Open Journal of Statistics
Vol.4 No.8(2014), Article ID:49745,8 pages
DOI:10.4236/ojs.2014.48054
A Non-Asymptotic Confidence Region with a Fixed Size for a Scalar Function Value: Applications in C-OTDR Monitoring Systems
Andrey V. Timofeev
JSK “EqualiZoom”, Astana, Kazakhstan
Email: timofeev.andrey@gmail.com
Copyright © 2014 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Received 3 June 2014; revised 1 July 2014; accepted 18 July 2014
Abstract
In this paper we will investigate some non-asymptotic properties of the modified least squares estimates for the non-linear function f(λ*) by observations that nonlinearly depend on the parameter λ*. Non-asymptotic confidence regions with fixed sizes for the modified least squares estimate are used. The obtained confidence region is valid for a finite number of data points when the distributions of the observations are unknown. Asymptotically the suggested estimates represent usual estimates of the least squares. The paper presents the results of practical applications of the proposed method in C-OTDR monitoring systems.
Keywords: Non-Asymptotic Nonlinear Estimation, Sequential Analysis, Confidence Regions, Guaranteed Accuracy, C-OTDR Monitoring System
1. Introduction
In some practical cases there appears a necessity to estimate of the value of the
function
by observations that depend on the parameter
. One of such cases is estimation of the
value
of acceptable thickness of paraffin film on the surface of oil transportation pipes
when the permission parameter
is a nonlinear function
. In this case the thickness of the paraffin
film
is estimated based on the data of the telemetric control. The final solution as
to whether the value
is acceptable will depend primarily on the value of
. Another actual example is the problem of
estimating the seawater absorption coefficient of the sonar signals in shallow water
when sensors of the C-OTDR monitoring system are used for measurements. In this
case, the absorption coefficient (target parameter
) depends nonlinearly on the water temperature
(unobservable parameter
) and on the frequency of sonar emissions.
In this particular case, it is very important to get the guaranteed accuracy estimate
of the absorption coefficient using only a limited number of observation steps (non-asymptotic
statement of the problem). The importance of non-asymptotic results is dictated
by restricted volume of available sample. In addition, the absorption coefficient
estimation should be performed as often as possible, to explore its connection with
the intensity of very dynamic factors (tidal and bottom currents, turbulence seawater).
This condition is realizable only if in each cycle of estimation we spend solely
a finite number of steps to provide the guaranteed accuracy estimates. Thus in this
particular case the non-asymptotic statement of the problem represents an objective
necessity. In this paper we will investigate some non-asymptotic properties of the
modified least squares estimates for the non-linear function
by observations that nonlinearly depend on the parameter
. Asymptotically the suggested estimates
represent usual estimates of the least squares. Asymptotic properties of nonlinear
least squares estimates are well investigated and discussed (Jennrich [1] , Ljung [2] , Lai [3] , Anderson and Taylor [4]
, Wu [5] , Hu [6]
, Skouras [7] ). At the same time, few results
addressing the finite sample properties exist, whereas the non-asymptotic solution
for the problem of the parameter estimation for stochastic process is practically
important because the sample volume is always limited from above. Accurate construction
of confidence regions for unknown parameters in a non-asymptotic configuration was
obtained for linear models of stochastic dynamic processes (Campi and Weyer [8] -[11] , Ooi, Campi
and Weyer [12] ). Non-asymptotic estimation of
scalar parameter of non-linear regression by means of confidence regions was examined
by Timofeev [13] . Similar estimation of multivariate
parameter was researched by Timofeev [14] [15] . In this paper a sequential design is suggested that
will make it possible to solve the problem of non-linear estimation of the function
value for a wide class of stochastic processes by means of confidence regions in
the non-asymptotic setting. The solution was obtained under condition of partial
a priori definiteness as regards to the stochastic distribution of the observations
2. Statement of the Problem
Let us consider estimation of the value of a continuous function
at the point
,
. The values of
,
are fixed, the value of the
is a priori unknown, but it is definite that the parameter
enters into the equation of an observed process
(1)
Here the non-observed sequence of the noise
and non-observed stochastic sequences
,
,
are defined on the stochastic basic
. Function
is a
-measurable real function of
. The class of models described by
(1) is wide and includes many linear and nonlinear regression models commonly used.
For example,
may be a nonlinear function of past observations
and any other variables
such that the
is
-measurable. It is needed to construct the confidence
interval of the fixed size for value of the
with the required value of the confidence coefficient
.
For the sake of clarity, the following shorthand notation will be used throughout
the rest of the paper:
instead of
and
instead of
3. The Main Result
Let us consider the following estimate:
.
where
(2)
Here,
,
,
is a real-valued function of the parameter
.
With,
, the functional (2) is an ordinary LS criterion which is
constructed with the sample volume of
. The sequential design for confidence estimation
of the parameter
will be regarded as a pair
where
and
is a Markov moment relative to the family
such that
.
Let us consider the closed interval. It is obvious that
, and we will use the
as a confidence interval for the value of
. Let
be u-neighbourhood of the interval
,
. The properties of the sequential
design
are described by the following:
Theorem 1. Assume that the following statements are true:
1) For some
stochastic functions
,
are continuously differentiable in the neighbourhood
and function
is continuous on the
.
2)
3) For every,
it is almost sure that a sequence of stochastic functions
converges on the.
4) If
then
.
5)
6).
7) There exists a known constant.
8).
Then for any
the following assertions hold true:
1).
2).
Remarks. The sequences,
are parameters of the estimation procedure. In order
to meet the condition 7, the sequences
,
may be definite, for example, in the form of
,
, where
, [a]-integral
part of the а,
. Indeed
Proof of the Theorem 1. The proof is based on the following statements.
Lemma 1. [1] Let us assume that for a numeric sequence
and a sequence of the continuous on the compact K functions
the following conditions are met:
1) If
the series
converges.
2) If
the series
converges uniformly in
.
3).
Then
converges uniformly in
.
Lemma 2. [16] Let
be a sequence of continuous on the compact K stochastic functions for which the
following conditions are met:
1) With every
a sequence
is consistent with a nondecreasing flow of
-subalgebras
2) If
the sequence
converges uniformly on the
and limiting function has the unique minimum in the point
.
3) Then there exists a sequence of the random values
consistent with a nondecreasing flow of
subalgebras
and such that
Here
is the norm of the space in which the compact K is embedded.
Lemma 3. [17] Let the
is a stochastic and continuous on the interval
function and for some constant
the following conditions are met:
Then a constant B exists and for it the following assertion is true:
.
This lemma is a corollary of the Theorem 19 [18] .
Lemma 4. Let us assume that the real function
and stochastic functions
,
in the (1) are continuous on the interval
. If the conditions 2 - 4 and 7 of
the Theorem 1 hold true, then we have
Proof on the Lemma 4. Consider the following representation:
From condition 2 of the theorem it follows that
is a square integrable martingale. From condition 2 of the Theorem 1 we have:
a.s.
Further using the strong law of large numbers for square integrable martingales [17] we have
(3)
For every
let us say that
.
It follows from conditions 2 and 8 of the Theorem 1 for the square integrable martingale
that the following assertion is true
and using the strong law of large numbers for square integrable martingales we have:
. (4)
From (3), (4) and condition 3 of the Theorem 1 and assertion of the Lemma 1 it follows that
(5)
is realized uniformly in.
Further, using (1) and (2) we have:
Taking into account that
if
,
and (3), (5) as well as conditions 3, 4 of the Theorem
1, the following assertions hold true:
• If, series
converges uniformly in
almost sure.
• The limiting function of the series
have the unique minimum in the point
.
From here and from assertion of the Lemma 2 we have
Hence, the Lemma 4 is proven.
Let us get back to the proof of the Theorem 1. The
and
are strongly consistent estimates of the parameter
(it follow from the Lemma 3). From here and from the continuity of the function
is succeed
.
Using (2), we can see that
(6)
Consider the set
(7)
Taking into account that on the set (7) the following inequalities hold true
from (6) it follows that on the same set the following conclusions are true, too:
(8)
By definition, the estimates
and
are the minimum points of the functions
and
, respectively, we have
Using (8) we can assume that on the set (7) the following inequalities are true:
(9)
Let us set the upper bound of the probability of the event (7) not happening:
(10)
From the conditions 1, 2, 5 of the Theorem 1, the function
has the following properties:
,
Using this property (10) and assertion of the Lemma 3 we have
. (11)
Using (9), (11) and condition 6, 7 of the Theorem 1 we can write that
The Theorem 1 is proven.
Note. From Lemma 4 it follows that asymptotically the estimates of
represent usual estimates of the least squares.
4. Practical Example
Let us consider the problem of estimating the seawater absorption coefficient of
the sonar signals in shallow water when sensors of a C-OTDR monitoring system are
used for measurements. The fiber-optic sensor (FOS) of the C-OTDR monitoring system
is laid on the sea bottom; it is an ordinary monomode fiber-optic line stowed inside
of a special hygroscopic cable. At the logical level the entire length of this cable
is split into equal portions duration of ~5 m. Each of these sections is called
a logical C-OTDR channel or DAS (distributed acoustic sensor). Each DAS is able
to measure the vibration of the hydrosphere which appeared in the area of its sensitivity.
In fact, FOS consists of a huge number of vibration sensitive sensors successively
arranged along the cable. A source of the hydroacoustic emissions (SHdE) emits a
pulsed narrowband signal at a predetermined frequency. The coordinates of the SHdE
are given. Therefore the angle of incidence of the hydroacoustic waves on FOS is
also known. We denote this angle as. When the wave reaches the DAS system, the
appropriate signal is fixed on N sensors (DAS) at successive times
. Let’s denote the moment when the signal reaches the
sensor as n (n is both the sensor’s number and the moment’s number). The signal
goes through a group of sensors one by one as it moves at a particular speed. Thus,
it reaches sensor 1 at the moment 1, sensor 2 at the moment 2 etc. The distribution
of energy from the emitting source at each of the N sensors, at moments
is described by the following equation:
Here
is the observed value of the signal energy in the n-th sensor at time n;
—a noise component at moment
, which appear due to the influence
of the dispersing medium, and
,
,
;
is a scalar function that describes the absorption of
the elastic vibrations, this function depends on following parameters: water temperature
T; value
, where
is the distance between the sensor number n and the sensor number
; n-sensor number; f-frequency of the
SHdE. The function
has the following simple form:
. Here, the constant D is given and depends
on the bottom and volume scattering. Function
is a seawater absorption coefficient, which represents the basic interest for us.
In turn, this function depends on an unknown parameter T (water temperature) and
a given parameter f (frequency radiation of the SHdE). Observing
, it is necessary to construct a confidence
interval with fixed size for the seawater absorption coefficient
by means of the sequential procedure which was suggested in the Section 3 of this
paper. Parameters f, S, C are given. Experimental data (observations) was obtained
for a depth of 50 m. The number of channels of the C-OTDR-system is 4000. Water
temperature T at a depth of 50 m was considered a priori unknown and subject to
estimation during the experiment. Thus, a primary purpose of the experiment was
to estimate of the absorption coefficient of sea water
, which depends nonlinearly on temperature
T. In other words, the task was a interval estimation of the nonlinear function
of the unknown parameter by dependent data
. Table 1
shows the estimation results of the absorption coefficient values at different sensing
frequencies
and temperature
with
.
Here the “point estimates” were calculated by alternative methods. The temperature
on the depth 50 m was determined by specialized sensors which were placed under
water during the experiment for additional control. Once again, we point out that
in the process of interval estimation, the water temperature T was considered
unknown. The column “Required width of the confidence interval” contains information
about a priori given size of the confidence interval for
which we wish to get results for. The column “Average observation time” contains
the average number of observation which had to be used to achieve the required width
of the confidence interval in the sequential procedure. The content of this table
shows that achieved results are practically acceptable.
5. Conclusions Remarks
In this paper we investigated some non-asymptotic properties of the modified least
squares estimates for the non-linear function
by observations that nonlinearly depend on the parameter
. The solution was found by sequence analysis.
The obtained confidence region is valid for a finite number of data points when
the distributions of the observations are unknown. If
, the suggested method allows us to construct
a confidence interval with fixed size for non-known parameter
. The results of the practical application
showed efficiency of the proposed approach.
Acknowledgements
This investigation has been produced under the project “Development of a remote monitoring system to protect backbone communications infrastructure, oil and gas pipelines and other extended objects (project code name— XY)”, financed under the project “Technology commercialization”, supported by the World Bank and the Government of the Republic of Kazakhstan.
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