Peculiarities of finding characteristic functions of the generating process in the model of stationary linear AR(2) process with negative binomial distribution

Authors

DOI:

https://doi.org/10.3103/S0735272716120050

Keywords:

generating process, binomial distribution, stationary linear process, random process, autoregression

Abstract

The linear random AR(2) autoregressive process having the negative binomial distribution has been considered. It has the form ξt + a1ξt–1 + a2ξt–1 = ςt, t Î Z, where {a1, a2 ≠ 0} are the autoregressive parameters; Z = {…, –1, 0, 1, …} is the sequence of integers; {ζt, t Î Z} is the random process with discrete time and independent values having the infinitely divisible distribution law that is called generating process. The method of finding the characteristic function of the generating process for linear autoregressive process having negative binomial distribution is presented. This inverse problem is solved by using properties of the characteristic function of stationary linear autoregressive process that can be presented in the Kolmogorov canonical form and as a linear stationary autoregressive process. An example of finding the Poisson spectrum of jumps and the characteristic function for the linear second order autoregressive process (AR(2)) with negative binomial distribution has been also presented.

References

DAVENPORT Jr., W.B.; ROOT, W.L. An Introduction to the Theory of Random Signals and Noise. Wiley-IEEE Press, 1987.

TIKHONOV, V.I. Statistical Radio Engineering, 2nd ed. [in Russian]. Moscow: Radio i Svyaz’, 1982.

MARCHENKO, B.G.; PROTSENKO, L.D. Determination of the characteristic function and Poisson spectrum of jumps of the generating process from the characteristic function of the linear system response. Radioelectron. Commun. Syst., v.25, n.9, p.31, 1982.

ZVARICH, V.N.; MARCHENKO, B.G. A method of finding the characteristic functions of generating processes for linear autoregressive processes. Radioelectron. Commun. Syst., v.42, n.7, p.64, 1999.

ZVARICH, V.N.; MARCHENKO, B.G.; BEDNYI, N.S. Linear random processes in some problems of data signal simulation. Elektronnoe Modelirovanie, v.23, n.3, p.79, 2001.

BOLDIN, M.V. Estimation of the distribution of noise in an autoregression scheme. Theory Probab. Appl., v.27, n.4, p.866-871, 1983. doi:http://dx.doi.org/10.1137/1127098.

AL-SMADI, ADNAN; ALSHAMALI, AHMAD. Fitting ARMA models to linear non-Gaussian processes using higher order statistics. Signal Processing, v.82, n.11, p.1789-1793, Nov. 2002. doi:http://dx.doi.org/10.1016/S0165-1684(02)00340-7.

ANDERSON, T.W. The Statistical Analysis of Time Series. Wiley, 1971. doi:http://dx.doi.org/10.1002/9781118186428.

BOX, G.E.P.; JENKINS, G.M.; REINSEL, G.C.; LJUNG, G.M. Time Series Analysis: Forecasting and Control, 5th ed. Wiley, 2015.

MARPLE Jr., S.L. Digital Spectral Analysis with Applications. Prentice Hall, 1987.

ZVARICH, V.N.; MARCHENKO, B.G. Characteristic function of the generating process in the model of stationary autoregressive process. Radioelectron. Commun. Syst., v.45, n.8, p.12, 2002.

DIVERSI, ROBERTO; GUIDORSI, ROBERTO. Fast filtering of noisy autoregressive signals. Signal Processing, v.87, n.11, p.2843-2849, Nov. 2007. doi:http://dx.doi.org/10.1016/j.sigpro.2007.05.018.

KOSHELEV, V.I.; ANDREEV, V.G. Synthesis of echo signal ARMA-models. Radioelectron. Commun. Syst., v.40, n.7, p.8, 1997.

KRASIL’NIKOV, A.I. Finding of Poisson spectrum of jumps of the linear system response under exposure to white noise. Space-Time Processing of Signals with Due Regard for their Propagation Medium: Collected Papers. Kharkov: KhAI, 1980.

KRASILNIKOV, A.I.; MARCHENKO, B.G.; MYSLOVICH, M.V. A Mathematical Model of Linear Random Processes in Substantiation of Diagnostic Criteria in Vibratiry Diagnistics of Rolling-Contact Bearing. Vibration Engineering. Hemisphere Pub. Corp., 1989, v.3, p.205-211.

KRASIL’NIKOV, A.I. Models of Noisy Signals in Systems of Diagnostics of Heat-and-Power Engineering Equipment [in Russian]. Kiev: Poligraf-servis, 2014.

COX, D.R. Statistical analysis of time series: Some recent developments. Scand. J. Statist., v.8, n.2, p.93-115, 1981, http://www.jstor.org/stable/4615819.

LAWRANCE, A.J. The innovation distribution of gamma distributed autoregressive process. Scand. J. Statist., v.9, n.4, p.234-236, 1982, http://www.jstor.org/stable/4615888.

MCKENZIE, Ed. Innovation distributions for gamma and negative binomial autoregressions. Scand. J. Statist., v.14, n.1, p.79-85, 1987, http://www.jstor.org/stable/4616050.

KREISS, J.-P. Estimation of the distribution function of noise in stationary processes. Metrika, v.38, n.1, p.285-297, Dec. 1991. doi:http://dx.doi.org/10.1007/BF02613623.

BILLINGSLEY, P. Convergence of Probability Measures, 2nd ed. Wiley-Interscience, 1999.

HANNAN, E.J. Multiple Time Series. Wiley, 1970. doi:http://dx.doi.org/10.1002/9780470316429.

NIEMI, HANNU. On the effects of a nonstationary noise on ARMA models. Scand. J. Statist., v.10, n.1, p.11-17, 1983, http://www.jstor.org/stable/4615895.

MARCHENKO, B.G. The Method of Statistical Integral Expressions and its Applications in Radio Engineering [in Russian]. Kyiv: Naukova Dumka, 1973.

MALYARENKO, A.P.; MARCHENKO, B.G. Poisson spectra of jumps of linear random processes. Visnyk TGTU, v.2, n.2, p.12-17, 1997.

LUKACS, E. Characteristic Functions, 2nd ed. Oxford University Press, 1987.

Published

2016-12-18

Issue

Section

Research Articles