 Open Access
 Total Downloads : 227
 Authors : Jose A. RaynalVillasenor, M. Elena RaynalGutierrez
 Paper ID : IJERTV2IS90702
 Volume & Issue : Volume 02, Issue 09 (September 2013)
 Published (First Online): 23092013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Computation of Confidence Limits for the Two Populations Extreme Value Type I Distribution
Jose A. RaynalVillasenor
Civil and Environmental Engineering Department, Universidad de las Americas Puebla
72820 Cholula, Puebla, Mexico
M. Elena RaynalGutierrez and
Department of Civil Engineering, Monterrey Institute of Technology Campus Puebla
Via Atlixcayotl # 2301 72453 Puebla, Puebla, Mexico
Abstract
A methodology for obtaining the confidence limits for the two populations extreme value type I distribution is presented. The methodology is based on the application of the maximum likelihood method for estimating the parameters of the distribution and the confidence limits of the design events. The confidence limits are obtained by using of the variancecovariance matrix of the parameters and assuming a normality of the design events to compute them. Given the complexity of the likelihood function, its logarithmic form is used and a nonlinear multivariable constrained optimization method is applied to maximize such
distribution of extreme values, TCEV, [3], [4], [5]
and [6], the mixed Gumbel, [7], [8], [9], and the
mixed general of extreme value, [10] and [11]. [12] used different mixtures of normal, gamma and Gumbel distributions to test the relevance of using mixture models, by computing the marginal likelihoods of single distribution models, and to verify the presence of a persistence in the time series by comparing independent and identically distributed and Markovian mixture models.
2. The Extreme Value Type I Distribution Function
The extreme value type I distribution function, for the maxima, is[13]:
x x
function to produce the maximum likelihood
F (x) exp exp
01
estimators of the parameters and its confidence limits of the distribution. An example of application
1
(1)
of the proposed methodology is contained in the paper. The results showed an improvement in the standard error of fit and confidence limits narrower than those produced by the one
population procedure.
where and x01 are the scale and location parameters, respectively.
The probability density function is given by, [13]:
1 x x01
f (x)
exp exp
1. Introduction
1
1
The method of maximum likelihood has been
exp x x01
recognized like one of the best methods of estimation of parameters of functions of probability distribution, the properties of its estimators like asymptotic unbiasness and sufficiency, as well as
1
(2)
the consistency and efficiency, have been briefed frequently in technical literature, [1], [2], etc. This method also has the virtue of being able to handle very complex likelihood functions with an amazing flexibility. The use of mixed functions of probability to fit samples coming from two or more populations has been proposed from time back, [1]. In the particular case of the functions of
where: – x and 0

Two Populations Extreme Value Type I Distribution Function
Based in the general form for distribution function for two populations, [1] proposed:
F(x)mix (1 p)F(x;1) pF(x;2 )
(3)
LnL(x; x01,1 , x02 , 2 , p)
where p is the proportion of the second population
1 p
exp
x x 01
in the sample.
The two populations extreme value type I
N Ln 1
1
x x
distribution function can be expressed as:
i1
e xp exp
01
(x x )
1
F (x)mix
(1 p) exp exp 01
p x x x x
1
exp 02 exp exp 02
(x x
2 2
2
p exp exp 01
(8)
2
(4)
In the proposed procedure, eq. (8) has been
and the corresponding probability density function is:
maximized directly by using the wellknown non linear mutlivariable constrained Rosenbrock optimization method, [14].
f (x)
1 pexp x x01
mix
1
1
5. Estimation of the Confidence Limits for
x x
the Two Populations Extreme Value Type
exp exp
p x x02
01
1
x x02
I Distribution
The variancecovariance matrix for the two populations extreme value type I distributions, may
be expressed as:
exp exp exp
2 2
2
Varx Covx ,
Covx , x
Covx , Covx , p
01 01 1
01 02
01 2 01
(5) Covx , Var Cov , x Cov , Cov , p
01 1 1 1 02 1 2 1

The Method of Maximum Likelihood
V Covx01, x02
Covx ,
Cov1, x02 Cov ,
Varx02 Covx ,
Covx02,2
Var
Covx02, p Cov , p
The likelihood function for N independent and
identically distributed X1, X2,…, Xn can be obtained as the joint probability density function,
01 2
Covx01, p
1 2 02 2 2 2
Cov1, p Covx02, p Cov2 , p Varp
(9)
that is, [1]:
N
and its elements are:
L(x, ) f (x )
2 LL
E 2
2 2 2
E LL
E LL
E LL
E LL
E LL
E LL
2 1
E LL
E LL
i
i1
(6)
x01
2 LL
x011 x01x02 x012
2 LL 2 LL 2 LL
x01p
2 LL
E x
E 2
E x
E
E p
01 1 1
1 02 1 2
1
2 LL 2 LL
2 LL 2 LL
2 LL
where is the parameter vector and f (.) is the
V E x x
E x
E x2
E x
E x p
01 02
1 02
02
02 2 02
probability density function.
2 LL
2 LL
2 LL
2 LL 2 LL
E x
E
E x
E 2 E p
The logarithmic version of the former equation is:
01 2
1 2
02 2
2
2
2 LL
2 LL
2 LL
2 LL
2 LL
E x p
E p
E x p
E p
E p2
N
N
LnL(x, )
Lnf (x)
01 1 02 2
(10)
i1
(7)
The second partial derivatives, which expected values must be obtained to evaluate the variance
Based in the statements of the previous section, the
logarithmic likelihood function of the two populations exreme value type I distribution is:
covariance matrix for the two populations extreme value type I distribution, are:
x x01 x x02
F x;1 exp
F x; 2 exp
x x
1
x x02
2
1 exp 01
LnL
1 p N
1
x x02
x
2
DEN
LnL
1 p
exp
N
N
1
01 1
i1
2
x x
(11)
2
2
2 i1
DEN
F x;1 exp
01
(14)
x x
1
LnL
N f x;1 f x;2
01
x x
p i1
DEN
(15)
exp
N
N
01 1
LnL 1 p
1
where:
1
2
1 i1
DEN
DEN = f(x)mix (16)
x x
F (x;1 ) exp exp 01
1
x x
(17)
(12) F (x;2 ) exp exp
02
x x02
2
(18)
F x; 2 exp
1 x x
2
f (x,1 )
exp exp
01
x x
1
1
1 exp 02
LnL
1 p N
2
x x01
x02
2
2 i1
DEN
exp
1
1
x x
(19)
f (x, 2 )
exp exp 02
(13)
2
exp
x x
2
02
2
(20)

Results and Discussion
The gauging station Jaina in the state of Sinaloa, located in Northwestern Mexico, with period of record (19411991), has been chosen to show the procedure for obtaining the values of the confidence limits based in the two populations extreme value type I distribution. The initial values required by the procedure were estimated by using the computer code FLODRO 4.0 in [15]. The design values and their confidence limits for the one and two populations approaches are shown in Tables 1 and 2, respectively, and were obtained by using the computer code contained in [16].
Table 1. One population design values and their confidence limits for gauging station Jaina, Sin
(SE = 371.42)
(1) (2) (3) (4) (5)
Figure 1. Empirical and One Population Theoretical Probability Distribution Function
and Confidence Limits for Gauging Station Jaina, Mexico
The standard error of fitting (SE) has been computed as, [17]:
N
N
5
821
1002
1184
363
10
1044
1277
1509
465
20
1256
1541
1825
569
50
1528
1881
2235
707
100
1731
2137
2543
812
5
821
1002
1184
363
10
1044
1277
1509
465
20
1256
1541
1825
569
50
1528
1881
2235
707
100
1731
2137
2543
812
(xi yi) 2
1 / 2
SE i 1

Return Period (years)

Lower Limit (m3/s)

Design Value (m3/s)

Upper Limit (m3/s)

Interval Width between Confidence Limits (m3/s)
Table 2. Two populations design values and their confidence limits for gauging station Jaina, Sin (SE = 276.15)
(N mj)
(21)
(1)
(2)
(3)
(4)
(5)
5
886
1007
1129
243
10
1345
1498
1651
306
20
1756
1938
2120
364
50
2261
2479
2698
437
100 2631 2876 3121 490

Return Period (years)

Lower Limit (m3/s)

Design Value (m3/s)

Upper Limit (m3/s)

Interval Width between Confidence Limits (m3/s)
A graphical representation of these results can be observed in figures 1 and 2, for the one population approach and for the two populations model, respectively.
Figure 2. Empirical and Two Populations Theoretical Probability Distribution Function
and Confidence Limits for Gauging Station Jaina, Mexico
The Gumbels reduced variate, required to produce the abscissa axis in graphical displays of flood data, models applied and its confidence limits, is obtained as follows:
y = – Ln(Ln(11/Tr)) (22)
where Tr is the return period in years.
It is observed that the two populations model fits the flood sample much better (SE = 276.15) compared with the one population model (SE= 371.42). The two populations model produced a narrower confidence limits, too.
The application of the proposed approach is restricted to the fact that the computer code for the Rosenbrocks constrained multivariable method
must be available, given that performing the required computations for such method without a computer code is just out of the question.



Conclusions
A procedure for the obtaining of the confidence limits for the two populations extreme value type I distribution has been described here, based on the method of maximum likelihood. The procedure has given good results so far with the samples of data analyzed until now, one of which was used as an example of application of the proposed methodology. It can be observed that in this example of application, the standard error of fitting has been reduced significantly and the width of the confidence limits was reduced, too. Based on this arguments, the authors recommend the procedure here shown as an effective tool for annual flood frequency analysis when two populations are detected within a sample of flood data.

Acknowledgements
The authors wishes thank to the Universidad de las Americas Puebla for the support granted to make this publication possible.

References


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