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Добірка наукової літератури з теми "Statistica bivariata"

Автор: Grafiati
Опубліковано: 10 березня 2023

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Статті в журналах з теми "Statistica bivariata"

1

Sinčić, Marko, Sanja Bernat Gazibara, Martin Krkač, and Snježana Mihalić Arbanas. "Landslide susceptibility assessment of the City of Karlovac using the bivariate statistical analysis." Rudarsko-geološko-naftni zbornik 38, no. 2 (2022): 149–70. http://dx.doi.org/10.17794/rgn.2022.2.13.

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A preliminary landslide susceptibility analysis on a regional scale of 1:100 000 using bivariate statistics was conducted for the City of Karlovac. The City administration compiled landslide inventory used in the analysis based on recorded landslides from 2014 to 2019 that caused significant damage to buildings or infrastructures. Analyses included 17 geofactors relevant to landslide occurrence and classified them into four groups: geomorphological (elevation, slope gradient, slope orientation, terrain curvature, terrain roughness), geological (lithology-rock type, proximity to geological contacts, proximity to faults), hydrological (proximity to drainage network, proximity to springs, proximity to temporary, permanent and to all streams, topographic wetness) and anthropogenic (proximity to traffic infrastructure, land cover using two classifications). Five scenarios were defined using a different combination of geofactors weighted by the Weights-of-Evidence (WoE) method, resulting in five different landslide susceptibility maps. The best landslide susceptibility map was selected upon the results of a ROC curve analysis, which was used to obtain success and prediction rates of each scenario. The novelty in the presented research is that a limited amount of thematic data and an incomplete landslide inventory map allows for the production of a preliminary landslide susceptibility map for usage in spatial planning. Also, this study provides a discussion regarding the used method, geofactors, defined scenarios and reliability of the results. The final preliminary landslide susceptibility map was derived using ten geofactors, which satisfied the pairwise CI test, and it is classified in four zones: low landslide susceptibility (57.05% of the area), medium landslide susceptibility (20.63% of the area), high landslide susceptibility (13.28% of the area), and very high landslide susceptibility (9.03% of the area), and has a success rate of 94% and a prediction rate of 93% making it a highly accurate source of preliminary information for the study area.
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2

Fiori, Simone. "Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling." Computational Intelligence and Neuroscience 2007 (2007): 1–23. http://dx.doi.org/10.1155/2007/71859.

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Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are “holes” in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure.
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3

Chen, Zhenmin, and Tieyong Hu. "Statistical Test for Bivariate Uniformity." Advances in Statistics 2014 (October 19, 2014): 1–6. http://dx.doi.org/10.1155/2014/740831.

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The purpose of the multidimension uniformity test is to check whether the underlying probability distribution of a multidimensional population differs from the multidimensional uniform distribution. The multidimensional uniformity test has applications in various fields such as biology, astronomy, and computer science. Such a test, however, has received less attention in the literature compared with the univariate case. A new test statistic for checking multidimensional uniformity is proposed in this paper. Some important properties of the proposed test statistic are discussed. As a special case, the bivariate statistic test is discussed in detail in this paper. The Monte Carlo simulation is used to compare the power of the newly proposed test with the distance-to-boundary test, which is a recently published statistical test for multidimensional uniformity. It has been shown that the test proposed in this paper is more powerful than the distance-to-boundary test in some cases.
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4

de Bairros, Thiago, Pedro Pereira, Rausley de Souza, and Michel Yacoub. "Bivariate Complex $\alpha$-$\mu$ Statistics." IEEE Transactions on Vehicular Technology 71, no. 3 (March 2022): 3276–80. http://dx.doi.org/10.1109/tvt.2022.3141232.

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5

Sari, Meylita, Sutikno, and Purhadi. "Parameter estimation and hypothesis testing of geographically and temporally weighted bivariate Poisson inverse Gaussian regression model." IOP Conference Series: Earth and Environmental Science 880, no. 1 (October 1, 2021): 012045. http://dx.doi.org/10.1088/1755-1315/880/1/012045.

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Abstract One of the appropriate methods used to model count data response and its corresponding predictors is Poisson regression. Poisson regression strictly assumes that the mean and variance of response variables should be equal (equidispersion). Nonetheless, some cases of the count data unsatisfied this assumption because variance can be larger than mean (over-dispersion). If overdispersion is violated, causing the underestimate standard error. Furthermore, this will lead to incorrect conclusions in the statistical test. Thus, a suitable method for modelling this kind of data needs to develop. One alternative model to outcome the overdispersion issue in bivariate response variable is the Bivariate Poisson Inverse Gaussian Regression (BPIGR) model. The BPIGR model can produce a global model for all locations. On the other hand, each location and time have different geographic conditions, social, cultural, and economical so that Geographically and Temporally Bivariate Poisson Inverse Gaussian Regression (GTWBPIGR)) is needed. The weighting function spatial-temporal in GTWBPIGR generates a different local model for each period. GTWBPIGR model solves the overdispersion case and generates global models for each period and location. The parameter estimation of the GTWBPIGR model uses the Maximum Likelihood Estimation (MLE) method, followed by Newton Raphson iteration. Meanwhile, the test statistics on the hypothesis testing is simultaneously testing of the GTWBPIGR model is obtained with the Maximum Likelihood Ratio Test (MLRT) approach, using n large samples of the statistical test is chi-square distribution. Moreover, the test statistics for partially testing used the Z-test statistic.
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6

Abusev, R. A., and N. V. Kolegova. "On quadratic errors of statistical estimators of distributions of sufficient statistics of bivariate exponential distributions." Journal of Mathematical Sciences 126, no. 1 (March 2005): 1017–23. http://dx.doi.org/10.1007/s10958-005-0013-6.

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7

Purhadi, Purhadi, and M. Fathurahman. "A Logit Model for Bivariate Binary Responses." Symmetry 13, no. 2 (February 16, 2021): 326. http://dx.doi.org/10.3390/sym13020326.

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This article provides a bivariate binary logit model and statistical inference procedures for parameter estimation and hypothesis testing. The bivariate binary logit (BBL) model is an extension of the binary logit model that has two correlated binary responses. The BBL model responses were formed using a 2 × 2 contingency table, which follows a multinomial distribution. The maximum likelihood and Berndt–Hall–Hall–Hausman (BHHH) methods were used to obtain the BBL model. Hypothesis testing of the BBL model contains the simultaneous test and the partial test. The test statistics of the simultaneous test and the partial test were determined using the maximum likelihood ratio test method. The likelihood ratio statistics of the simultaneous test and the partial test were approximately asymptotically chi-square distributed with 3p degrees of freedom. The BBL model was applied to a real dataset, and the BBL model with the single covariate was better than the BBL model with multiple covariates.
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8

Packard, Gary C., Geoffrey F. Birchard, and Thomas J. Boardman. "Fitting statistical models in bivariate allometry." Biological Reviews 86, no. 3 (October 5, 2010): 549–63. http://dx.doi.org/10.1111/j.1469-185x.2010.00160.x.

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9

Barakat, H. M. "On Moments of Bivariate Order Statistics." Annals of the Institute of Statistical Mathematics 51, no. 2 (June 1999): 351–58. http://dx.doi.org/10.1023/a:1003818410698.

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10

Falk, Michael, and Florian Wisheckel. "Asymptotic independence of bivariate order statistics." Statistics & Probability Letters 125 (June 2017): 91–98. http://dx.doi.org/10.1016/j.spl.2017.01.020.

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Дисертації з теми "Statistica bivariata"

1

Haug, Mark. "Nonparametric density estimation for univariate and bivariate distributions with applications in discriminant analysis for the bivariate case." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9916.

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2

Liu, Yunfeng. "Tests of Bivariate Stochastic Order." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20257.

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The purpose of this thesis is to compare rank-based tests of bivariate stochastic order. Given two bivariate distributions $F$ and $G$, the general problem we are dealing with is to test $H_0: F=G$ against $H_1:F
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3

Onnen, Nathaniel J. "Estimation of Bivariate Spatial Data." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1616243660473062.

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4

Casanova, Gurrera María de los Desamparados. "Construction of Bivariate Distributions and Statistical Dependence Operations." Doctoral thesis, Universitat de Barcelona, 2005. http://hdl.handle.net/10803/1555.

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Dependence between random variables is studied at various levels in the first part, while the last two chapters are devoted to the construction of bivariate distributions via principal components. Chapter 1 of Preliminaries is devoted to general dependence concepts (Fréchet classes, copulas, and parametric families of distributions). In Chapter 2, we generalize the union and intersection operations of two distance matrices to symmetric nonnegative definite matrices. These operations are shown to be useful in the geometric interpretation of Related Metric Scaling (RMS ), and possibly in other approaches of Multivariate Analysis. They show relevant properties that are studied in this chapter. The behaviour of the operations is, in some way, analogous to that presented by the intersection and union between vector spaces; in particular, we prove that the intersection of orthogonal matrices is the null matrix, while the union is the direct sum of the matrices. Matrices that share their eigenvectors form an equivalence class, and a partial order relation is defined. This class is closed for the union and intersection operations. A continuous extension of these operations is presented in Chapter 3. Infinite matrices are studied in the context of bounded integral operators and numerical kernels. We put the basis for extending RMS to continuous random variables and, hence, infinite matrices. The starting point is Mercer's Theorem, which ensures the existence of an orthogonal expansion of the covariance kernel K (s, t) = min {F (s) , F (t)} - F (s) F (t), where F is the cumulative distribution function of each marginal variable. The sets of eigenvalues and eigenfunctions of K, whose existence is ensured by the cited theorem, allow us to define a product between symmetric and positive (semi)definite kernels, and, further, to define the intersection and the union between them. Results obtained in the discrete instance are extended in this chapter to continuous variables, with examples. Such covariance kernels (symmetric and positive definite) are associated with symmetric and positive quadrant dependent (PQD) bivariate distributions. Covariance between functions of bounded variation defined on the range of some random variables, joined by distributions of this type, can be computed by means of their cumulative distribution functions. In Chapter 4, further consequences are obtained, especially some relevant relations between the covariance and the Fréchet bounds, with a number of results that can be useful in the characterization of independence as well as in testing goodness-of-fit. The intersection of two kernels (defined in Chapter 3) is a particular instance of the covariance between functions. Covariance is a quasiinner product defined through the joint distribution of the variables involved. A measure of affinity between functions with respect to H is defined, and also studied. In Chapter 5, from the concept of affinity between functions via an extension of the covariance, we define the dimension of a distribution, we relate it to the diagonal expansion and find the dimension for some parametric families. Diagonal expansions of bivariate distributions (Lancaster) allows us to construct bivariate distributions. It has proved to be adequate for constructing Markov processes, and has also been applied to engineering problems among other uses. This method has been generalized using the principal dimensions of each marginal variable that are, by construction, canonical variables. We introduce in Chapter 6 the theoretical foundations of this method. In Chapter 7 we study the bivariate, symmetric families obtained when the marginals are Uniform on (0, 1), Exponential with mean 1, standard Logistic, and Pareto (3,1). Conditions for the bivariate density, first canonical correlation and maximum correlation of each family of densities are given in some cases. The corresponding copulas are obtained.
Al Capítol 1 de Preliminars es revisen conceptes de dependència generals (classes de Fréchet, còpules, i famílies paramètriques de distribucions). Al Capítol 2, generalitzem les operacions unió i intersecció de dues matrius de distàncies a matrius simètriques semidefinides positives qualssevol. Aquestes operacions s'han mostrat d'utilitat en la interpretació geomètrica del Related Metric Scaling (RMS), i possiblement en altres tècniques d'Anàlisi Multivariant. S'estudien llur propietats que són similars, en alguns aspectes, a les de la unió i intersecció de subespais vectorials. Al Capítol 3 es presenta una extensió al continuu d'aquestes operacions, mitjançant matrius infinites en el context dels operadors integrals acotats i nuclis numèrics. S'estableix la base per a extendre el RMS a variables contínues i, per tant, a matrius infinites. Es parteix del Teorema de Mercer el qual assegura l'existència d'una expansió ortogonal del nucli de la covariança K (s, t) = min {F (s), F (t)} - F (s) F (t), on F és la funció de distribució de cada variable marginal. Els conjunts de valors i funcions pròpies d'aquest nucli ens permeten definir un producte entre nuclis i la intersecció i unió entre nuclis simètrics semidefinits positius. Tals nuclis de covariança s'associen amb distribucions bivariants també simètriques i amb dependència quadrant positiva (PQD). El producte de dos nuclis és un cas particular de covariança entre funcions, que es pot obtenir a partir de les distribucions conjunta i marginals, com s'estudia al Capítol 4 per a funcions de variació afitada, fixada la distribució bivariant H. S'obtenen interessants relacions amb les cotes de Fréchet. Aquesta covariança entre funcions és un producte quasiescalar a l'espai de funcions de variació afitada i permet definir una mesura d'afinitat. Al Capítol 5 aquesta H-afinitat s'utilitza per definir la dimensió d'una distribució. Les components principals d'una variable (Capítol 6) s'utilitzen com a variables canòniques a l'expansió diagonal de Lancaster (Capítol 7 i últim) per a construïr distribucions bivariants amb marginals Uniformes al (0,1), Exponencial de mitjana 1, Logística estàndard, i Pareto (3,1). S'obtenen condicions per la densitat bivariant, correlacions canòniques i correlació màxima per cada família. S'obtenen les còpules corresponents.
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5

Baggs, Maria Geraldine E. "Properties of order statistics from bivariate exponential distributions /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487858417983938.

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6

Koen, Marthinus Christoffel. "The analysis of some bivariate astronomical time series." Master's thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/17341.

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Bibliography: pages 75-76.
In the first part of the thesis, a linear time domain transfer function is fitted to satellite observations of a variable galaxy, NGC5548. The transfer functions relate an input series (ultraviolet continuum flux) to an output series (emission line flux). The methodology for fitting transfer function is briefly described. The autocorrelation structure of the observations of NGC5548 in different electromagnetic spectral bands is investigated, and appropriate univariate autoregressive moving average models given. The results of extensive transfer function fitting using respectively the λ1337 and λ1350 continuum variations as input series, are presented. There is little evidence for a dead time in the response of the emission line variations which are presumed driven by the continuum. Part 2 of the thesis is devoted to the estimation of the lag between two irregularly spaced astronomical time series. Lag estimation methods which have been used in the astronomy literature are reviewed. Some problems are pointed out, particularly the influence of autocorrelation and non-stationarity of the series. If the two series can be modelled as random walks, both these problems can be dealt with efficiently. Maximum likelihood estimation of the random walk and measurement error variances, as well as the lag between the two series, is discussed. Large-sample properties of the estimators are derived. An efficient computational procedure for the likelihood which exploits the sparseness of the covariance matrix, is briefly described. Results are derived for two example data sets: the variations in the two gravitationally lensed images of a quasar, and brightness changes of the active galaxy NGC3783 in two different wavelengths. The thesis is concluded with a brief consideration of other analysis methods which appear interesting.
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7

He, Qinying. "Inference on correlation from incomplete bivariate samples." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180468775.

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8

Wang, Chunnan. "Analysis of a new bivariate distribution in reliability theory." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284128.

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Freund [1961] introduced a bivariate extension of the exponential distribution that provides a model in which the exponential residual lifetime of one component depends on the working status of another component. We define and study an extension of the Freund distribution in this dissertation. In the first chapter we define some basic concepts that are needed for later developments. We give the definition of the multivariate conditional hazard rate functions of a nonnegative absolutely continuous random vector and study a characterization of these functions in Section 1.1. Then we study some notions of aging: an increasing failure rate (IFR) distribution, a decreasing failure rate (DFR) distribution, an increasing failure rate average (IFRA) distribution, and a decreasing failure rate average (DFRA) distribution in Section 1.2. In Section 1.3 we study two concepts of multivariate dependence: association and positive quadrant dependence. In Chapter 2 we construct a shock model and the new bivariate distribution is the joint distribution of the resulting lifetimes. We explicitly compute the density function, survival function, moment generating function, marginal density functions and marginal survival functions. Also in this chapter, we study the correlation coefficient and other senses of positive dependence of the two random variables of the new bivariate distribution. Then we extend the new distribution to multivariate case. In Chapter 3 we study some aging properties. We obtain two results about the new distribution in n dimensions. The first result says that the marginal distributions of the new multivariate distribution have decreasing failure rate if the conditional hazard rates are decreasing and bounded above by 1. The second one concerns an (n-1 )-out-of-n system such that the joint distribution of the lifetimes of each component is the new distribution in n dimensions. It gives conditions on the parameters under which the system has an IFRA distribution. In Chapter 4 we develop some estimation procedure for the parameters a and b of the new bivariate distribution. We apply the method of moments and the maximum likelihood principle to estimate the parameters. We prove that the method of moments estimator is a consistent asymptotically normal estimator. Then we use Mathematica to run simulation and compare the method of moments estimator with the maximum likelihood estimator. We also compute the 95% confidence interval for a and b from the method of moments estimator. In the last chapter we study a stochastic ordering problem. We have two nonnegative n dimensional random vectors X and Y. We assume that X and Y have the same conditional hazard rates up to a certain level. We give a condition under which the two vectors X and Y are stochastically ordered.
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9

Murphy, Orla. "Copula-based tests of independence for bivariate discrete data." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117229.

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New statistics are proposed for testing the hypothesis that two non-continuous random variables are independent. These statistics, which lead to consistent tests, are Cramér–von Mises and Kolmogorov–Smirnov type functionals of the checkerboard copula. The power of the new tests is compared via simulation to those based on the Pearson chi-squared, likelihood ratio, and Zelterman statistics often used in this context. To study their power, data are generated from five families of bivariate distributions whose margins may be known or not. In all cases considered, the new tests are seen to be more powerful than the standard tests. The new tests and the Zelterman statistic maintain their levels when the data are sparse; as is well known, this is not the case for Pearson's chi-squared and the likelihood ratio test. On the basis of the results presented here, the new Cramér–von Mises statistics can be recommended to test the independence between two random variables in the presence of ties in the sample.
De nouvelles statistiques sont proposées pour tester l'indépendance de deux aléas non continus. Ces statistiques, qui mènent à des tests convergents, sont des fonctionnelles de type Cramér–von Mises et Kolmogorov–Smirnov de la copule en damier. La puissance des nouveaux tests est comparée par simulation à celle des tests fondés sur les statistiques du khi-deux de Pearson, du rapport des vraisemblances et de la statistique de Zelterman souvent utilisées dans ce contexte. Pour étudier leur puissance, on génère des données de cinq familles de lois bivariées dont les marges peuvent être connues ou non. Dans tous les cas considérés, les nouveaux tests s'avèrent plus puissants que les tests standard. À l'instar du test de Zelterman, les nouveaux tests maintiennent leur seuil lorsque les données sont clairsemées; comme on le sait, ce n'est pas le cas des tests du khi-deux de Pearson et du rapport des vraisemblances. À la lumière des résultats présentés ici, les nouvelles statistiques de Cramér–von Mises peuvent être recommandées pour tester l'indépendance entre deux aléas en présence d'ex æquo dans les données.
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Lin, Min. "Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321226.

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Книги з теми "Statistica bivariata"

1

Haung, Xin. Statistics of bivariate extreme values. Amsterdam: Thesis Publishers, 1992.

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2

Diekhoff, George. Statistics for the social and behavioral sciences: Univariate, bivariate, multivariate. Dubuque, IA: Wm. C. Brown Publishers, 1992.

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3

Diekhoff, George. Statistics for the social and behavioral sciences: Univariate, bivariate, and multivariate. IA: Wm. C. Brown, 1992.

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4

K, Kocherlakota, ed. Bivariate discrete distributions. New York: M. Dekker, 1992.

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5

Jacoby, William. Statistical Graphics for Univariate and Bivariate Data. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications Inc., 1997. http://dx.doi.org/10.4135/9781412985963.

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6

Applied statistics: From bivariate through multivariate techniques. Los Angeles: SAGE Publications, 2008.

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7

Applied statistics: From bivariate through multivariate techniques. 2nd ed. Thousand Oaks: SAGE Publications, 2013.

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8

Jacoby, William G. Statistical graphics for univariate and bivariate data. Thousand Oaks, Calif: Sage Publications, 1997.

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9

D, Lai C., ed. Continuous bivariate distributions, emphasising applications. Adelaide, South Australia: Rumsby Scientific Publishing, 1990.

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10

Statistical fundamentals: Using Microsoft Excel for univariate and bivariate analysis. Chesapeake: Watertree Press, 2016.

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Частини книг з теми "Statistica bivariata"

1

Trauth, Martin H. "Bivariate Statistics." In MATLAB® Recipes for Earth Sciences, 121–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_4.

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2

Trauth, Martin H. "Bivariate Statistics." In MATLAB® Recipes for Earth Sciences, 145–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38441-8_4.

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3

Trauth, Martin H. "Bivariate Statistics." In MATLAB® Recipes for Earth Sciences, 61–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72749-1_4.

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4

Trauth, Martin H. "Bivariate Statistics." In Springer Textbooks in Earth Sciences, Geography and Environment, 117–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07719-7_4.

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Trauth, Martin H. "Bivariate Statistics." In MATLAB® Recipes for Earth Sciences, 79–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12762-5_4.

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Trauth, Martin H. "Bivariate Statistics." In MATLAB® Recipes for Earth Sciences, 61–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-27984-9_4.

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Manderscheid, Katharina. "Bivariate Statistik." In Sozialwissenschaftliche Datenanalyse mit R, 95–115. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-15902-3_6.

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Hilgers, Ralf-Dieter, Peter Bauer, and Victor Scheiber. "Bivariate Statistik." In Einführung in die Medizinische Statistik, 27–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06858-8_3.

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Manderscheid, Katharina. "Bivariate Statistik." In Sozialwissenschaftliche Datenanalyse mit R, 91–107. Wiesbaden: VS Verlag für Sozialwissenschaften, 2012. http://dx.doi.org/10.1007/978-3-531-94185-1_6.

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Trauth, Martin H. "Bivariate Statistik." In MATLAB®-Rezepte für die Geowissenschaften, 155–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64357-0_4.

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Тези доповідей конференцій з теми "Statistica bivariata"

1

Roslan, Razira Aniza, Suriani Hassan, Khadizah Ghazali, Rubena Yusoff, and Darmesah Gabda. "Prediction of riverflow using bivariate extreme value distribution with composite likelihood approach." In The 5TH ISM INTERNATIONAL STATISTICAL CONFERENCE 2021 (ISM-V): Statistics in the Spotlight: Navigating the New Norm. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0111260.

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2

Naess, Arvid, and Oleh Karpa. "Statistics of Extreme Wind Speeds and Wave Heights by the Bivariate ACER Method." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10760.

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Анотація:
In the reliability engineering and design of offshore structures probabilistic approaches are frequently adopted. They require the estimation of extreme quantiles of oceanographic data based on the statistical information. Due to strong correlation between such random variables as e.g. wave heights and wind speeds, application of the multivariate, or bivariate in the simplest case, extreme value theory is sometimes necessary. The paper focuses on the extension of the ACER method for prediction of extreme value statistics to the case of bivariate time series. Using the ACER method it is possible to provide an estimate of the exact extreme value distribution of a univariate time series. This is obtained by introducing a cascade of conditioning approximations to the exact extreme value distribution. When this cascade has converged, an estimate of the exact distribution has been obtained. In this paper it will be shown how the univariate ACER method can be extended in a natural way to also cover the case of bivariate data. Application of the bivariate ACER method will also be demonstrated at the measured coupled wind speed and wave height data.
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3

Flamant, Julien, Pierre Chainais, and Nicolas Le Bihan. "Linear Filtering of Bivariate Signals Using Quaternions." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450687.

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4

Lestari, Tika, Khreshna Syuhada, and Utriweni Mukhaiyar. "Bivariate control chart with copula." In 1ST INTERNATIONAL CONFERENCE ON ACTUARIAL SCIENCE AND STATISTICS (ICASS 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4936445.

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5

Rehman, N., D. Looney, T. M. Rutkowski, and D. P. Mandic. "Bivariate EMD-based image fusion." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278639.

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6

Repko, A., P. H. A. J. M. Van Gelder, H. G. Voortman, and J. K. Vrijling. "Bivariate Statistical Analysis of Wave Climates." In 27th International Conference on Coastal Engineering (ICCE). Reston, VA: American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40549(276)46.

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7

Vyas, Reeta. "Fluctuation properties of intracavity second-harmonic generation." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.wq5.

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Fluctuation properties of the fundamental light beam in intracavity second harmonic generation (ISHG) are studied theoretically. It is shown that the field produced by the ISHG can be described as a superposition of a coherent component and bivariate Gaussian noise field. Using this property of the ISHG field a generating function for photon counting statistics is obtained. From this generating function various expressions for quantities characterizing statistical properties such as photon counting distribution, factorial moments, and statistics of times are calculated. Curves are presented to illustrate the behavior. These results for the ISHG are also compared with those obtained for the optical parametric oscillators.
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8

Lefevre, Jeanne, Nicolas Le Bihan, and Pierre-Olivier Amblard. "A Geometrical Study of the Bivariate Fractional Gaussian Noise." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450737.

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9

Amin, Nor Azrita Mohd, Mohd Bakri Adam, Noor Akma Ibrahim, and Ahmad Zaharin Aris. "Bivariate generalized Pareto distribution for extreme atmospheric particulate matter." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907445.

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10

Torres-Avilés, F., C. Molina, and M. J. Muñoz. "Bayesian approaches for Poisson models to estimate bivariate relative risks." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759618.

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Звіти організацій з теми "Statistica bivariata"

1

Lo, Gane Samb. How to use the functional empirical process for deriving asymptotic laws for functions of the sample. Arxiv, July 2016. http://dx.doi.org/10.16929/hs/imhotep.2016.x.001.

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Анотація:
The functional empirical process is a very powerful tool for deriving asymptotic laws for almost any kind of statistics whenever we know how to express them into functions of the sample. Since this method seems to be applied more and more in the very recent future, this paper is intended to provide a complete but short description and justification of the method and to illustrate it with a non-trivial example using bivariate data. It may also serve for citation without repeating the arguments
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2

Sánchez-Páez, David A. Effects of income inequality on COVID-19 infections and deaths during the first wave of the pandemic: Evidence from European countries. Verlag der Österreichischen Akademie der Wissenschaften, August 2021. http://dx.doi.org/10.1553/populationyearbook2022.res1.1.

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Анотація:
Evidence from research on infectious diseases suggests that income inequality is related to higher rates of infection and death in disadvantaged population groups. Our objective is to examine whether there was an association between income inequality and the numbers of cases and deaths during the first wave of the COVID- 19 pandemic in European countries. We determined the duration of the first wave by first smoothing the number of daily cases, and then using a LOESS regression to fit the smoothed trend. Next, we estimated quasi-Poisson regressions. Results from the bivariate models suggest there was a moderate positive association between the Gini index values and the cumulated number of infections and deaths during the first wave, although the statistical significance of this association disappeared when controls were included. Results from multivariate models suggest that higher numbers of infections and deaths from COVID-19 were associated with countries having more essential workers, larger elderly populations and lower health care capacities.
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