Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends the Gaussian distribution to non-Gaussian and nonlinear situations, enabling new methods in assessing dependence, estimating probability, and spectral density functions, and discrimination.

\n Format: Paperback / softback
\n Length: 458 pages
\n Publication date: 08 October 2021
\n Publisher: Elsevier Science Publishing Co Inc
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Statistical Modeling using Local Gaussian Approximation is a powerful technique that extends the well-known Gaussian distribution to a wide range of non-Gaussian and nonlinear situations. By employing local approximation, it enables the reader to explore new methods for assessing dependence and conditional dependence, estimating probability and spectral density functions, and performing discrimination tasks. This release includes chapters covering various topics such as parametric, nonparametric, locally parametric, dependence, local Gaussian correlation and dependence, local Gaussian correlation and the copula, applications in finance, and more. Additionally, there are chapters dedicated to measuring dependence and testing for independence, time series dependence and spectral analysis, multivariate density estimation, conditional density estimation, the local Gaussian partial correlation, regression and conditional regression quantiles, and a local Gaussian Fisher discriminant.

This technique allows for the approximation of non-Gaussian distributions by using a combination of Gaussian functions, which can be useful in many applications. For example, it can be used to model complex phenomena such as financial data, where the distribution of returns is not Gaussian. Local Gaussian Approximation also provides a flexible framework for analyzing data, as it allows for the specification of different types of priors and likelihood functions. This flexibility enables researchers to tailor the model to their specific data and research objectives. Overall, Statistical Modeling using Local Gaussian Approximation is a valuable tool for researchers and practitioners in fields such as statistics, economics, and computer science, as it enables them to analyze and model non-Gaussian data more effectively.

\n Weight: 450g\n
Dimension: 229 x 152 (mm)\n
ISBN-13: 9780128158616\n \n