Discrete-Valued Time Series
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (222 p.)Innehållstyp: - text
- computer
- online resource
- 9783725804771
- 9783725804788
- Mathematics and Science
- Mathematics
- Applied mathematics
- Bayesian estimation
- Bayesian filtering
- CMPB thinning operator
- CMPBAR model
- Granger causality
- INAR bootstrap
- INAR(1) process
- INARCH model
- INGARCH
- INMA(1) process
- Kalmykov order
- Poisson INAR(1) model
- TP2 transition probability matrix
- Yule–Walker equations
- autoregressive model
- bounded time series
- censored time series
- conditional distribution
- conditional mutual information
- convolution closed infinitely divisible
- count time series
- discrete-time Markov chain
- discrete-valued time series
- dynamic structure
- equi-dispersion
- ergodicity
- financial complex network
- integer-valued time series
- interval estimation
- iterated extended Kalman filter
- mixed embedding
- n onlinear state space model
- observation-driven
- over-dispersion
- partial autocorrelation function
- risk model
- robust estimation
- ruin probability
- run length
- saddlepoint approximation
- singular value decomposition
- statistical process control
- stochastic premiums
- symbol sequences
- thinning operator
- thinning-based model
- time series of counts
- under-dispersion
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The analysis and modeling of time series has been an active research area for more than 100 years, with the main focus on time series having a continuous range consisting of real numbers or real vectors. It took until the 1980s for the first papers on discrete-valued time series to appear. In the 2000s, a rapid increase in research activity was noted, but only in the last few years was a certain maturity and consolidation of the area of discrete-valued time series observed. This reprint is a collection of articles on a wide range of topics on discrete-valued time series (especially count time series), covering stochastic models and methods for their analysis, univariate and multivariate time series, applications of time series methods to risk analysis, statistical process control, and many more. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.
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eng
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