Se RANDOM

Vol.

2

No.

2

(1979)

309-323

CONVERGENCE OF WEIGHTED SUMS OF INDEPENDENT RANDOM VARIABLES AND AN EXTENSION TO BANACH

SPACE-VALUED RANDOM VARIABLES

W.J. PADGETT and R.L. TAYLOR

Department of Mathematics, Computer Science, and Statistics University of South Carolina, Columbia

Columbia, South Carolina 29208 U.S.A.

(Received June 23, 1978)

ABSTRACT. Let {X

k}

be independent random variables with EX_k 0 for all k and let

{ank:

^{n > i, k >} i} be an array of real numbers. In this paper

n

the almost sure convergence of Sn E ank

Xk,

^{n 1,2, to a constant is}

k=l

studied under various conditions on the weights

{ank}

and on the random variables {X

k}

^using martingale theory. In addition, the results are extended to weighted sums of random elements in Banach spaces which have Schauder bases. This

extension provides a convergence theorem that applies to stochastic processes which may be considered as random elements in function spaces.

KEY WORDS AND PHRASES. Weighted Sums, Strong Law of ^Large Numbers, ^{Almost Se} Convergence, Generzed

Gaussian

Random Variables, Random Elements

in

Banach Space, Schauder Basis.

AMS (MOS) SUBJECT CLASSIFICATION (1970) CODES. Primary 60F15,

60B05;

Secondary

60G99.

J. PADGETT AND R. L. TAYLOR i. INTRODUCTION.

Let

(, F, P)

denote a probability space and let

}

^{be a sequence of}

independent random variables with

E

0 for all k

1,2,

Let

{ank

^{n e i, k e i} be an array of} real numbers and define S

^Z

^a n k=l nk

’

n 1,2,

Several results have been obtained in recent years concerning the almost sure convergence of the sequence

{S }

under various conditions on the weights

n

{ank}

and boundedness conditions on the random variables

{}

^{or on their}

moments. For example, Chow (1966) and Stout

(1968)

required conditions on

{ank}

^{such as (i)}

^E

^ank^{2 < K n}

^Y

^{for some constants K and}

^y

^or

(ii) A

Z

^{2 %}

n

ank

^{< for all n and l}

exp(--)

^{< for all % > 0, and}

k--i n--i n

either a uniform bound on certain moments of the random variables

{}

^{or the}

(a2

²

condition that for all k,

E[exp(t)]

^{< exp}

= ^/2)

^{for some a > 0 and all}

real numbers t.

Stout’s (1968)

results concerned the complete convergence of

{S n}

ⁱⁿ the sense of Hsu and Robbins (1947) which implies almost sure con- vergence. Also, Rohatgi

(1971)

considered the almost sure convergence of

{Sn ^}

^to zero by requiring the uniform dominance of

{}

ⁱⁿ the sense that there exists a random variable X such that

e[lla] ^e[IXla]

^{for all a > 0}

and all k, where X has a finite (I

+ I/a)th

absolute moment and >0 is such that

maXlank

⁰

^{(n-a). (It}

^was also assumed that lim

ank

0 for all k and

k n-o

Z lank

^{C for all}

^n.)

^More recently, Chow and Lai (1973) have studied

k=l ⁱ

the almost sure converzence of

{n-

S

}

(some i

2)

for independent and identically distributed random variables with finite th absolute moments and with somewhat weaker conditions on the array

{ank}

^{than those in Stout}

^(1968).

Lai

(1974)

has indicated the importance of weighted sums in control charts.

Chow

(1966)

defined a random variable X to be generalized Gaussian if there exists a number > 0 (referred to here as the parameter) such that for every real number t, E[exp(t X)] ^< exp(²

t2/2).

Special cases of generalized Gaussian randomvariables are normal with zero means or bounded, symmetric random variables.

In Section 2 of this paper the definition of a

totally .$eneralized

Gaussian random variable is given. Thenthe almost sure convergence of

{S }

n for independent (but not necessarily identically distributed) random variables which are totally generalized Gaussian is obtained using martingale theory with very general conditions on the sequence of weights

{ank}.

^The

relaxation of the restrictions on the weights which is achieved in these re- sults is summarized and discussed in Section 3.

Also

^{in Section 4 the}

results for random variables are extended to sequences of independent random elements in separable Banach spaces. This extension will provide a convergence theory for weighted sums of stochastic processes which

mav

^be considered as random elements in function spaces, such as the Wiener process on a closed interval [0, T] for some T >0 [see Billingsley

(1968)].

2. WEIGHTED SUMS OF RANDOM VARIABLES.

Let

{ank:

^{n > i, k > i} be a} double sequence of real numbers satisfying the following conditions:

(i)

ank

^> 0 for all k and n,

an_l,

k ^>

ank

for k 1,2,...,n-i and

E

>

E

for all n;

k=n

an-l,k

k=n

ank

(2) ^Y.

ank

2 ^_<

^F

^{for all n} and some positive constant

r;

k=l

2 _/

0 as n ^/

;

^and 3

E

anl "

k=n+l

(4)

lira

ank

0 for all k.

n

L. TAYLOR

A sequence

{ank}

^{satisfying Conditions}

^(1)-(4)

^{will be called a Type (P)}

sequence.

Conditions (1)-(4) will be considered in detail in Section 3, and sev- eral examples of sequences which are of Type

(P)

will be given there. Those examples will contrast previous results with the results which are given in this section and will show that the weights can be very different from the traditional weights

ank

ⁿ

-I

^{if k i,..., n. However, in Section 3, the}

traditional weighting is shown to be a special case of a Type (P) sequence k-3/4

if k n

+

i, by letting

ank

DEFINITION i. ^l,et X be a random variable such that its expected value EX exists. Let I

A ^{be the indicator random variable for the event} A, and let X

+

X

I[X

> 0] ^and

X-

-X

I[X<0].

^{Then X will be said to be totally}

generalized Gaussian if X

+-

EX

+

_is

generalized Gaussian with parameter

< 2

1/2

and

X- EX-

is generalized Gaussian with parameter < 2

1/2

We now prove the following theorem.

THEOREM i. Let

{}

be independent random variables with

E

^{0 for}

each k and let

{ank}

^{be a Type}

^(P)

^sequence. If for each k, is totally generalized Gaussian, then there exists a constant y such that as n-o

n

$ 7

n k=l

ank ^y

almost surely.

PROOF. For each k, let X

k and be defined as in Definition i.

Then

- ^,

^{k 1,2 Also, define}

^F+

ⁿ

^{X

^andX

^+}

^{n to be}

sigma-field generated by

X1,...

n n

ank

n k=l

S

E

a for n 1,2,

nk n kffil

{}

^are independent random variables, and by hypothesis we may Now,

obtain for every k

_< exp (t²

+ tEXt)

^(2.1)

for all real numbers t. Define

n

n n k=n+l

ank

k=l

. ^akk

^g

^),

^{n 1,2,}

Then for n 2 by Condition

(I)

^on

{ank}

and inequality (2.1) ^{we obtain} n

n

n-I

_k=n+l

ank

^exp _k=l

n-i 2

F+

^]

k=l k=n+l

n

x =p(-

I

n-I

ⁿ

k=l k+l k=l

n-i 2 _v

+

( ank ⁺ ank)P(a

²

⁺

^a

E)

i k=n+l ^{nn nn}

n

xexp

(- I a )

k=l

n-i n-i

=p(

_k=l

ank K+

_k

^ank

^{2 p}

^(-

_k=l

^akk

^g

n-i 2

n-I

k=l k n-i

k-

i

Y-I

^a.s.

Hence,

{Y}

^{is a pertinEale th respect to}

{}.

For each n

2 n

EIY+

_n ^E[exp(S

⁺

_n

⁺ ank- akk E.)] v+

k=l k=l

[

E[P(ank ^)]]p ank-

^E

⁾

kffil kffin+l i

n 2

+

^{2 n}

[H

P(ank ⁺ _ank E.)]

^p(

_ank- a)

k=l k=n+l

2 n

exp

ank)

^p

^[[ (ank a) E+]

k=l k=l

_< exp

(r)<

by inequality (2.1) and Conditions (i) (that

an_l,k

^>

ank

^{for k 1,2, n-i}

and (2) on the

{ank}.

^{Thus sup}

EIY+I

^<

^o,

^{and by the martingale} convergence n

theorem [Tucker

(1967)],

there is a random variable Z

y+

I

^{such that}

n/Z

I ^a.s.

By Condition (3) of a Type

(P)

sequence, there exists a random variable Z

I

such that S

+

ⁿ

n- Ek=l akk

^{E /Z}

I

^a.s. Similarly, there exists a random variable Z

2 ^{such that}

S-n ik=in ^{akk E/Z}

^{2 a.s. Next, EXk 0 implies that}

E

E. ^Hence,

^Sn

$/

^{n Sn}

n n

(Sn ⁺ Y’k=l akk m)

^(Sn

Y’k=l ^{akk m)/}

^{ZI Z2 Z a.s.}

By Condition (4) of Type (P) sequence, for each j 1,2,...

anj Xj

⁰

a.s. as

n-o.

Therefore,

Sn ^{(anlXl + +} an,k_l

_n

-i ^{+ (ank +}

+

ann nX ^/ Z a.s. implies that for every k, ^a X ^/ Z a s Thus, j--k

n

^j

Z is measurable with respect to the tail sigma-field of

{}

^{and there exists}

a constantysuch that P[Z --y]

I,

Tucker (1967, p. 75). Therefore, S /y n a.s.

III

If, in addition to the Conditions (1)-(4), ^the sequence

{ank}

^satisfies

n 2

the condition that Y.

ank

^{0 as}

^n-o,

then the following theorem may be ob- k=l

tained.

THEOREM 2. Let

{}

^{be a} sequence of independent random variables with EXk 0 for all k, and let

{ank}

^{be a Type (P) sequence. If is totally}

n 2

generalized Gaussian for each k, and if

ank

^{/ 0 as n /}

,

^then

n k=l

Sn k=F’l ^ank +

^{0 almost surely as n /}

.

PROOF. By Theorem i, there exists a constant y such that S * y almost n

surely. Hence, S ^/

’

ⁱⁿ probability. Let S

+

and

S-

be defined as in the

n n n

+

n

E$

+ _r.

EXI

^By Chebyshev’s inequality proof of Theorem i. Define

n

ⁿ _k=l

^ank

for 4>0

n- n

^{>] -< var(S} _k--i

^ank

^vat(

^).

Since

0 E

^is generallzed Gausslan with < 2 and mean zero, var

() E(0 ⁾²

^is uniformly bounded for all k by a positive constant B.

Thus,

p[

IS ⁺

_n

n ^{+ I> ]--2B ank}

^{2 / 0 as n /} by hypothesis.

k=l That is, S

+ +

n

n

^{/ 0 in} probability as n ^/

.

The same argument gives S

n

n

^{/ 0 in} probability as n ^/

.

^{Hence, com-}

bining the two results yields

Sn--(S+ _-n n ^{+) (Sn-} n

^/ 0 in-probability as n ^/

.

^This implies that

y

0 since the limit in probability is unique.

That ^is S ^/ 0 a.s.

///

n

To obtain convergence of weighted sums of random variables ^which are not identically distributed, dominance of the random variable by an integrable random variable or in some other sense is not an unusual condition {see Chow

(1966),

Stout

(1968),

or Rohatgi

(1971)).

However, it is a troublesome res- trictlon. The strength provided by the results of this section is in the relaxation of conditions on the weights

{ank}.

^An examination of the weights which are of Type

(P)

^is provided in the next section.

3. CONDITIONS ON THE WEIGHTS.

In this section the conditions on the weights will be examined. In par- ticular, the Type

(P)

sequences will be shown to be different from the weights used in previous results. A strong law of large numbers can be obtained from

n

these results, but the sequence

{ 7. _ank:

^{n >_}

^I}

^{need not be bounded in gen-}

k=l eral.

The first example will consist of weights

{ank}

^{which satisfy Conditions}

TAYLOR

n 2

(1)-(4) and Y.

ank

^{/ 0 as n /} but which do not satisfy the conditions of k=l

Theorem 4 of Stout

(1968)

or Theorem

I

of Chow

(1966).

Define i

[Zn(n+l)]

²

k-(l+)

k,n 1 2

ank

where >0. Then

(I) (4)

hold and for all n nY. 2

[#.n(n+l)

]

-I

ⁿZ i k=l

ank

k=l k²⁺²⁽

However,

A 7.

ank

n k=l

-< [Zn(n+l)] X ’2’2:

^{/ 0 as n /}

k=l k

i

k__E

^{i i}

^X

^where

Zn

(n+l)

i k2+2e

Za

(n+l)

X k2+2:

^{>0. Thus,}

^X exp(-X/A_),, ^X exp[-Zn(n+l)]

n=l k=l n=l

X

_n+l n=l

=,

^which does not satisfy the hypothesis of

Stout’s (1968)

Theorem 4 or

Chow’s (1966)

Theorem i.

For the next example, define

n

-I

if k 1,2,..., ⁿ

ank

k

-3/4"

if k n+l, n+2,

This sequence satisfies Conditions

(1)-(4),

and hence a strong law of large numbers is available from the results of Section 2.

However,

since the second moments of the random variables in Theorem 2 can be uniformly bounded,

Kolmogorov’s

criterion is easily satisfied for the weights

ank

ⁿ

-I

^i-<k<n, and convergence of S follows immediately. Thus, these results are most use-

n n

ful when considering nonuniform weighting where

{ I ank ^n>

^{i} may be}

k=l unbounded. For example, define

ank

n if k 1,2,...,n

7.

3/2

^{if k n+l}

j=n+l

J

0 _if k >n+l.

This sequence is also of Type

(P).

Conditions (i) and

(2)

imply that

Z a,.,.

^<

F.

k--i

Thus, Kronecker type arguments would suffice for weights

{ank}

^{where the de-}

crease down the diagonal of the summability matrix offsets the possible decrease along the rows, for example, if

a > However letting

n,k+l akk ank ak+l,k+l"

ank

n-1/2 ^/n(k+7)

^{if k-- i,..., n-i}

n-i if k=n

Z j-2)1/2

J--n+l

if k n+l

0 ^if k > n+l

defines weights which are of Type

(P)

but which have the property that for each k there exists an n such that

an,k+l akk

^<

ank ak+l, k+l"

^Moreover,

n-k ^if k l,...,n-I

n-i if k=n

j-2)1/2

j=n+l

if k n+l

0 if k> n+l

defines extremely nonuniform weights which are of Type

(P)

but such that for every n >2,

an,k+ I akk

^<

ank ak+l,k+ I

^{for each k i,..., n-2.}

An exhaustive discussion of the weights which are of Type ^(P) will not be presented, but it is important to note that this more general condition on the weights is balanced by the assumption of total generalized Gaussian nonidentically distributed random variables.

4. EXTENSION TO RANDOM ELEMENTS IN A BANACH SPACE.

In this section an extension of Theorem 2 for random elements in a Banach space will be obtained. The study of random elements in abstract spaces was inspired by the consideration of stochastic processes as random elements in appropriate spaces of functions

[see,

for example Mann

(1951)

and Billingsley

(1968)],

and various properties of random variables have been extended to random elements. In particular, laws of large numbers for random elements in abstract spaces have been studied extensively [see Padgett and Taylor

(1973),

for example, and Alf

(1975a)

for a more recent result]. Also, Alf (1975b) has extended the results of Jamison, Orey, and Pruitt (1965) to weighted sums of random elements in a Banach space.

Let

X

denote a

(real)

separable Bam_h space with norm

II ^II,

^{and let}

(, F,

P) be a probability space. A random element V in

X

is a measurable function (with respect to the smallest sigma-field generated by the open sub- sets of

X)

from into

X.

The random elements

{V }

in

X

are said to be iden- tically distributed if their induced probabilities on

X

are the same.

Further,

{V }

are said to be

independent

^{if for} every finite collection n

{BI,

^B

k}

^{of Borel subsets of}

^X

k

P[VI

^BI,

..., VkB ^k]

_i=l

^P[ViBi].

An

expected

value for a random element V in

X

will be defined by the Pettis integral. That is, V has expected value

EVX

if f(EV) Elf(V)] for every

continuous linear functional f on

X.

A Schauder basis for a Banach space

X

is a sequence

{b i}

^c

^X

^{such that}

for each

xeX

there exists a unique sequence of scalars

{t i}

^satisfying

x lira Y. t i b

i. A sequence of linear operators

{U }

can be defined on

X

n-o i=l n

by

n

Un(X)

^l

fi(x)bi,

^{n-- 1,2,}

i=l

for

xe

where f

i(x)

^{t. is the} ith coordinate functional for the basis. For a Banach space

X,

the coordinate functionals are continuous linear functionals on

X.

Further, a sequence of linear operators

{}

^{may be defined on}

^X

^by

(x)

^x

Un(X), ^xX,

^{n 1,2, It is} well-known that for a Banach space there exists a basis constant m. >0 such that

IUnl ^l<m

^{for all n, and}

hence

II

^{< m}

⁺

^{i for all n, Marti}

⁽¹⁹⁶⁹⁾

and Wilansky

(1964).

Theorem 3 extends Theorem 2 to Banach spaces which have Schauder bases.

A definition is needed first.

DEFINITION 2. Let V be a random element in a Banach space

X

which has a Schauder basis with coordinate functionals

{f.}.

Then V is coordinatewise totally generalized Gaussian if f

i(V)

^{is a totally} generalized Gaussian ran- dom variable for each i 1,2,

THEOREM 3. Let

X

be a Banach space with a Schauder basis

{b i}

^{and let}

{V k}

be independent ^random elements in

X

such that EV

k 0. Let

{ank}

^be

a Type (P) sequence. Suppose that for each k 1,2,..., V

k is a coordinate- wise totally generalized Gaussian random element (with respect to the basis

{bi})

that for sufficiently large positive integers p the random variables

{llQp(Vk) II-gllqp(Vk)ll}k=l

are generalized Gaussian wlth parameters

k<21/2,

and that as

p-o

^there exist constants

Cp, Cp/

^{0, such that}

320

sup

Z

^{n 2}

k=,

nkEIIQp(Vk) ^ll -<C. fk=. ^{an" -*n/’}

^:hnn/

Is=ll ^II

_kl

^{ank Vkll/}

^{0 almost surely.}

PROOF. For each n and p, write

n n n

S ^7, V

k Z

Up(V ^{k) +}

^I

Qp(Vk)

n k--i

ank

k--1

ank

k=l

ank

^(4.1)

Let p be a fixed positive integer and consider

n p n

lUp (k=Zl ^{ank Vk) ll II}

i=

1

^f

^{i(kZl= ank}

^V

^{k)bi l"}

p

n

_<

Z Z

ank fi ^{(Vk) l"} Ibi ^I,

i=l k=l

(4.2)

where

(fi ^}

^are the coordinate functionals for the basis. Now since

lfil

^{0, i-- i,..., p, for each i--} 1,2,..., P’

(fi(Vk)}mkll

^{is a sequence}

of independent random variables with

E[fi(Vk) fi(Evk)

^{0 for all k. Thus,}

by Theorem 2, since f

i(Vk)

^is totally generalized Gaussian for each k and i=

n

1,2,..., p,

Z ank fi ^(Vk)

⁺ 0 almost surely as n ^/ for each ^i. Thus, kl

from (4.2) for each p there ^exists an event with

P(p)

0 such that P

m implies that for > 0 there is an integer N

I

^so that for n>N

P _n I

Iup(kZ__l ^{ank Vk())}

^<

.

^(4.3)

n

Now, consider

lQp( k=IZ ^{ank Vk) II"}

^{For each p,}

^{{Qp(V k) }k= I}

^{is a}

sequence of independent random elements since

Qp

^is a continuous linear operator Thus,

{(llQp(V k) ll-EIIQp(Vk)ll)}

k-i are independent random variables with zero means for each p. Thus, by Theorem 2, since

E

Iqp(Vk)

^{is (totally)} generalized Gaussian for each k (and sufficiently large p), as n ^/

n

l

ank[l IQp(Vk) EIIQp(Vk) ^I]

^{/ 0}

^(4.4)

k=l

almost surely.

Now,

n n

lQp(kE=l ^{ank Vk) ll II}

_k=l^7.

^{ank Qp}

n

< l

ank[IIQp(Vk) ^II

^E

lIQp

k=l n

+

Y.

ank EIIQp(Vk) ^II.

k=l

(4.5) But,

by hypothesis sup

Z

ⁿ

n k=l

ank Ell Qp (Vk) II<Cp"

^For

^>

^{0 and a large}

(fixed)

Po’

^there exists an integer N

2 ^such that for nN_2, n

Z an_

k

El IQpo(Vk)

^<

"

^Also for sufficiently large

Pl

^>

Po

^and

I ^a

_O

k=l

where

P()

^{0, there exlsts an integer N}₃ ^{such that nN}₃ implies from (4.4) that

Y.

ank[l IQp(Vk())

^E

IQp(Vk) ^I]

^<

-.

k=l

(4.6)

Therefore, from (4.i), (4.3),

(4.5)

and (4.6) for

0

^u

,

^where

g0--pl

^f_P ^{for p}

Pl’

^{and for n _> max}

{N1, N2, N3},

[[Sn()[]

^<

[]Up(kE__l ^{ank Vk())[l +} [[QP(k =ZI ^{ank Vk())[[}

^<

^{" III}

Theorem 3 gives convergence results for random elements which need not be identically distributed and which do not have restrictive moment conditions

(see the results of Padgett and Taylor

(1976),

^for example). Also, the con- ditlons on the weights

{ank}

^are very general as indicated in Section 3.

Moreover, the uniform dominance in probability of Rohatgl

(1971)

and Padgett and Taylor

(1974)

^is eliminated by the modified generalized Gausslan type of condition. Finally, if

X

Rⁿ with the usual norm, then Theorem 3 gives a convergence theorem for n-dimenslonal random vectors, and since f

l(x)

⁰

W. J. PADGETT AND R. L. TAYLOR

for 1>n, xCR

n, _Qp(X)

0 for p>n, and only Inequality (4.3) ^is needed.

These results may be applied to stochastic processes. For example, Theorem 3 may be applied to sequences of separable Wiener processes on

[0,i]

(or [0,T])

since such processes may be considered as random elements in the Banach space C[0,1] [Billlngsley

(1968)]

which has a Schauder basis.

REFERENCES

i. Alf, Carol. Rates of convergence for

the

laws of large numbers for independent Banach-valued random variables, J. Multivariate Anal.

5

(1975a)

322-329.

2. Alf, Carol. Convergence of weighted sums of independent Banach-valued random variables

(abstract),

I. M. S. Bulletin 4 (1975b) 139.

3. Billingsley, P.

Convergence

of

Probability Measures,

^Wiley, New York, 1968.

4. Chow, Y. S. Some convergence theorems for independent random variables, Ann. Math. Statist. 37 (1966) 1482-1493.

5. Chow, Y. S. and Lai, T. L. Limiting behavior of weighted sums of in- dependent random variables, Ann. Prob. i

(1973)

810-824.

6. Hsu, P. L. and Robbins, H. Complete convergence and the law of large numbers, Proc. Nat. Acad. Sci. U. S. A. 33

(1947)

25-31.

7. Jamison,

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Journal of Applied Mathematics and Decision Sciences

Special Issue on

Intelligent Computational Methods for Financial Engineering

Call for Papers

As a multidisciplinary field, financial engineering is becom- ing increasingly important in today’s economic and financial world, especially in areas such as portfolio management, as- set valuation and prediction, fraud detection, and credit risk management. For example, in a credit risk context, the re- cently approved Basel II guidelines advise financial institu- tions to build comprehensible credit risk models in order to optimize their capital allocation policy. Computational methods are being intensively studied and applied to im- prove the quality of the financial decisions that need to be made. Until now, computational methods and models are central to the analysis of economic and financial decisions.

However, more and more researchers have found that the financial environment is not ruled by mathematical distribu- tions or statistical models. In such situations, some attempts have also been made to develop financial engineering mod- els using intelligent computing approaches. For example, an artificial neural network (ANN) is a nonparametric estima- tion technique which does not make any distributional as- sumptions regarding the underlying asset. Instead, ANN ap- proach develops a model using sets of unknown parameters and lets the optimization routine seek the best fitting pa- rameters to obtain the desired results. The main aim of this special issue is not to merely illustrate the superior perfor- mance of a new intelligent computational method, but also to demonstrate how it can be used effectively in a financial engineering environment to improve and facilitate financial decision making. In this sense, the submissions should es- pecially address how the results of estimated computational models (e.g., ANN, support vector machines, evolutionary algorithm, and fuzzy models) can be used to develop intelli- gent, easy-to-use, and/or comprehensible computational sys- tems (e.g., decision support systems, agent-based system, and web-based systems)

This special issue will include (but not be limited to) the following topics:

• Computational methods: artificial intelligence, neu- ral networks, evolutionary algorithms, fuzzy inference, hybrid learning, ensemble learning, cooperative learn- ing, multiagent learning

• Application fields: asset valuation and prediction, as- set allocation and portfolio selection, bankruptcy pre- diction, fraud detection, credit risk management

• Implementation aspects: decision support systems, expert systems, information systems, intelligent agents, web service, monitoring, deployment, imple- mentation

Authors should follow the Journal of Applied Mathemat- ics and Decision Sciences manuscript format described at the journal site http://www.hindawi.com/journals/jamds/.

Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Track- ing System athttp://mts.hindawi.com/, according to the fol- lowing timetable:

Manuscript Due December 1, 2008 First Round of Reviews March 1, 2009 Publication Date June 1, 2009

Guest Editors

Lean Yu,Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;

Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;

[email protected]

Shouyang Wang,Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; [email protected]

K. K. Lai,Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; [email protected]

Hindawi Publishing Corporation http://www.hindawi.com