnings Survey (SES)6 results for 2011.
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Nezym, Vitaliy Yu, and Georgiy P. Polupan. "A New Statistical-Based Correlation for the Compressor Tandem Cascade Parameters Effects on the Loss Coefficient." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27245.

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One of the effective methods for improvement of compressor stage operation is a flow control within the compressor passages. An application of boundary layer control on the suction surface of a blade profile may be useful in tandem cascade configuration. Total tandem guide vane consists of a series of two vanes: guide vane (the first segment) and straightening vane (the rear segment). These vanes have a definite shift along the axial and peripheral direction, providing a perfect re-distributing of energy within the blade passages between different flow zones. There are several parameters influencing mainly on loss in a tandem cascade. Experimental results of the parametric test are used for the creation of a statistical model of loss determination of compressor tandem cascade using the Group Method of Data Handling (GMDH). The paper focuses on the statistical-based correlation that encompasses most pertinent parameters featuring tandem configuration and operating condition.
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Antinori, Giulia, Yannick Muller, Fabian Duddeck, and Andreas Fischersworring-Bunk. "Statistical Methods for a Stochastic Analysis of the Secondary Air System of a Jet Engine Low Pressure Turbine." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94881.

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In this paper several stochastic methods are evaluated with respect to their applicability for the analysis of fluid networks. The methods are applied for the analysis of a 1D flow model of the Secondary Air System (SAS) of a three stages low pressure turbine (LPT) of a jet engine. The stochastic analysis is comprised of a sensitivity analysis followed by an uncertainty analysis. The sensitivity analysis is performed to gain a better understanding of the SAS physics and robustness, to identify the important variables and to reduce the number of parameters involved in the simulations for the uncertainty analysis. The uncertainty analysis, using probability distributions derived from the manufacturing process, allows to determine the effect of the input uncertainties on responses such as pressures, fluid temperatures and mass flow rates. A review of the most common and relevant sampling methods is performed. A comparison of the respective computational cost and of the sample points distribution is proposed with the aim of finding the most suited method. The study shows that some of the sampling methods can not be recommended since they produce spurious correlations between independent input variables. With regards to the sensitivity analysis, many literature sources state that the Pearson correlation method is only valid for linear models when assessing the importance of input variables. As the SAS is highly non-linear, non-parametric variance based methods are introduced here to make up for the limitations of the correlation method. Following the results of the study, it is recommended to combine the sampling method with a non-parametric variance based method. Thus, the main effects as well as all the interactions among variables are captured.
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Rhudy, Matthew, Brian Bucci, Jeffrey Vipperman, Jeffrey Allanach, and Bruce Abraham. "Microphone Array Analysis Methods Using Cross-Correlations." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10798.

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Due to civilian noise complaints and damage claims, there is a need to establish an accurate record of impulse noise generated at military installations. Current noise monitoring systems are susceptible to false positive detection of impulse events due to wind noise. In order to analyze the characteristics of noise events, multiple channel data methods were investigated. A microphone array was used to collect four channel data of military impulse noise and wind noise. These data were then analyzed using cross-correlation functions to characterize the input waveforms. Four different analyses of microphone array data are presented. A new value, the min peak correlation coefficient, is defined as a measure of the likelihood that a given waveform originated from a correlated noise source. Using a sound source localization technique, the angle of incidence of the noise source can be calculated. A method was also developed to combine the four individual microphone channels into one. This method aimed to preserve the correlated part of the overall signal, while minimizing the effects of uncorrelated noise, such as wind. Lastly, a statistical method called the acoustic likelihood test is presented as a method of determining if a signal is correlated or not.
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Niedźwiedzka, Agnieszka, and Seweryn Lipiński. "Statistical methods of images analysis as an essential tool in the assessment process of computer methods intended for numerical simulations of cavitating flows." In ILASS2017 - 28th European Conference on Liquid Atomization and Spray Systems. Valencia: Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/ilass2017.2017.4993.

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Cavitation, because of its negative effects, like e.g. erosion, noise or vibration, is usually an undesirablephenomenon. However, in devices where spraying and atomization are expected, cavitation is required. This group of devices includes e.g. diesel injectors. Appearance of vapour bubbles results in increase of the maximum flow velocity. It is possible for the following reasons. Firstly, bubbles start to form in the throat, so its diameter reduces. Secondly, appearance of vapour bubbles along the wall results in a slip boundary condition. Moreover, cavitation has a positive influence on a spray cone angle. However, regardless of the place of occurrence, research on cavitation bases primarily on numerical simulations. The area of numerical methods intended for cavitating flows includes many solutions which differ not only in the basic assumptions, i.e. considering flow either as a multiphase mixture with the average density or just as two independent liquids, fluid and vapour, with a distinct boundary between them, but also in many methods applied in particular approaches. Currently, to choose the best way of the prediction of cavitation phenomenon for the undertaken issue, many aspects should be considered. The most important factor is the assessment level between the results of numerical simulation and experimental data. Secondary are computing time, requirements for the hardware, price of software and additional costs connected with the selected software. The final decision about the chosen way of the cavitation prediction results from all the above-considered elements. The main aim of the work is to present the methods of image analysis, which can be very helpful in this process. The main advantage of these methods is the quantitative answer about the correlation degree between analysed images. It eliminates subjective decisions based solely on a raw imaging material. The image material used in the work was obtained via numerical simulations performed in ANSYS Fluent. Presented methodology bases on their statistical analysis that considers the shape and intensity of cavitating area, as well as on basic methods of image processing and analysis. The conclusion is that the obtained results demonstrate the usefulness of the proposed methods in the aspect of a reliable comparison of images obtained in the numericalstudies of the cavitation phenomenon.DOI: http://dx.doi.org/10.4995/ILASS2017.2017.4993
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