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This book provides statistical methodologies for time series data,
focusing on copula-based Markov chain models for serially
correlated time series. It also includes data examples from
economics, engineering, finance, sport and other disciplines to
illustrate the methods presented. An accessible textbook for
students in the fields of economics, management, mathematics,
statistics, and related fields wanting to gain insights into the
statistical analysis of time series data using copulas, the book
also features stand-alone chapters to appeal to researchers. As the
subtitle suggests, the book highlights parametric models based on
normal distribution, t-distribution, normal mixture distribution,
Poisson distribution, and others. Presenting likelihood-based
methods as the main statistical tools for fitting the models, the
book details the development of computing techniques to find the
maximum likelihood estimator. It also addresses statistical process
control, as well as Bayesian and regression methods. Lastly, to
help readers analyze their data, it provides computer codes (R
codes) for most of the statistical methods.
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