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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Estimation Under Normal Mixture Models for Financial Time Series Data,assets, we select a normal mixture distribution for the marginal distribution. Based on the normal mixture distribution for the marginal distribution and the Clayton copula for serial dependence, we obtain the corresponding likelihood function. In order to obtain the maximum likelihood estimators, w

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Bayesian Estimation Under the ,-Distribution for Financial Time Series,rkov chain. Due to the computational difficulty of obtaining maximum likelihood estimates, alternatively, we develop Bayesian inference using the empirical Bayes method through the resampling procedure. We provide a Metropolis–Hastings algorithm to simulate the posterior distribution. We also analyz

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Control Charts of Mean by Using Copula Markov SPC and Conditional Distribution by Copula,(Commun Stat: Simul Comput 46:3067–3087, 2017) under serial dependence after accounting for the directional dependence by diverse copula functions. To illustrate the method proposed by Kim et al. (Commun Stat: Simul Comput, 2019), we revisit the case study of Major League Baseball (MLB), where the S

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Copula Markov Models for Count Series with Excess Zeros,. In some cases, a specific count, say zero, may occur more often than usual. Additionally, serial dependence might be found among these counts if they are recorded over time. Overlooking the frequent occurrence of zeros and the serial dependence could lead to false inference. In this chapter, Marko

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