Titlebook: Copula-Based Markov Models for Time Series; Parametric Inference Li-Hsien Sun,Xin-Wei Huang,Takeshi Emura Book 2020 The Editor(s) (if appli

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Book 2020imum 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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Writing Assignments: Where Writing Beginsmodels, bivariate copula functions such as the bivariate Gaussian, Frank, and Gumbel are chosen to construct a bivariate distribution of two consecutive observations. Moreover, the trivariate Gaussian and max-infinitely divisible copula functions are considered to build the joint distribution of thr

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https://doi.org/10.1007/978-981-15-4998-4Copula; Maximum Likelihood Estimator; Serial Correlation; Markov Chain; Serial Dependence; Statistical Pr

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978-981-15-4997-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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SpringerBriefs in Statisticshttp://image.papertrans.cn/c/image/238182.jpg

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Copula-Based Markov Models for Time Series978-981-15-4998-4Series ISSN 2191-544X Series E-ISSN 2191-5458

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White Passage and Black Pedagogy introduce five datasets, namely, the chemical process data, S&P 500 stock market index data, the batting average data in MLB, the stock price data of Dow Jones Industrial Average, and data on the number of arsons.

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Overview of the Book with Data Examples, introduce five datasets, namely, the chemical process data, S&P 500 stock market index data, the batting average data in MLB, the stock price data of Dow Jones Industrial Average, and data on the number of arsons.