Interpolation Problem for Periodically Correlated Stochastic Sequences with Missing Observations

  • Iryna Golichenko Department of Mathematical Analysis and Probability Theory, National Technical University of Ukraine ``Igor Sikorsky Kyiv Politechnic Institute''
  • Mikhail Moklyachuk Kyiv National Taras Shevchenko University
Keywords: Periodically correlated sequence, optimal linear estimate, mean square error, least favourable spectral density matrix, minimax spectral characteristic

Abstract

The problem of mean square optimal estimation of linear functionals which depend on the unknown values of a periodically correlated stochastic sequence is considered. The estimates are based on observations of the sequence with a noise. Formulas for calculation the mean square errors and the spectral characteristics of the optimal estimates of functionals are derived in the case of spectral certainty, where spectral densities of the sequences are exactly known. Formulas that determine the least favorable spectral densities and the minimax spectral characteristics are proposed in the case of spectral uncertainty, where spectral densities of the sequences are not exactly known while some classes of admissible spectral densities are specified.

Author Biography

Mikhail Moklyachuk, Kyiv National Taras Shevchenko University
Department of Probability Theory, Statistics and Actuarial Mathematics, Professor

References

B. Abraham, Missing observations in time series, Communications in Statistics–Theory and Methods, vol. 10, pp. 1643–1653, 1981.

L. Aggoun, and R. J. Elliott, Measure theory and filtering: introduction and applications, Cambridge University Press, 2004.

D. Z. Arov, and H. Dym, Multivariate prediction, de Branges spaces, and related extension and inverse problems, Birkhäuser, 2018.

I.V. Basawa, and B.L.S. Prakasa Rao, Statistical inference for stochastic processes, London: Academic Press, 1980.

W. R. Bennett, Statistics of regenerative digital transmission, Bell System Technical Journal, vol. 37, no. 6, pp. 1501–1542, 1958.

P. Bondon, Influence of missing values on the prediction of a stationary time series, Journal of Time Series Analysis, vol. 26, no. 4, pp. 519-525, 2005.

P. Bondon, Prediction with incomplete past of a stationary process, Stochastic Processes and Applications. vol.98, pp. 67-76, 2002.

R. Cheng, A.G. Miamee, and M. Pourahmadi, Some extremal problems in Lp(w), Proceedings of the American Mathematical Society. vol.126, pp. 2333–2340, 1998.

R. Cheng, and M. Pourahmadi, Prediction with incomplete past and interpolation of missing values, Statistics & Probability Letters. vol. 33, pp. 341–346, 1996.

S. Cohen, and R.J. Elliott, Stochastic calculus and applications, Basel: Birkhauser, 2015.

M. J. Daniels, and J. W. Hogan, Missing data in longitudinal studies: strategies for Bayesian modeling and sensitivity analysis, Boca Raton: Taylor & Francis Group, 2008.

I. I. Dubovets’ka, O.Yu. Masyutka, and M.P. Moklyachuk, Interpolation of periodically correlated stochastic sequences, Theory of Probability and Mathematical Statistics, vol. 84, pp. 43-56, 2012.

I. I. Dubovets’ka, and M. P. Moklyachuk, Filtration of linear functionals of periodically correlated sequences, Theory of Probability and Mathematical Statistics, vol. 86, pp. 51-64, 2013.

I. I. Dubovets’ka, and M. P. Moklyachuk, Minimax estimation problem for periodically correlated stochastic processes, Journal of Mathematics and System Science, vol. 3, no. 1, pp. 26-30, 2013.

I. I. Dubovets’ka, and M. P. Moklyachuk, Extrapolation of periodically correlated processes from observations with noise, Theory of Probability and Mathematical Statistics, vol. 88, pp. 67-83, 2014.

I. I. Dubovets’ka, and M. P. Moklyachuk, On minimax estimation problems for periodically correlated stochastic processes, Contemporary Mathematics and Statistics, vol.2, no. 1, pp. 123-150, 2014.

W. A. Gardner, and L. E. Franks, Characterization of cyclostationary random signal processes, IEEE Transactions on information theory, vol. IT-21, no. 1, pp. 4-14, 1975.

W.A.Gardner, Cyclostationarity in communications and signal processing, New York: IEEE Press, 1994.

W.A.Gardner, A. Napolitano, L. Paura, Cyclostationarity: Half a century of research, Signal Processing, vol. 86, pp. 639–697, 2006.

E. G. Gladyshev, Periodically correlated random sequences, Sov. Math. Dokl., vol. 2, pp. 385–388, 1961.

U. Grenander, A prediction problem in game theory, Arkiv för Matematik, vol. 3, pp. 371–379, 1957.

M. S. Grewal, and A. P. Andrews, Kalman filtering. Theory and practice with MATLAB, Hoboken, NJ: John Wiley & Sons, 2015.

E. J. Hannan, Multiple time series, Wiley Series in Probability and Mathematical Statistics. New York: John Wiley & Sons, 1970.

H. L. Hurd, and A. Miamee, Periodically correlated random sequences, Wiley Series in Probability and Statistics, 2007.

A. D. Ioffe, and V. M. Tihomirov, Theory of extremal problems, Studies in Mathematics and its Applications, Vol. 6. Amsterdam, New York, Oxford: North-Holland Publishing Company. XII, 1979.

G. Kallianpur, Stochastic filtering theory, New York, Heidelberg, Berlin: Springer-Verlag, 1980.

Y. Kasahara, M. Pourahmadi, and A. Inoue, Duals of random vectors and processes with applications to prediction problems with missing values, Statistics & Probability Letters, vol. 79, no. 14, pp. 1637–1646, 2009.

S. A. Kassam, and H. V. Poor, Robust techniques for signal processing: A survey, Proceedings of the IEEE, vol. 73, no. 3, pp.433–481, 1985.

A.N.Kolmogorov, Selected works by A.N.Kolmogorov Vol.II: Probability theory and mathematical statistics. Ed. by A.N.Shiryayev, Mathematics and Its Applications. Soviet Series. 26. Dordrecht etc. Kluwer Academic Publishers, 1992.

D. Koroliouk, Stationary statistical experiments and the optimal estimator for a predictable component, Journal of Mathematical Sciences, vol. 214, no.2, pp. 220–228, 2016.

D. Koroliouk, V. S. Koroliuk, E. Nicolai, P. Bisegna, L. Stella, N. Rosato, A statistical model of macromolecules dynamics for Fluorescence Correlation Spectroscopy data analysis, Statistics, Optimization and Informa. Comput., vol. 4, no. 3, pp. 233-242, 2016.

D. V. Koroliouk, and V. S. Koroliuk, Filtration of stationary Gaussian statistical experiments, Journal of Mathematical Sciences, vol. 229, no.1, pp. 30–35, 2018.

P. S. Kozak, and M. P. Moklyachuk, Estimates of functionals constructed from random sequences with periodically stationary increments, Theory of Probability and Mathematical Statistics, vol. 97, pp. 85-98, 2018.

Kutoyants, Yu.A. Statistical inference for spatial Poisson processes, New York: Springer, 1998.

Kutoyants, Yu.A. Statistical inference for ergodic diffusion processes, London: Springer, 2004.

Little, R. J. A.; Rubin, D. B. Statistical analysis with missing data, Hoboken, NJ: Wiley, 2019.

Liptser, R.S.; Shiryaev, A.N. Statistics of random processes I. General theory, New York – Heidelberg – Berlin: Springer, 2001.

Liptser, R.S.; Shiryaev, A.N. Statistics of random processes II. Applications theory, New York – Heidelberg – Berlin: Springer, 2001.

M. M. Luz and M. P. Moklyachuk, Interpolation of functionals of stochastic sequences with stationary increments, Theory of Probability and Mathematical Statistics, vol. 87, pp. 117-133, 2013.

M. M. Luz, and M. P. Moklyachuk, Minimax-robust filtering problem for stochastic sequences with stationary increments, Theory of Probability and Mathematical Statistics, vol. 89, pp. 127–142, 2014.

M. M. Luz, and M. P. Moklyachuk, Minimax-robust filtering problem for stochastic sequences with stationary increments and cointegrated sequences, Statistics, Optimization & Information Computing, vol. 2, no. 3, pp. 176–199, 2014.

M. M. Luz, and M. P. Moklyachuk, Minimax Interpolation problem for stochastic processes with stationary increments, Statistics, Optimization & Information Computing, vol. 3, no. 1, pp. 30–41, 2015.

M. M. Luz, and M. P. Moklyachuk, Minimax-robust prediction problem for stochastic sequences with stationary increments and cointegrated sequences, Statistics, Optimization & Information Computing, vol. 3, no. 2, pp. 160–188, 2015.

M. M. Luz, and M. P. Moklyachuk, Minimax-robust filtering problem for stochastic sequences with stationary increments and cointegrated sequences, Cogent Mathematics, vol. 3:1167811, pp. 1–21, 2016.

M. M. Luz, and M. P. Moklyachuk, Minimax prediction of stochastic processes with stationary increments from observations with stationary noise, Cogent Mathematics, vol. 3:1133219, pp. 1–17, 2016.

M. M. Luz, and M. P. Moklyachuk, Minimax interpolation of sequences with stationary increments and cointegrated sequences, Modern Stochastics: Theory and Applications, vol. 3, no. 1. pp. 59–87, 2016.

M. M. Luz, and M. P. Moklyachuk, Filtering problem for functionals of stationary sequences, Statistics, Optimization & Information Computing, vol. 4, no. 1, pp. 68–83, 2016.

M. M. Luz, and M. P. Moklyachuk, Minimax interpolation of stochastic processes with stationary increments from observations with noise, Theory of Probability and Mathematical Statistics, vol. 94, pp. 121–135, 2017.

M. M. Luz, and M. P. Moklyachuk, Estimation of stochastic processes with stationary increments and cointegrated sequences, London: ISTE Ltd, Hoboken, NJ: John Wiley & Sons Inc., 2019.

A. Makagon, Theoretical prediction of periodically correlated sequences, Probability and Mathematical Statistics, vol. 19, no. 2, pp. 287–322, 1999.

A. Makagon, A. G. Miamee, H. Salehi, and A. R. Soltani, Stationary sequences associated with a periodically correlated sequence, Probability and Mathematical Statistics, vol. 31, no. 2, pp. 263–283, 2011.

O. Yu. Masyutka, M. P. Moklyachuk, and M. I. Sidei, Interpolation problem for multidimensional stationary sequences with missing observations, Stochastic Modeling and Applications, vol. 22, no. 2, pp. 85–103, 2018.

O. Yu. Masyutka, M. P. Moklyachuk, and M. I. Sidei, Interpolation problem for stationary sequences with missing observations, Statistics, Optimization & Information Computing, vol. 7, no. 1, pp. 97-117, 2019.

O. Yu. Masyutka, M. P. Moklyachuk, and M. I. Sidei, Interpolation problem for multidimensional stationary processes with missing observations, Statistics, Optimization and Information Computing, vol. 7, no. 1, pp. 118-132, 2019.

O. Yu. Masyutka, M. P. Moklyachuk, and M. I. Sidei, Filtering of multidimensional stationary processes with missing observations, Universal Journal of Mathematics and Applications, vol. 2, no.1, pp. 24–35, 2019.

O. Yu. Masyutka, M. P. Moklyachuk, and M. I. Sidei, Filtering of multidimensional stationary sequences with missing observations, Carpathian Mathematical Publications, vol.11, no.2, pp. 361-378, 2019.

P. E. McKnight, K. M. McKnight, S. Sidani, and A. J. Figueredo, Missing data: A gentle introduction, NY: Guilford Press, 2007.

M. P. Moklyachuk, On a filtering problem for vector–valued sequences, Theory of Probability and Mathematical Statistics, vol. 47, pp. 107–114, 1993.

M. P. Moklyachuk, On minimax filtration of vector processes, Ukrainian Mathematical Journal, vol. 45, no.3, pp. 414–423, 1993.

M. P. Moklyachuk, On interpolation problem for vector–valued stochastic sequences, Random Operators and Stochastic Equations, vol.3, no.1, pp. 63–74, 1995.

M. P. Moklyachuk, Robust procedures in time series analysis, Theory of Stochastic Processes, vol. 6, no. 3-4, pp. 127-147, 2000.

M. P. Moklyachuk, Game theory and convex optimization methods in robust estimation problems, Theory of Stochastic Processes, vol. 7, no. 1-2, pp. 253–264, 2001.

M. P. Moklyachuk, Robust estimations of functionals of stochastic processes, Kyiv University, Kyiv, 2008.

M. P. Moklyachuk, Minimax-robust estimation problems for stationary stochastic sequences, Statistics, Optimization & Information Computing, vol. 3, no. 4, pp. 348–419, 2015.

M. P. Moklyachuk, and I. I. Golichenko, Periodically correlated processes estimates, LAP Lambert Academic Publishing, 2016.

M. P. Moklyachuk, and O. Yu. Masyutka, Interpolation of multidimensional stationary sequences, Theory of Probability and Mathematical Statistics, vol. 73, pp. 125–133, 2006.

M. P. Moklyachuk, and O. Yu. Masyutka, Extrapolation of multidimensional stationary processes, Random Operators and Stochastic Equations, vol. 14, pp. 233–244, 2006.

M. P. Moklyachuk, and O. Yu. Masyutka, Minimax prediction problem for multidimensional stationary stochastic processes, Communications in Statistics – Theory and Methods, vol. 40, no. 19-20, pp. 3700–3710, 2011.

M. P. Moklyachuk, and O. Yu. Masyutka, Minimax-robust estimation technique for stationary stochastic processes, LAP Lambert Academic Publishing, 2012.

Moklyachuk, M. P.; Masyutka, A. Yu.; Golichenko, I.I. Estimates of periodically correlated isotropic random fields. Nova Science Publishers Inc. New York, 2018.

M.P.Moklyachuk,andM.I.Sidei, Interpolationproblemforstationarysequenceswithmissingobservations, Statistics,Optimization & Information Computing, vol. 3, no. 3, pp. 259-275, 2015.

M. P. Moklyachuk, and M. I. Sidei, Interpolation of stationary sequences observed with the noise, Theory of Probability and Mathematical Statistics, vol. 93, pp. 143-156, 2016.

M. P. Moklyachuk, and M. I. Sidei, Filtering problem for stationary sequences with missing observations. Statistics, Optimization & Information Computing, vol. 4, no. 4, pp. 308 - 325, 2016.

M. Moklyachuk, and M. Sidei, Filtering Problem for functionals of stationary processes with missing observations, Communications in Optimization Theory, 2016, pp.1-18, Article ID 21, 2016.

M. P. Moklyachuk, and M. I. Sidei, Extrapolation problem for stationary sequences with missing observations,

Statistics, Optimization & Information Computing, vol. 5, no. 3, pp. 212–233, 2017.

M. P. Moklyachuk, M. I. Sidei, and O. Yu. Masyutka, Estimation of stochastic processes with missing observations, New York: Nova Science Publishers, 2019.

A. Napolitano, Cyclostationarity: Limits and generalizations, Signal processing, vol. 120, pp. 323–347, 2016.

A. Napolitano, Cyclostationarity: New trends and applications, Signal processing, vol. 120, pp. 385–408, 2016.

M. M. Pelagatti, Time series modelling with unobserved components New York: CRC Press, 2015.

B.L.S. Prakasa Rao, Statistical inference for fractional diffusion processes, Chichester: John Wiley & Sons, 2010.

B.L.S. Prakasa Rao, Associated sequences, demimartingales and nonparametric inference Basel: Birkhäuser, 2012.

M. Pourahmadi, A. Inoue, and Y. Kasahara A prediction problem in L2(w). Proceedings of the American Mathematical Society. Vol. 135, No. 4, pp. 1233-1239, 2007.

B. N. Pshenichnyj, Necessary conditions of an extremum, Pure and Applied mathematics. 4. New York: Marcel Dekker, 1971.

M. B. Rajarshi, Statistical inference for discrete time stochastic processes, New Dehli: Springer India, 2012.

R. T. Rockafellar, Convex Analysis, Princeton University Press, 1997.

Yu. A. Rozanov, Stationary stochastic processes, San Francisco-Cambridge-London-Amsterdam: Holden-Day, 1967.

B. L. Rozovsky, S. V. Lototsky, Stochastic evolution systems. Linear theory and applications to non-linear filtering, Springer, 2018.

K. S. Vastola, and H. V. Poor, An analysis of the effects of spectral uncertainty on Wiener filtering, Automatica, vol. 28, pp. 289–293,1983.

N. Wiener, Extrapolation, interpolation and smoothing of stationary time series. With engineering applications, The M. I. T. Press, Massachusetts Institute of Technology, Cambridge, Mass., 1966.

Woodward, W.A.; Gray, H.L.; Elliott, A.C. Applied time series analysis with R, CRC Press, Taylor & Francis Group, 2017.

A. M. Yaglom, Correlation theory of stationary and related random functions. Vol. 1: Basic results, Springer Series in Statistics, Springer-Verlag, New York etc., 1987.

A. M. Yaglom, Correlation theory of stationary and related random functions. Vol. 2: Supplementary notes and references, Springer Series in Statistics, Springer-Verlag, New York etc., 1987.

Published
2020-02-20
How to Cite
Golichenko, I., & Moklyachuk, M. (2020). Interpolation Problem for Periodically Correlated Stochastic Sequences with Missing Observations. Statistics, Optimization & Information Computing, 8(2), 631-654. https://doi.org/10.19139/soic-2310-5070-458
Section
Research Articles

Most read articles by the same author(s)