| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and indexes. |
| Summary |
"Reviews local dependence modeling, with applications to time series and finance markets Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences Integrates textual content with three useful R packages"-- Provided by publisher. |
| Note |
Description based on online resource; title from digital title page (viewed on October 22, 2021). |
| Contents |
4.4 Limit theorems -- 4.4.1 Asymptotic theory for b fixed -- 4.4.2 Asymptotic theory as b -> -- 0 -- 4.5 Properties -- 4.5.1 Some general properties of the local Gaussian correlation -- 4.5.2 Independence and functional dependence -- 4.5.3 The Rényi criteria -- 4.5.4 Linear transformation and symmetries -- 4.6 Examples -- 4.6.1 Simulated examples -- 4.6.2 A real-data example -- 4.7 Transforming the marginals: Normalized local correlation -- 4.7.1 Examples of the normalized LGC -- 4.8 Some practical considerations -- 4.8.1 Estimating the standard error of estimates -- 4.8.2 Choosing the bandwidth -- 4.9 The p-dimensional case -- 4.10 Proof of asymptotic results -- 4.10.1 Non-normalized -- 4.10.2 Normalized -- 4.10.3 Proof of the linear transformation result -- References -- 5 Local Gaussian correlation and the copula -- 5.1 Introduction -- 5.2 Local Gaussian correlation for copula models -- 5.2.1 Tail behavior -- 5.2.2 Normalized local Gaussian correlation -- 5.3 Examples -- 5.3.1 Archimedean copulas -- 5.3.2 Elliptical copulas -- 5.4 Recognizing copulas by goodness-of-fit -- 5.4.1 Uniform pseudo-observations -- 5.4.2 Gaussian pseudo-observations -- 5.4.3 A goodness-of-fit test based on local Gaussian correlation -- 5.4.4 Choice of bandwidth -- 5.4.5 Simulation study -- 5.4.6 Visualizing departures from H0 -- 5.5 A real-data study -- References -- 6 Applications in finance -- 6.1 Introduction -- 6.2 Conditional correlation and the bias problem -- 6.2.1 Why local Gaussian correlation is better -- 6.3 Empirical analysis of dependence of financial returns -- 6.3.1 Daily stock index returns -- 6.3.2 Monthly stock index returns -- 6.3.3 Dependence between commodities, bonds, and stocks -- 6.4 The portfolio allocation problem -- 6.4.1 Portfolio allocation using the LGC -- 6.4.2 Empirical example -- 6.5 Financial contagion. |
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8.3.3 Sanity testing the implemented estimation algorithm -- 8.3.3.1 Gaussian white noise -- 8.3.3.2 Some trigonometric examples -- 8.3.4 Real data and a fitted GARCH-type model -- 8.3.4.1 The real data example -- 8.3.4.2 A heatmap/distance plot for the dmbp-data -- 8.3.4.3 A GARCH-type model -- 8.3.4.4 Local testing of fitted models -- References -- 9 Multivariate density estimation -- 9.1 Introduction -- 9.2 Description of the estimator -- 9.2.1 Estimation of the marginals -- 9.2.2 Estimation of the joint dependence function -- 9.3 Asymptotic theory -- 9.4 Bandwidth selection -- 9.5 An example -- 9.6 Investigating performance in the multivariate case -- 9.7 A more flexible version of the LGDE -- 9.8 Proofs -- 9.8.1 Proof of Theorem 9.1 -- 9.8.2 Proof of Theorems 9.2 and 9.3 -- 9.8.3 Proof of Theorem 9.4 -- References -- 10 Conditional density estimation -- 10.1 Introduction -- 10.2 Estimating the conditional density -- 10.3 Asymptotic theory for dependent data -- 10.4 Examples -- 10.4.1 Stock data -- 10.4.2 Simulations -- 10.4.2.1 Simulated data with relevant variables -- 10.4.3 Simulated data from a heavy-tailed distribution -- 10.4.4 Simulated data with irrelevant variables -- 10.5 Proof of theorems -- 10.5.1 Proof of Theorem 10.1 -- 10.5.2 Proof of Theorem 10.2 -- References -- 11 The local Gaussian partial correlation -- 11.1 Introduction -- 11.2 The local Gaussian partial correlation -- 11.2.1 Definition -- 11.3 Properties -- 11.4 Estimation of the LGPC by local likelihood -- 11.4.1 Estimation of R(z) when p = 3 and X(2) is a scalar -- 11.4.2 Estimation of R(z) when X(2) is a vector -- 11.5 Asymptotic theory -- 11.6 Examples -- 11.7 Testing for conditional independence -- 11.7.1 The recent fauna of nonparametric tests -- 11.7.2 A test for conditional independence -- 11.7.3 Comparing with other tests -- 11.8 The multivariate LGPC. |
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11.8.1 Definition -- 11.8.2 Some particular cases -- References -- 12 Regression and conditional regression quantiles -- 12.1 Introduction -- 12.2 Comparison with additive regression modeling -- 12.3 Local Gaussian regression estimation -- 12.4 Asymptotic normality -- 12.5 Example -- 12.6 Conditional quantiles -- 12.6.1 Distribution of the conditional empirical distribution function -- 12.6.2 Convergence rate -- 12.6.3 Distribution of conditional quantiles -- 12.7 Proof -- References -- 13 A local Gaussian Fisher discriminant -- 13.1 Introduction -- 13.1.1 Background -- 13.1.2 Estimating densities and discriminants -- 13.2 A local Gaussian Fisher discriminant -- 13.3 Some asymptotics of Bayes risk -- 13.4 Choice of bandwidth -- 13.5 Illustrations -- 13.5.1 Simulations -- 13.5.2 Illustration: Fraud detection -- 13.6 Summary remark -- References -- Author index -- Subject index -- Back Cover. |
| Subject |
Statistics.
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Mathematical models.
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Gaussian distribution.
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Approximation theory.
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Modèles mathématiques.
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Loi de Gauss (Statistique)
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Théorie de l'approximation.
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Statistique.
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mathematical models.
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statistics.
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Approximation theory
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Gaussian distribution
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Mathematical models
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Statistics
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| Added Author |
Otneim, Håkon, author.
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Stove, Bård, author.
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| Other Form: |
Print version: 0128158611 9780128158616 (OCoLC)1245659327 |
| ISBN |
9780128154458 (electronic bk.) |
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0128154454 (electronic bk.) |
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9780128158616 (electronic bk.) |
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0128158611 (electronic bk.) |
| Standard No. |
AU@ 000070477188 |
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