scholarly journals Minimax prediction of sequences with periodically stationary increments

2021 ◽  
Vol 13 (2) ◽  
pp. 352-376
Author(s):  
P.S. Kozak ◽  
M.M. Luz ◽  
M.P. Moklyachuk
Keyword(s):  
Spectral Characteristics ◽  
Optimal Estimation ◽  
Mean Square ◽  
Linear Functionals ◽  
Spectral Densities ◽  
Stationary Increments ◽  
Optimal Estimates ◽  
Optimal Linear ◽  
The Mean ◽  
Mean Square Errors

The problem of optimal estimation of linear functionals constructed from unobserved values of a stochastic sequence with periodically stationary increments based on its observations at points $ k<0$ is considered. For sequences with known spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal estimates of the functionals. Formulas that determine the least favourable spectral densities and minimax (robust) spectral characteristics of the optimal linear estimates of functionals are derived in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are given.

scholarly journals Minimax-robust forecasting of sequences with periodically stationary long memory multiple seasonal increments

2020 ◽  
Vol 8 (3) ◽  
pp. 684-721
Author(s):  
Maksym Luz ◽  
Mikhail Moklyachuk
Keyword(s):  
Long Memory ◽  
Spectral Characteristics ◽  
Optimal Estimation ◽  
Mean Square ◽  
Linear Functionals ◽  
Spectral Densities ◽  
Optimal Estimates ◽  
Optimal Linear ◽  
The Mean ◽  
Mean Square Errors

We introduce a stochastic sequence $\zeta(k)$ with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. We solve the problem of optimal estimation of linear functionals constructed from unobserved values of the stochastic sequence $\zeta(k)$  based on its  observations at points $ k<0$. For sequences with known matrices of spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal estimates of the functionals. Formulas that determine the least favorable spectral densities and minimax (robust) spectral characteristics of the optimal linear estimates of the functionals are proposed in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are given.

scholarly journals Minimax-robust estimation problems for sequences with periodically stationary increments observed with noise

2020 ◽  
pp. 68-83
Author(s):  
M. P. Moklyachuk ◽  
M. M. Luz
Keyword(s):  
Spectral Characteristics ◽  
Optimal Estimation ◽  
Mean Square ◽  
Linear Functionals ◽  
Spectral Densities ◽  
Stationary Increments ◽  
Estimation Problems ◽  
Optimal Linear ◽  
The Mean ◽  
Mean Square Errors

The problem of optimal estimation of linear functionals constructed from the unobserved values of a stochastic sequence with periodically stationary increments based on observations of the sequence with stationary noise is considered. For sequences with known spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal estimates of the functionals. Formulas that determine the least favorable spectral densities and the minimax-robust spectral characteristics of the optimal linear estimates of functionals are proposed in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are specified.

scholarly journals Interpolation Problem for Periodically Correlated Stochastic Sequences with Missing Observations

2020 ◽  
Vol 8 (2) ◽  
pp. 631-654
Author(s):  
Iryna Golichenko ◽  
Mikhail Moklyachuk
Keyword(s):  
Interpolation Problem ◽  
Spectral Characteristics ◽  
Optimal Estimation ◽  
Mean Square ◽  
Missing Observations ◽  
Linear Functionals ◽  
Spectral Densities ◽  
Optimal Estimates ◽  
The Mean ◽  
Mean Square Errors

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.

scholarly journals Filtering of multidimensional stationary sequences with missing observations

2019 ◽  
Vol 11 (2) ◽  
pp. 361-378
Author(s):  
O.Yu. Masyutka ◽  
M.P. Moklyachuk ◽  
M.I. Sidei
Keyword(s):  
Spectral Characteristics ◽  
Stochastic Noise ◽  
Mean Square ◽  
Missing Observations ◽  
Linear Functionals ◽  
Spectral Densities ◽  
Optimal Linear ◽  
The Mean ◽  
Noise Sequence ◽  
Mean Square Errors

The problem of mean-square optimal linear estimation of linear functionals which depend on the unknown values of a multidimensional stationary stochastic sequence is considered. Estimates are based on observations of the sequence with an additive stationary stochastic noise sequence at points which do not belong to some finite intervals of a real line. Formulas for calculating the mean-square errors and the spectral characteristics of the optimal linear estimates of the functionals are proposed under the condition of spectral certainty, where spectral densities of the sequences are exactly known. The minimax (robust) method of estimation is applied in the case where spectral densities are not known exactly while some sets of admissible spectral densities are given. Formulas that determine the least favorable spectral densities and minimax spectral characteristics are proposed for some special sets of admissible densities.

Extrapolation Problem for Continuous Time Periodically Correlated Isotropic Random Fields

2017 ◽  
Vol 19 ◽  
pp. 1-23
Author(s):  
Iryna Golichenko ◽  
Oleksand Masyutka ◽  
Mikhail Moklyachuk
Keyword(s):  
Random Field ◽  
Spectral Characteristics ◽  
Mean Square ◽  
Spectral Densities ◽  
Optimal Linear ◽  
Extrapolation Problem ◽  
Time Argument ◽  
The Mean ◽  
Mean Square Errors ◽  
Isotropic Random

The problem of optimal linear estimation of functionals depending on the unknown values of a random fieldζ(t,x), which is mean-square continuous periodically correlated with respect to time argumenttє R and isotropic on the unit sphere Sn with respect to spatial argumentxєSn. Estimates are based on observations of the fieldζ(t,x) +Θ(t,x) at points (t,x) :t< 0;xєSn, whereΘ(t,x) is an uncorrelated withζ(t,x) random field, which is mean-square continuous periodically correlated with respect to time argumenttє R and isotropic on the sphereSnwith respect to spatial argumentxєSn. Formulas for calculating the mean square errors and the spectral characteristics of the optimal linear estimate of functionals are derived in the case of spectral certainty where the spectral densities of the fields are exactly known. Formulas that determine the least favourable spectral densities and the minimax (robust) spectral characteristics are proposed in the case where the spectral densities are not exactly known while a class of admissible spectral densities is given.

scholarly journals Robust Filtering of Sequences with Periodically Stationary Multiplicative Seasonal Increments

2021 ◽  
Vol 9 (4) ◽  
pp. 1010-1030
Author(s):  
Maksym Luz ◽  
Mikhail Moklyachuk
Keyword(s):  
Spectral Characteristics ◽  
Linear Filtering ◽  
Linear Functionals ◽  
Spectral Densities ◽  
Optimal Linear ◽  
The Mean ◽  
Noise Sequence ◽  
Mean Square Errors ◽  
Filtering Problem ◽  
Optimal Linear Filtering

We consider stochastic sequences with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. We solve the filtering problem for linear functionals constructed from unobserved values of a stochastic sequence of this type based on observations of the sequence with a periodically stationary noise sequence. For sequences with known matrices of spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal filtering of the functionals. Formulas that determine the least favourable spectral densities and the minimax (robust) spectral characteristics of the optimal linear filtering of the functionals are proposed in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are given.

scholarly journals Realization of the Primary Terrestrial Reference Frame

1991 ◽  
Vol 127 ◽  
pp. 108-115
Author(s):  
W. Kosek ◽  
B. Kołaczek
Keyword(s):  
Reference Frame ◽  
Geographic Distribution ◽  
Terrestrial Reference Frame ◽  
Mean Square ◽  
Minimum Value ◽  
Collocation Points ◽  
Station Coordinates ◽  
The Mean ◽  
Transformation Parameters ◽  
Mean Square Errors

AbstractThe PTRF is based on 43 sites with 64 SSC collocation points with the optimum geographic distribution, which were selected from all stations of the ITRF89 according to the criterion of the minimum value of the errors of 7 parameters of transformation. The ITRF89 was computed by the IERS Terrestrial Frame Section in Institut Geographique National - IGN and contains 192 VLBI and SLR stations (points) with 119 collocation ones. The PTRF has been compared with the ITRF89. The errors of the 7 parameters of transformation between the PTRF and 18 individual SSC as well as the mean square errors of station coordinates are of the same order as those for the ITRF89. The transformation parameters between the ITRF89 and the PTRF are negligible and their errors are of the order of 3 mm.

Transient Response of a Shear Beam to Correlated Random Boundary Excitation

1977 ◽  
Vol 44 (3) ◽  
pp. 487-491 ◽  
Author(s):  
S. F. Masri ◽  
F. Udwadia
Keyword(s):  
Time Delays ◽  
Spectral Characteristics ◽  
Mean Square Displacement ◽  
Dimensionless Time ◽  
Mean Square ◽  
Random Boundary ◽  
Mean Square Response ◽  
Shear Beam ◽  
The Mean ◽  
Support Motion

The transient mean-square displacement, slope, and relative motion of a viscously damped shear beam subjected to correlated random boundary excitation is presented. The effects of various system parameters including the spectral characteristics of the excitation, the delay time between the beam support motion, and the beam damping have been investigated. Marked amplifications in the mean-square response are shown to occur for certain dimensionless time delays.

Mathematical-Statistical Methods for the Analysis of Aerial Triangulation Adjustment Errors Using a Computer Program

1975 ◽  
Vol 29 (2) ◽  
pp. 175-188
Author(s):  
M. Mosaad Allam
Keyword(s):  
Computer Program ◽  
Frequency Interval ◽  
Mean Square ◽  
Fisher Criterion ◽  
Statistical Parameters ◽  
Sample Distribution ◽  
Development Section ◽  
The Mean ◽  
Kolmogorov Criterion ◽  
Mean Square Errors

In practice, photogrammetrists use a single statistic reliability interval criterion, based on the mean square errors, to judge the accuracy of adjustment of photogrammetric blocks. Even in some cases, if the practical and theoretical distributions of frequency interval agree, such a test does not make it possible to establish the closeness of their convergence nor the degree of their difference. In other words, to get a complete picture of the character of the distribution of errors in the adjusted photogrammetric blocks, it is insufficient to investigate any single statistic. In the Research and Development Section of the Topographical Survey Directorate, a computer program (SABA) has been designed to analyze the errors of photogrammetric block adjustments, compute various statistical parameters and check the sample distribution using Kolmogorov criterion. Based on the decision taken, the correspondence between the empirical and theoretical distribution series are checked using the criterion χ2. The program divides the adjusted block to make a comparative evaluation of accuracies in the different sub-blocks. In this case, in addition to Kolmogorov and χ2 tests, the program checks the reliability intervals of the means and mean square errors of the samples and uses Fisher criterion ‘F’ to check the hypothesis of the equality of dispersion. SABA is coded in Fortran IV and Compass for the CDC CYBER 74 and requires a central memory of 28K decimal works. SABA is the acronym for Statistical Analysis of Block Adjustment.

Experimental Research on Headwork Spillway Dam of Ecuador Coca Codo Sinclair Hydropower Station

2012 ◽  
Vol 256-259 ◽  
pp. 2474-2479
Author(s):  
Yuan Fa Li ◽  
Cai Ping Wu ◽  
Li Xuan Song ◽  
Yan Fen Ren
Keyword(s):  
Discharge Capacity ◽  
Spectral Characteristics ◽  
Square Root ◽  
Mean Square ◽  
Design Requirement ◽  
Body Type ◽  
Power Station ◽  
Fluctuating Pressure ◽  
The Mean ◽  
Dominant Frequencies

The discharge capacity, pressure and downstream dissipation for scour prevention under fluctuating pressure of the power station headwork spillway dam were measured and analyzed through model test. The research showed that the discharge capacity of the spillway dam met the design requirement, the designed body type of the spillway dam met the requirement of standards. The modified spillway basin body type and the downstream protection type could meet the requirement of dissipation for scour prevention. In the experiment, the mean square root, spectral characteristics and amplitude sampling of the spillway dam flow fluctuating pressure were measured and analyzed. The maximum fluctuating pressure mean root was about 4.31m water column, the dominant frequencies of flow fluctuating pressure at all measuring points ranged from 0.01 to 2Hz (prototype), and the random process of the flow fluctuating pressure conformed to probability normal distribution (Gaussian distribution) on the whole.

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