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ON THE CONFIDENCE INTERVALS OF PARAMETRIC FUNCTIONS FOR DISTRIBUTIONS GENERATED BY SYMMETRIC STABLE LAWS D. Farbod, K. V. Gasparian

1. INTRODUCTION

The “Discrete Distributions Generated by Standard Symmetric Stable Densi-ties” (DSSD in short) are employed as a model for applications in Evolutionary Large-Scale Biomolecular Systems (Astola and Danielian, 2007; Astola et al., 2007, 2008, 2010). It is of interest to consider the statistical analysis of parameters esti-mators for the DSSD. A major drawback of the DSSD is that neither theirs prob-ability mass functions nor theirs cumulative distribution functions can be ex-pressed in closed form. The main aim of this paper is to discuss asymptotic con-fidence intervals of the ML Estimators for some parametric functions of such dis-tributions.

This paper is formed as follows. Section 2 briefly introduces Symmetric Stable

Laws. Section 3 considers DSSD and presents the asymptotic properties of the ML Estimators for such distributions. The main results of the paper are proposed

in Section 4. Section 5 concludes.

2. SYMMETRIC STABLE LAWS

The Stable Laws, introduced by Paul Levy in the 1920’s, form a rich class of probability distributions allowing heavy tails, skewness and have many other use-ful mathematical properties (Farbod and Gasparian, 2008; Nolan, 2010). We de-fine by ( , , , )S     the class of all stable distributions with the following parame-ters: the index exponent (0, 2], the skewness parameter  [ 1,1], the scale parameter   and the location parameter (0, ),    The index expo-( , ). nent  is the most important parameter of the Stable Laws and it measures how heavy-tailed the distribution is.

When  the sub-family ( ,0, , )0, S    is symmetric about . A symmetric stable distribution is called standard if   and 1   It is possible without loss 0. of generality to consider only standard symmetric stable distributions (Zolotarev,

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1986, p. 60). It is well-known that a standard symmetric stable random variable

X is best defined by its characteristic function (Zolotarev, 1986):

( ) exp( ), (0, 2].

X t t

   

In the present paper, we consider the class ( ,0,1,0),S   with parametric 1 space (compare to DuMouchel, 1973):

, , ,

{( ) : 0 1or 1 2, 0, 1, 0},

          

         

where  is some small constant, and let D be some subset of  whose closure

D is also contained in .

Then we know the following series expansion for density of the standard symmetric stable distribution from the class ( ,0,1,0),S    (Matsui and Take-1 mura, 2004; Zolotarev, 1986): 1 1 1 ) ( 1) 1 ( 1) sin , 0, ( ) ! 2 ( ; 1 1 1 , 0. ( ) j j j j x j if x a j s x if x b                               

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If x  the density function (1-a) is defined as ( ; )0, s x   s x( ; ). We know that for 0  the series (1-a) is convergent for each  1 x  and for 10,    2 may be justified as an asymptotic expansion as x   For more on this see . Zolotarev (1986). Moreover, for all x 

1 1 ( ; ) (0; ) 1 . s xs          

3. DISTRIBUTIONS GENERATED BY SYMMETRIC STABLE LAWS

There are different methods for constructing of parametric families of discrete distributions arising in Large-Scale Biomolecular Systems (see, for example, Kuznetsov, 2003; Danielian and Astola, 2004, 2006). One of them is based on

discretization of Stable Densities (see, for example, Astola et al., 2010). Let us

con-sider the following class of the DSSD constructed from (1).

For 0   or 1  1   and  2,  we have (see, for example, Farbod, 0 2011):

1

( ; ) ( ; )

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where 0 ( ; ). y cs y   

The main goal of the present paper is to consider some functions of parame-ters involved (2), and then to obtain the asymptotic confidence intervals of the

ML Estimators for this functions.

3.1. ML Estimator

Let n ( 1,..., )

n

XX X , with realization xn( ,...,x1 xn), be a sample from (2).

Let us assume the following regularity conditions (compare to Borovkov, 1998):

1. D is a compact set of ;

2. p x( ; )1p x( ;2) for all 12 where  1, 2;

3. Probability distributions  with probability mass functions as in (2) have  a common support, i.e. the set

Support   { : ( ; ) 0},x p x  

does not depend on   ;

4. The function ( ; ) ln ( ; )l x   p x  is twice continuously differentiable with respect to  for all x 0,1, 2,... In addition,

2 2 ( ; ) ( ), l xM x     where E M X[ ( 1)]  ;

5. For all    the Fisher’s information quality ( )I  contained in observa-tion X satisfies to the following condition 1,

2 2 1 1 2 ( ; ) ( ; ) 0 I( ) El X El X ,                      and is continuous in .

It was proved (Farbod, 2007) that the regularity conditions mentioned above are satisfied by the model (2). Under this conditions, we have the following well-known Theorem (Borovkov, 1998; Lehmann, 1983) about asymptotic behavior of the ML Estimators.

Theorem 3.1: Suppose that the regularity conditions 1-5 are fulfilled. Then the

like-lihood equation ln ( ; ) 0, n L x     

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with L X( n; ) ni1p X( i; ) has a unique solution ˆnˆn(Xn) in .D This

solution is a ML Estimator for  and has the following properties: (I) Consistency, i.e.

ˆn  ,

   ((ˆn  )0   0as n   ) (II) Asymptotic normality, i.e.

2 ˆ

ˆn ( n ) d (0, ( )).

un     N   (3)

(III) Asymptotic efficiency: the asymptotic variance  2( ) in (3) is 2( ) I 1( ),     such that 1 ˆ ˆ ( ) d (0, ( )). n n u n N I 

(IV) Convergence of moments: ˆ

[ ]k [ ]k 1.

n

E u E  k (4)

From (4), when k  the property of asymptotic unbiasedness also holds, that is 1,

1 2 ˆ

[ ]n ( ).

E    o n

(V) If h t is a some differentiable function on ( )  such that ( ) 0h  , then:

2 ˆ [ ( )] ( ( ) ( )) 0, . ( ) d n h n h h N I               (5)

Remark 3.1: Substituting k  into (4) we have 2

2 ˆ 1 (1) ( ) . ( ) n o E n I     

Also, (5) can be represented as

2 2 ˆ [ ( )] ( ( ) ( )) (1 (1)). ( ) n h E h h o n I          

Remark 3.2: The estimator ˆn is asymptotically optimal (see, for instance, Leh-mann, 1983), that is for all asymptotically normal  estimators which satisfy

condition (II) with asymptotic variance ( ( )) ,  2 the following inequality holds: 1( ) lim [ (ˆ ) ] lim2 [ ( ) ] ( ( )) .2 2

n n n n

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4. THE CONFIDENCE INTERVALS OF PARAMETRIC FUNCTIONS

We consider the questions about estimators and their properties for some “useful” functions of the index  Before that, let us prove the following Lemma: .

Lemma 4.1: The function ( )I  of the model (2) admits the following representa-tion

1

( ) { ( ( ; ))},

I  Var U s X  (Var means Variance),

where 1 1 1 1 1 1 ( ; ) ( ; ) ( ( ; )) (ln ( ; )) , ( ; ) , ( ; ) s X s X U s X s X s X s X                 is a

contribution function of X for the stable law (1), and 01 I( )   . Proof. It is readily seen that

( ) ( ; ) ( ( ; )) (ln ( ; )) , ( ; ) c s x U p x p x s x c             ( )c ( )c ,      such that keeping in mind the representation

1 1 0 ( ; ) [ ( ( ; ))] ( ) ( ) . x s x E U s X c c c          

 

we have (compare to Farbod, 2011)

2 2 2 1 1 ( ) 1 ( ) [ ( ( ; ))] [ ( ( ; ))] c { ( ( ; ))}. I E U p X E U s X Var U s X c                

It is obvious ( ) 0.I   To prove I( ) Var U s X{ ( ( 1; ))}   it suffices to , show that 2 2 1 0 ( ( ; )) 1 [ ( ( ; ))] , ( ; ) x s x E U s X c s x          

 

which is met obviously. The Lemma 4.1 is proved.

Let us now find the asymptotic confidence intervals for parametric function ( )

h of  :

Theorem 4.1: If the regularity conditions 1-5 are satisfied for the model (2) and

( )

h t is some differentiable function on such that ( ) 0h  ( is a true value of parameter), then the  -level (0< <1) asymptotic confidence interval for

( )

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2 ˆ ˆ ˆ ( ) ( ) ( ) n n n h h z n I        , where 2 z is 2 

-level critical point for standard normal law.

Proof. From property (V) of Theorem 3.1 one can obtain that if n   ˆ ( ) ( ( ) ( )) (0,1). ( ) d n n I h h N h        

Then, by the Continuity Theorems (Borovkov, 1998) we have

ˆ ˆ ˆ ( ) ( ( ) ( )) (0,1), ( ) n d n n n I h h N h         such that 2 2 ˆ ˆ ˆ ˆ ˆ ˆ ( ) ( ) ( ) ( ) ( ) 1 , ( ) ( ) n n n n n n h h P h z h h z n In I                            

when n   The proof is complete. .

We consider some useful parametric functions:

Example 1. Let h( ) I( ). Then by Theorem 4.1 we have the following

 level asymptotic confidence interval for ( )I  :

2 ˆ ˆ ˆ ( ) ( ) ( ( ) ) 1 , ( ) n n n I P I I z n I                     

where (see Lemma 4.1)

1 1

( ) 2 [ ( ( ; )), ( ( ; ))].

I  Cov U s X  U s X  (Cov means Covariance).

Example 2. Let hx( ) p x( ; ) s x( ; ),

c

    where x N {0} is fixed. Again us-ing Theorem 4.1 we obtain:

2 ˆ ˆ ˆ ( ; ) ( ; ) ( ( ; ) ) 1 , ( ) n n n p x P p x p x z n I                    

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where ( ; ) ( ; ) p x p x        1 s x( ; ) ( )c s x( ; ) c c             U s x( ( ; )) ( ; )  p x   p x( ; ) E U s X[ ( ( 1; ))]  p x( ; ) { ( ( ; ))  U s x  E U s X[ ( ( 1; ))]}.

Example 3. Set now ( )t ( ) 1 ( ) ( ; ),

x t

hF tF tp x

 

   

where t (0, ) . From Theorem 4.1 we have:

2 ˆ ˆ ˆ ( ) ( ) ( ( ) ) 1 , ( ) n n n F t P F t F t z n I                     where ( ) ( ; ) x t F tp x     

 ( ; ) ( ; ) ( ) ( ; ) ( ; ) x t x t c s x p x p x s x c             

E U s X[ ( ( 1; ))] 1  X t1 E U s X[ ( ( 1; ))] F t( ) (1X t1 F t( ))E U s X[ ( ( 1; ))]. (Here 1 1 1 1 , 1 0 . X t if X t if X t       ). Example 4. Assuming 0 ( ) ( ; ). y hcs y    

By Theorem 4.1 we have: 2 2 ˆ ˆ ˆ ˆ ˆ ˆ ( ) ( ) 1 , ( ) ( ) n n n n n n c c P c z c c z n In I                          

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where 1 0 ( ; ) ( ) ( ; ) [ ( ( ; ))]. ( ; ) x s x c c p x c E U s X s x              

 

Example 5. Let us have h( ) ( )c .

c

 

   Using Theorem 4.1 we have:

2 2 ˆ ˆ ˆ ˆ ˆ ˆ (ln ) (ln ) (ln ) (ln ) (ln ) 1 , ( ) ( ) n n n n n n n c c P c z c c z n In I                           where 1 ( ) (ln )c c E U s X[ ( ( ; ))]. c               5. CONCLUSIONS

In this paper, the asymptotic confidence interval for the parametric function ( )

h has been obtained (Theorem 4.1). We also considered some useful para-metric functions of the index parameter  (Examples 1-5). With the help of Theorem 3.1, Lemma 4.1 and Theorem 4.1 the asymptotic confidence intervals for such parametric functions have been proposed.

This theoretical results may be applied for obtaining the respective statistical inferences in Bioinformatics, and in other theories where the Stable Laws are used, for example, in Financial Mathematics and Economics.

ACKNOWLEDGMENT

The authors would like to express their sincere thanks to the referee for the helpful suggestions.

Department of Mathematics, DAVOOD FARBOD

Quchan Institute of Engineering and Technology, Quchan, Razavi Khorasan Province, Iran

Chair of Probability Theory and Mathematical Statistics, KAREN V. GASPARIAN

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REFERENCES

J. ASTOLA, E. DANIELIAN, (2007), Frequency Distributions in Biomolecular Systems and Growing Net-works, Tampere International Center for Signal Processing (TICSP), Series no. 31, Tampere, Finland.

J. ASTOLA, E. DANIELIAN, A. ARAKELYAN, (2007), Frequency distributions in growing biomolecular net-works based on stable densities, Journal Reports National Academy of Sciences of Armenia,

107(1), pp. 26-36.

J. ASTOLA, E. DANIELIAN, A. ARAKELYAN, (2008), On distance in variation for frequency distributions generated by stable laws, Journal Reports National Academy of Sciences of Armenia,

108(2), pp. 99-109.

J. ASTOLA, E. DANIELIAN, S. ARZUMANYAN, (2010), Frequency distributions in bioinformatics, A Re-view, Journal Proceedings Yerevan State University: Phys. Math. Sci., 223(3), pp. 3-22. A. A. BOROVKOV, (1998), Mathematical Statistics, Gordon and Breach Science Publishers,

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D. FARBOD, K. V. GASPARIAN, (2008), Asymptotic properties of maximum likelihood estimator for some discrete distributions generated by Cauchy stable law, Statistica, Bologna, 68(3), pp. 321-326. V. A. KUZNETSOV, (2003), Family of skewed distributions associated with the gene expression and

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SUMMARY

On the confidence intervals of parametric functions for Distributions Generated by Symmetric Stable Laws

In this paper we consider “Discrete Distributions Generated by Standard Symmetric Stable Densities” (DSSD in short) arising in Bioinformatics (Astola and Danielian, 2007). Using well-known asymptotic properties of the maximum likelihood (ML) estimators we obtain the respective asymptotic confidence intervals for some useful parametric func-tions of the DSSD.

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