New PDF release: Hidden Markov and Other Models for Discrete-valued Time

By Iain L. MacDonald, Walter Zucchini

ISBN-10: 0412558505

ISBN-13: 9780412558504

Discrete-valued time sequence are universal in perform, yet equipment for his or her research aren't recognized. in recent times, equipment were constructed that are in particular designed for the research of discrete-valued time sequence. Hidden Markov and different versions for Discrete-Valued Time sequence introduces a brand new, flexible, and computationally tractable category of versions, the "hidden Markov" types. It provides an in depth account of those types, then applies them to information from quite a lot of assorted topic components, together with medication, climatology, and geophysics. This booklet might be valuable to researchers and postgraduate and senior undergraduate scholars in records. Researchers and utilized statisticians who research time sequence facts in drugs, animal habit, hydrology, and sociology also will locate this data important.

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Extra info for Hidden Markov and Other Models for Discrete-valued Time Series (Monographs on Statistics and Applied Probability 70)

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5 Models based on thinning "A fairly broad class of models is that based on the idea of 'thiIlning'. Such models are discussed by McKenzie (1985a,b; 1986; 1987; 1988a,b), AI-Osh and Alzaid (1987; 198,8; 1991), Alzaid and AI-Osh (1988; 1990; 1993), Du and Li (1991) and, AI-Osh and, Aly (1992). Dion, Gauthier and Latour (1992) show that these models are simple functionals of a multitype branching process with immigration: this enables them to unify and extend many of the results available for the models based on thinning.

8) of McKenzie (1986). 8). Furthermore, the model St == a * St-l + R t has the possibly useful interpretation that St consists of the survivors of those present at time t 1 (each with survival probability a ) plus R t new entrants entering between t - 1 and t. The above model is the geometric autoregressive process of order one of McKenzie (1985b), described also by Alzaid and AI-Osh· (1988). 5. g. Poisson, binomial or nega:tive binomial) and dependence structure (autoregressive, moving average or ARMA).

In that section the number of deliveries by Caesarean section is modelled as a proportion of the total number of deliveries, and related, inter alia, ,to the corresponding proportion in the previous month. 8, Parameter~drivenmodels We now consider models of the kInd described by Cox {1981) as 'parameter-driven'. Conditional on some unobserved 'parameter process', the observations in such a model are independent, with distribution determined by the current state of theparalneter process. For instance, the conditional distribution of an observation could be Poisson, with mean Al or A2 depending on whether a two-state parameter process is ,in state 1 or state 2.

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Hidden Markov and Other Models for Discrete-valued Time Series (Monographs on Statistics and Applied Probability 70) by Iain L. MacDonald, Walter Zucchini

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