# Stochastic processes, interpolation of

The problem of estimating the values of a stochastic process $ X ( t) $
on some interval $ a < t < b $
using its observed values outside this interval. Usually one has in mind the interpolation estimator $ \widehat{X} ( t) $
for which the mean-square error of interpolation is minimal compared to all other estimators:

$$ {\mathsf E} | \widehat{X} ( t) - X ( t) | ^ {2} = \min ; $$

the interpolation is called linear if one restricts attention to linear estimators. One of the first problems posed and solved was that of linear interpolation of the value $ X ( 0) $ of a stationary sequence. This problem is analogous to the following one: In the space $ L _ {2} $ of square-integrable functions on the interval $ - \pi < \lambda \leq \pi $, one must find the projection of $ \phi ( \lambda ) \in L _ {2} $ onto the subspace generated by the functions $ e ^ {i \lambda k } \phi ( \lambda ) $, $ k = \pm 1 , \pm 2 ,\dots $. This problem has been greatly generalized in the theory of stationary stochastic processes (cf. Stationary stochastic process; [1], [2]). One application is the problem of interpolation of the stochastic process arising from the system

$$ L X ( t) = Y ( t) ,\ t > t _ {0} , $$

where $ L $ is a linear differential operator of order $ l $, and $ Y ( t) $, $ t > t _ {0} $, is a white noise process. For given initial values $ X ^ {(} k) ( t _ {0} ) $, $ k = 1 \dots l - 1 $, independent of the white noise, the optimal interpolation estimator $ X ( t) $, $ a < t < b $, is the solution of the corresponding boundary value problem

$$ L ^ {*} L \widehat{X} ( t) = 0 ,\ \ a < t < b , $$

where $ L ^ {*} $ denotes the formal adjoint operator,

$$ \widehat{X} {} ^ {(} k) ( s) = X ^ {(} k) ( s) ,\ \ k = 0 \dots l , $$

with boundary conditions at the boundary points $ s = a , b $. For systems of stochastic differential equations the problem of interpolation of some components given the values of other observed components reduces to similar interpolation equations. (See [3].)

#### References

[1] | A.N. Kolmogorov, "Stationary sequences in Hilbert space" Byull. Moskov. Gos. Univ. Sekt. A , 2 : 6 (1941) pp. 1–40 (In Russian) |

[2] | Yu.A. Rozanov, "Stationary stochastic processes" , Holden-Day (1967) (Translated from Russian) |

[3] | R.S. Liptser, A.N. Shiryaev, "Statistics of stochastic processes" , 1–2 , Springer (1977–1978) (Translated from Russian) |

#### Comments

The interpolation problem is usually defined as the estimation of an unobserved stochastic process on some time interval given a related stochastic process that is observed outside this time interval. One distinguishes two special cases: 1) linear least-squares interpolation, in which the estimator is constrained to be linear and minimizes a least-squares criterion, see [a1], [a3]; and 2) interpolation in which the conditional distribution of the estimator given the observations is determined, see [a2].

For the Western literature on interpolation see [a5], Sect. 5.3 and [a1], Sect. 4.13. Additional Russian references that have been translated are [a6]; [a7], Sect. 37. For recent developments using stochastic realization theory see [a3], [a4]. Results for the interpolation problem may also be deduced from those for the smoothing problem [a2].

#### References

[a1] | H. Dym, H.P. McKean, "Gaussian processes, function theory, and the inverse spectral problem" , Acad. Press (1976) |

[a2] | E. Pardoux, "Equations du filtrage nonlinéaire, de la prédiction et du lissage" Stochastics , 6 (1982) pp. 193–231 |

[a3] | M. Pavon, "New results on the interpolation problem for continuous-time stationary increment processes" SIAM J. Control Optim. , 22 (1984) pp. 133–142 |

[a4] | M. Pavon, "Optimal interpolation for linear stochastic systems" SIAM J. Control Optim. , 22 (1984) pp. 618–629 |

[a5] | N. Wiener, "Extrapolation, interpolation, and smoothing of stationary time series: with engineering applications" , M.I.T. (1949) |

[a6] | A.M. Yaglom, "Extrapolation, interpolation and filtration of stationary random processes with rational spectral density" Amer. Math. Soc. Sel. Transl. Math. Statist. , 4 (1963) pp. 345–387 Tr. Moskov. Mat. Obshch. , 4 (1955) pp. 333–374 |

[a7] | A.M. Yaglom, "An introduction to the theory of stationary random functions" , Prentice-Hall (1962) (Translated from Russian) |

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Stochastic processes, interpolation of.

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