Modeling Zero Inflation in Count Data Time Series with Bounded Support

Methodology and Computing in Applied Probability - Tập 20 Số 2 - Trang 589-609 - 2018
Tobias A. Möller1, Christian Weiß1, Hee-Young Kim2, Andrei Sirchenko3
1Department of Mathematics and Statistics, Helmut Schmidt University, Hamburg, Germany
2Division of Economics and Statistics, National Statistics, Korea University, Sejong, South Korea
3Department of Economics, Higher School of Economics, Moscow, Russia

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Tài liệu tham khảo

Barreto-Souza W (2015) Zero-modified geometric INAR(1) process for modelling count time series with deflation or inflation of zeros. J Time Ser Anal 36(6):839–852

Billingsley P (1961) Statistical inference for Markov processes. Statistical research monographs, University of Chicago Press

Emiliano PC, Vivanco MJF, De Menezes FS (2014) Information criteria: how do they behave in different models? Comput Stat Data Anal 69:141–153

Fox AJ (1972) Outliers in time series. J R Stat Soc B 34(3):350–363

Gonçalves E, Mendes-Lopes N, Silva F (2016) Zero-inflated compound Poisson distributions in integer-valued GARCH models. Statistics 50(3):558–578

Grunwald G, Hyndman RJ, Tedesco L, Tweedie RL (2000) Non-Gaussian conditional linear AR(1) models. Aust N Z J Stat 42(4):479–495

Hilbe JM (2014) Modeling count data. Cambridge University Press, New York

Jazi MA, Jones G, Lai C-D (2012) First-order integer valued processes with zero inflated Poisson innovations. J Time Ser Anal 33(6):954–963

Jung RC, Kukuk M, Liesenfeld R (2006) Time series of count data: modelling and estimation and diagnostics. Comput Stat Data Anal 51(4):2350–2364

McKenzie E (1985) Some simple models for discrete variate time series. Water Resour Bull 21(4):645–650

Möller TA, Silva ME, Weiß CH, Scotto MG, Pereira I (2016) Self-exciting threshold binomial autoregressive processes. AStA Adv Stat Anal 100(4):369–400

Nastić AS, Ristić MM, Miletić Ilić A (2017) A geometric time series model with an alternative dependent Bernoulli counting series. Commun Stat Theory Methods 46(2):770–785

Rubin DB (1976) Inference and missing data. Biometrika 63(3):581–592

Seneta E (1983) Non-negative matrices and Markov chains, 2nd edn. Springer Verlag, New York

Sirchenko A (2013) A model for ordinal responses with an application to policy interest rate. NBP Working paper No 148

Steutel FW, van Harn K (1979) Discrete analogues of self-decomposability and stability. Ann Probab 7(5):893–899

Weiß CH (2009a) Monitoring correlated processes with binomial marginals. J Appl Stat 36(4):399–414

Weiß CH (2009b) A new class of autoregressive models for time series of binomial counts. Commun Stat Theory Methods 38(4):447–460

Weiß CH, Homburg A, Puig P (2016) Testing for zero inflation and overdispersion in INAR(1) models. Statistical Papers, to appear

Weiß CH, Kim H-Y (2013) Binomial AR (1) processes: moments, cumulants, and estimation. Statistics 47(3):494–510

Weiß CH, Kim H-Y (2014) Diagnosing and modeling extra-binomial variation for time-dependent counts. Appl Stochast Models Bus Ind 30(5):588–608

Weiß CH, Pollett PK (2012) Chain binomial models and binomial autoregressive processes. Biometrics 68(3):815–824

Weiß CH, Pollett PK (2014) Binomial autoregressive processes with density dependent thinning. J Time Ser Anal 35(2):115–132

Weiß CH, Testik MC (2015) On the Phase I analysis for monitoring time-dependent count processes. IIE Trans 47(3):294–306

Zeileis A, Kleiber C, Jackman S (2008) Regression models for count data in R. J Stat Softw 27(8):1–25

Zhu F (2012) Zero-inflated Poisson and negative binomial integer-valued GARCH models. J Stat Plann Infer 142(4):826–839

Zucchini W, MacDonald IL (2009) Hidden markov models for time series: an introduction using R. Chapman and Hall/CRC, London

Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York