Modelling Count Data Time Series with Markov Processes Based on Binomial Thinning

By Rong Zhu, Harry Joe. 2006

106 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a8ee649a86765ada468d796bbd4c54d96d9c95e3

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Citations in the corpus

Zero truncated Poisson integer-valued AR(1) model (2010, methodology): “Now, we apply some models to the original time series such as PoINAR(1) (Al-Osh and Alzaid 1987), NBRCINAR(1) (Weis 2008), NBINAR(1) ( Zhu and Joe 2006 ),”
Parameter change test for zero-inflated generalized Poisson autoregressive models (2016, background): “[11] Sequences of counts appear in many other application fields such as statistical quality control [12] and insurance.[13] See also Winkelmann,[14] who provides a survey of statistical and econometric techniques for count data on the basis of conditional distribution models, and Jung and Tremayne,”
The combined INAR(p) models for time series of counts (2008, background): “…conditional probability P (α ◦t+1 Xt, . . . , α ◦t+p Xt | Xt = xt,Ht−1) equals P (α ◦t+1 Xt, . . . , α ◦t+p Xt | Xt = xt), where Ht−1 abbreviates the process history of all Xs and α ◦s+j Xs for s ≤ t − 1 and j = 1, . . . , p. Zhu & Joe (2006) only analyzed the special CINAR(2) model of Example 4.”
Estimation in an Integer-Valued Autoregressive Process with Negative Binomial Marginals (NBINAR(1)) (2012, background): “Random variable Z n has already been considered by McKenzie ( 1987 ) and Zhu and Joe ( 2006 ).”
A New Class of Autoregressive Models for Time Series of Binomial Counts (2009, methodology): “The definition of the model is motivated by an idea of Jayakumar (1995), Zhu and Joe (2006), and Weiß (2008a), who combined the usual INAR(1) model with the multinomial decision process proposed by Lawrance and Lewis (1980).”
Efficient probabilistic forecasts for counts (2011, unknown): “…et al. (1995), Latour (1998), Ispany et al. (2003), Freeland and McCabe (2004), Jung et al. (2005), McCabe and Martin (2005), Silva and Oliveira (2005), Jung and Tremayne (2006), Zhu and Joe (2006), Neal and Subba Rao (2007), Bu and McCabe (2008), Bu et al. (2008) and Drost et al. (2008, 2009).”
Integer-valued autoregressive models for counts showing underdispersion (2013, unknown): “Therefore, another approach to extend the INAR(1) model to a pth order autoregressive model might be of interest, the combined INAR(p) model (CINAR(p)) of [31,35].”
Changepoints in times series of counts (2012, background): “Sequences of counts which exhibit temporal dependence appear also in many other fields of applications like statistical quality control [30] or finance [27], [9], [19] and insurance [34].”
Count Time Series Models (2012, background): “The upper left plot shows a time series of claims–referred to as series C3–of short-term disability benefits made by cut injured workers in the logging industry, (Zhu and Joe (2006)).”
Discrete-valued ARMA processes (2009, methodology): “The data were previously analyzed by Freeland (1998) and Zhu and Joe (2006) using the thinning operator based model.”
A geometric time series model with dependent Bernoulli counting series (2013, background): “…(Ristić et al., 2009), mixed INAR(1) and mixed ab INAR(1) (Nastić and Ristić, 2012), INAR(1) models I1, I2 and I3 with negative binomial marginals (Zhu and Joe, 2006, 2010), iterated INAR(1) model with negative binomial marginals (Al-Osh and Aly, 1992), NBINAR(1), INAR process with negative…”