Tải bản đầy đủ - 0 (trang)
Appendix. Some Generalities on Orlicz Young Functions and Orlicz Spaces

Appendix. Some Generalities on Orlicz Young Functions and Orlicz Spaces

Tải bản đầy đủ - 0trang

324



R. Adamczak and W. Bednorz



(iii) There exist positive constants C; x0 such that

F.sx/

F.x/

for all s



1, x



1 G.sx/



C



G.x/



x0 .



Lemma 22 For any Young function

x 0,



such that limx!1 .x/=x D 1 and any



/ 1 .x/



xÄ.



1



.x/ Ä 2x:



Moreover, the right hand side inequality holds for any strictly increasing continuous function W Œ0; 1/ ! Œ0; 1/, such that .0/ D 0, .1/ D 1,

limx!1 .x/=x D 1.

Lemma 23 Let ' and



be two Young functions. Assume that

lim '.x/=x D 1:



x!1



If '



1



ı



is equivalent to a Young function, then so is .



/



1



ı' .



Proof It is easy to see that under the assumptions of the lemma we also have

.x/ are finite for all x

0. Applying

limx!1 .x/=x D 1 and thus ' .x/,

1

Lemma 21 with F D ' 1 , G D

, we get that

'

for some C > 0, all s

enough,



1



.sx/

1 .sx/



C



1



'



1



.x/



1 .x/



1 and x large enough. By Lemma 22 we obtain for x large

. / 1 .sx/

.' / 1 .sx/



.4C/



/ 1 .x/

;

.' / 1 .x/



1.



which ends the proof by another application of Lemma 21.



t

u



Acknowledgements Research partially supported by MNiSW Grant N N201 608740 and the

Foundation for Polish Science.



References

1. R. Adamczak, A tail inequality for suprema of unbounded empirical processes with

applications to Markov chains. Electron. J. Probab. 13(34), 1000–1034 (2008)

2. R. Adamczak, W. Bednorz, Exponential concentration inequalities for additive functionals of

Markov chains. ESAIM: Probab. Stat. 19, 440–481 (2015)



Orlicz Integrability of Additive Functionals of Markov Chains



325



3. R. Adamczak, A.E. Litvak, A. Pajor, N. Tomczak-Jaegermann, Restricted isometry property

of matrices with independent columns and neighborly polytopes by random sampling. Constr.

Approx. 34(1), 61–88 (2011)

4. K.B. Athreya, P. Ney, A new approach to the limit theory of recurrent Markov chains. Trans.

Am. Math. Soc. 245, 493–501 (1978)

5. P.H. Baxendale, Renewal theory and computable convergence rates for geometrically ergodic

Markov chains. Ann. Appl. Probab. 15(1B), 700–738 (2005)

6. W. Bednorz, K. Łatuszy´nski, R. Latała, A regeneration proof of the central limit theorem for

uniformly ergodic Markov chains. Electron. Commun. Probab. 13, 8598 (2008)

7. P. Bertail, S. Clộmenỗon, Sharp bounds for the tails of functionals of Markov chains. Teor.

Veroyatn. Primen. 54(3), 609–619 (2009)

8. E. Bolthausen, The Berry-Esseen theorem for functionals of discrete Markov chains. Z.

Wahrsch. Verw. Gebiete 54(1), 59–73 (1980)

9. E. Bolthausen, The Berry-Esseén theorem for strongly mixing Harris recurrent Markov chains.

Z. Wahrsch. Verw. Gebiete 60(3), 283–289 (1982)

10. R.C. Bradley, On quantiles and the central limit question for strongly mixing sequences. J.

Theor. Probab. 10(2), 507–555 (1997)

11. R.C. Bradley, Jr., Information regularity and the central limit question. Rocky Mt. J. Math.

13(1), 77–97 (1983)

12. X. Chen, Limit theorems for functionals of ergodic Markov chains with general state space.

Mem. Am. Math. Soc. 139(664), xiv+203 (1999)

13. S.J.M. Clộmenỗon, Moment and probability inequalities for sums of bounded additive

functionals of regular Markov chains via the Nummelin splitting technique. Stat. Probab. Lett.

55(3), 227–238 (2001)

14. R. Douc, G. Fort, E. Moulines, P. Soulier, Practical drift conditions for subgeometric rates of

convergence. Ann. Appl. Probab. 14(3), 1353–1377 (2004)

15. R. Douc, A. Guillin, E. Moulines, Bounds on regeneration times and limit theorems for

subgeometric Markov chains. Ann. Inst. Henri Poincaré Probab. Stat. 44(2), 239–257 (2008)

16. P. Doukhan, P. Massart, E. Rio, The functional central limit theorem for strongly mixing

processes. Ann. Inst. Henri Poincaré Probab. Stat. 30(1), 63–82 (1994)

17. U. Einmahl, D. Li, Characterization of LIL behavior in Banach space. Trans. Am. Math. Soc.

360(12), 6677–6693 (2008)

18. O. Häggström, On the central limit theorem for geometrically ergodic Markov chains. Probab.

Theory Relat. Fields 132(1), 74–82 (2005)

19. P. Hitczenko, S.J. Montgomery-Smith, K. Oleszkiewicz, Moment inequalities for sums of

certain independent symmetric random variables. Stud. Math. 123(1), 15–42 (1997)

20. G.L. Jones, On the Markov chain central limit theorem. Probab. Surv. 1, 299–320 (2004)

21. I. Kontoyiannis, S.P. Meyn, Spectral theory and limit theorems for geometrically ergodic

Markov processes. Ann. Appl. Probab. 13(1), 304–362 (2003)

22. M.A. Krasnoselski˘ı, J.B. Ruticki˘ı, Convex Functions and Orlicz Spaces. Translated from the

first Russian edition by Leo F. Boron (P. Noordhoff, Groningen, 1961)

23. K. Łatuszy´nski, B. Miasojedow, W. Niemiro, Nonasymptotic bounds on the mean square error

for MCMC estimates via renewal techniques, in Monte Carlo and Quasi-Monte Carlo Methods

2010. Springer Proceedings in Mathematics and Statistics, vol. 23 (Springer, Heidelberg,

2012), pp. 539–555

24. K. Łatuszy´nski, B. Miasojedow, W. Niemiro, Nonasymptotic bounds on the estimation error

of MCMC algorithms. Bernoulli 19(5A), 2033–2066 (2014)

25. L. Maligranda, Orlicz Spaces and Interpolation. Seminários de Matemática [Seminars in

Mathematics], vol. 5 (Universidade Estadual de Campinas, Campinas, 1989)

26. L. Maligranda, E. Nakai, Pointwise multipliers of Orlicz spaces. Arch. Math. 95(3), 251–256

(2010)

27. F. Merlevède, M. Peligrad, E. Rio, A Bernstein type inequality and moderate deviations for

weakly dependent sequences. Probab. Theory Relat. Fields 151(3–4), 435–474 (2011)



326



R. Adamczak and W. Bednorz



28. S. Meyn, R.L. Tweedie, Markov Chains and Stochastic Stability, 2nd edn. (Cambridge

University Press, Cambridge, 2009)

29. S.J. Montgomery-Smith, Comparison of Orlicz-Lorentz spaces. Stud. Math. 103(2), 161–189

(1992)

30. E. Nummelin, A splitting technique for Harris recurrent Markov chains. Z. Wahrsch. Verw.

Gebiete 43(4), 309–318 (1978)

31. E. Nummelin, General Irreducible Markov Chains and Nonnegative Operators. Cambridge

Tracts in Mathematics, vol. 83 (Cambridge University Press, Cambridge, 1984)

32. E. Nummelin, P. Tuominen, Geometric ergodicity of Harris recurrent Markov chains with

applications to renewal theory. Stoch. Process. Appl. 12(2), 187–202 (1982)

33. E. Nummelin, P. Tuominen, The rate of convergence in Orey’s theorem for Harris recurrent

Markov chains with applications to renewal theory. Stoch. Process. Appl. 15(3), 295–311

(1983)

34. R. O’Neil, Fractional integration in Orlicz spaces. I. Trans. Am. Math. Soc., 115, 300–328

(1965)

35. J.W. Pitman, An identity for stopping times of a Markov process, in Studies in Probability

and Statistics (Papers in Honour of Edwin J. G. Pitman) (North-Holland, Amsterdam, 1976),

pp. 41–57

36. J.W. Pitman, Occupation measures for Markov chains. Adv. Appl. Probab. 9(1), 69–86 (1977)

37. M.M. Rao, Z.D. Ren, Theory of Orlicz Spaces. Monographs and Textbooks in Pure and Applied

Mathematics, vol. 146 (Marcel Dekker, New York, 1991)

38. E. Rio, The functional law of the iterated logarithm for stationary strongly mixing sequences.

Ann. Probab. 23(3), 1188–1203 (1995)

39. E. Rio, Théorie asymptotique des processus aléatoires faiblement dépendants. Mathématiques

& Applications, vol. 31 (Springer, Berlin, 2000)

40. G.O. Roberts, J.S. Rosenthal, General state space Markov chains and MCMC algorithms.

Probab. Surv. 1, 20–71 (2004)

41. S.M. Srivastava, A Course on Borel Sets. Graduate Texts in Mathematics, vol. 180 (Springer,

New York, 1998)



Bounds for Stochastic Processes on Product

Index Spaces

Witold Bednorz



Abstract In this paper we discuss the question of how to bound the supremum of

a stochastic process with an index set of a product type. It is tempting to approach

the question by analyzing the process on each of the marginal index sets separately.

However it turns out that it is necessary to also study suitable partitions of the entire

index set. We show what can be done in this direction and how to use the method to

reprove some known results. In particular we point out that all known applications of

the Bernoulli Theorem can be obtained in this way. Moreover we use the shattering

dimension to slightly extend the application to VC classes. We also show some

application to the regularity of paths of processes which take values in vector spaces.

Finally we give a short proof of the Mendelson–Paouris result on sums of squares

for empirical processes.

Keywords Shattering dimension • Stochastic inequalities • VC classes



Mathematics Subject Classification (2010). Primary 60G15; Secondary 60G17



1 Introduction

In this paper I denotes a countable set and .F; k k/ a separable Banach space.

Consider the class A of subsets of I. We say that the class A satisfies the maximal

inequality if for any symmetric independent random variables Xi , i 2 I taking values



Research partially supported by MNiSW Grant N N201 608740 and MNiSW program Mobility

Plus.

W. Bednorz ( )

Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

e-mail: wbednorz@mimuw.edu.pl

© Springer International Publishing Switzerland 2016

C. Houdré et al. (eds.), High Dimensional Probability VII,

Progress in Probability 71, DOI 10.1007/978-3-319-40519-3_14



327



328



W. Bednorz



in F the following inequality holds

E sup

A2A



X



Xi 6 KE



i2A



X



Xi ;



(1.1)



i2I



where K depends on A only. We point out that in this paper K will be used to denote

constants that appear in the formulation of our results and may depend on them.

We use c; C; L; M to denote absolute constants, which may change their values from

line to line by numerical factors. Also we write to express that two quantities are

comparable up to a universal constant. This will help us to reduce the notation in

this paper. It is an easy observation to see that (1.1) is equivalent to

E sup

A2A



X



vi "i 6 KE



i2A



X



vi "i ;



(1.2)



i2I



where .vi /i2I , consists of vectors in F and ."i /i2I is a Bernoulli sequence, i.e. a

sequence of independent r.v.’s such that P."i D ˙1/ D 12 .

To understand what is the proper characterization of such classes A we recall

here the notion of VC dimension. We say that A has VC dimension d if there exists

a set B

I, jBj D d such that jfB \ A W A 2 Agj D 2d but for all B

I,

jBj > d, jfB \ A W A 2 Agj < 2dC1 . It means that A shatters some set B of

cardinality d, but does not shatter any set of cardinality d C 1. The result which

has been proved in [1] as a corollary of the Bernoulli Theorem states that finite

VC dimension is the necessary and sufficient condition for the class A to have the

property (1.1). Since our paper refers often to the Bernoulli Theorem we recall its

formulation. We begin by mentioning Talagrand’s result for Gaussian’s

P processes. In

order to find two-sided bounds for supremum of the process G.t/ D i2I ti gi , where

t 2 T

`2 .I/ and .gi /i2I is a Gaussian sequence, i.e. a sequence of independent

standard Gaussian r.v.’s we need Talagrand’s 2 .T/ numbers, cf. Definition 2.2.19

in [15] or (3.1) below. By the well known Theorem 2.4.1 in [15] we have

2 .T/:



E sup G.t/

t2T



(1.3)



The Bernoulli Theorem, i.e. Theorem 1.1 in [1], concerns a similar question for

processes of random signs.

Theorem 1.1 Suppose that T

E sup



X



t2T i2I



`2 .I/. Then

!



ti "i



inf



T T1 CT2



sup ktk1 C

t2T1



2 .T2 /



:



where the infimum is taken over all decompositions

T1 C T2 D ft1 C t2 W t1 2

P

T1 ; t2 2 T2 g that contain the set T and ktk1 D i2I jti j.



Bounds for Stochastic Processes on Product Index Spaces



329



Note that if 0 2 T then we can also require that 0 2 T2 in the above result. The

consequence of Theorem 1.1 to our problem with maximal inequalities is as follows.

Theorem 1.2 The class A satisfies (1.1) with a finite constant K if and only if A is

a VC class of a finite dimension. Moreover the square root of the dimension is up to

a universal constant comparable with the optimal value of K.

Observe that part of the result is obvious. Namely one can easily show that

if A satisfies the maximal inequality then it is necessarily a VC class of a finite

dimension. Indeed let ."i /i2I be a Bernoulli sequence. Suppose that set B

I is

shattered. Let xi D 1 for i 2 B and xi D 0, i 62 B. Obviously

ˇ

ˇ

ˇ

ˇ

ˇX ˇ p

ˇ

ˇX

ˇ

ˇ

ˇ

ˇ



xi " i ˇ D E ˇ

"i ˇ 6 jBj

ˇ

ˇ

ˇ

ˇ

i2I



i2B



and on the other hand

ˇ

ˇ

ˇ

ˇ

ˇX

ˇ

ˇX

ˇ

X

ˇ

ˇ

ˇ

ˇ

E sup ˇ

xi "i ˇ D E sup ˇ

xi " i ˇ > E

"i 1"i D1 D jBj=2:

ˇ

ˇ

A2A ˇ

A2A ˇ

i2A



i2A\B



i2B



p

Consequently if (1.1) holds then K > jBj=2. Therefore (1.1) implies that the

cardinality of B must be smaller or equal 4K 2 .

Much more difficult is to prove the converse statement, i.e. that for

p each VC

class A of dimension d inequality (1.2) holds with K comparable with d. In order

to prove this result one has to first replace the basic formulation of the maximal

inequality—(1.2) by its equivalent version

ˇ

ˇ

ˇ

ˇ

ˇX ˇ

ˇX ˇ

ˇ

ˇ

ˇ

ˇ

E sup sup ˇ

ti "i ˇ 6 KE sup ˇ

ti "i ˇ ;

ˇ

ˇ

ˇ

ˇ

A2A t2T

t2T

i2A



(1.4)



i2I



`2 .I/. Note that we use

where ."i /i2I is a Bernoulli sequence and 0 2 T

absolute values since part of our work concerns complex

spaces. However it is

P

important P

to mention that in the real case E supt2T i2I ti "i is comparable with

E supt2T j i2I ti "i j if 0 2 T and therefore we often require in this paper that

0 2 T `2 .I/. Let us denote

ˇ

ˇ

ˇ

ˇ

ˇX ˇ

ˇX ˇ

ˇ

ˇ

ˇ

ˇ

b.T/ D E sup ˇ

ti "i ˇ ; g.T/ D E sup ˇ

ti gi ˇ ;

ˇ

ˇ

ˇ

ˇ

t2T

t2T

i2I



i2I



where ."i /i2I , .gi /i2I are respectively Bernoulli and Gaussian sequence. We recall

that, what was known for a long time [5, 7], (1.4) holds when Bernoulli random



330



W. Bednorz



variables are replaced by Gaussians, i.e.

ˇ

ˇ

ˇ

ˇ

ˇX ˇ

ˇX ˇ

p

p

ˇ

ˇ

ˇ

ˇ

E sup sup ˇ

gi ti ˇ 6 C dE sup ˇ

ti gi ˇ D C dg.T/;

ˇ

ˇ

t2T ˇ

A2A t2T ˇ

i2A



(1.5)



i2I



for any 0 2 T

`2 .I/. Due to Theorem 1.1 one can cover the set T by T1 C T2 ,

where 0 2 T2 and

(

)

max sup ktk1 ; g.T2 / 6 Lb.T/:



(1.6)



t2T1



Therefore using (1.5) and (1.6)

ˇ

ˇ

ˇX ˇ

ˇ

ˇ

"i ti ˇ

E sup sup ˇ

ˇ

ˇ

A2A t2T

i2A



6 sup ktk1 C E sup sup j

t2T1



A2A t2T2



r



6 sup ktk1 C

t2T1



t2T1



2

2



E sup sup j



r



6 sup ktk1 C



2



t2T1



"i ti j



i2A



E sup sup j



r



6 sup ktk1 C



X



A2A t2T2



A2A t2T2



X



ti "i Ejgi jj



i2A



X



ti gi j



i2A



p

g.T2 / 6 CL db.T/:



This proves Theorem 1.2.

Here is another example in which a similar approach works. Let G be a compact

Abelian group and .vi /i2I a sequence of vectors taking values in F. Let i , i 2 I be

characters on G. A deep result of Fernique [4] is

E sup

h2G



X

i2I



vi i .h/gi 6 C E



X

i2I



ˇ

ˇ!

ˇX

ˇ

ˇ

ˇ

vi gi C sup E sup ˇ

x .vi / i .h/gi ˇ :

ˇ

ˇ

kx k61 h2G

i2I



Bounds for Stochastic Processes on Product Index Spaces



331



This can be rewritten similarly as (1.5), i.e. for any 0 2 T

complex space in this case)



`2 .I/ (which is a



ˇ

ˇ

ˇ

ˇ!

ˇX

ˇ

ˇX

ˇ

ˇ

ˇ

ˇ

ˇ

E sup sup ˇ

ti i .h/gi ˇ 6 C g.T/ C sup E sup ˇ

ti i .h/gi ˇ :

ˇ

ˇ

h2G t2T ˇ

t2T

h2G ˇ

i2I



(1.7)



i2I



Once again the Bernoulli Theorem permits us to prove a similar result for Bernoulli

sequences. Namely by Theorem 1.1 we get the decomposition T T1 C T2 , 0 2 T2

such that

)

(

max sup ktk1 ; g.T2 / 6 Lb.T/:



(1.8)



t2T1



Consequently using (1.7), (1.8) and j i .h/j 6 1 we get

ˇ

ˇ

ˇX

ˇ

ˇ

ˇ

E sup sup ˇ

ti i .h/"i ˇ

ˇ

ˇ

h2G t2T i2I

ˇ

ˇ

ˇ

ˇX

ˇ

ˇ

6 sup ktk1 C E sup sup ˇ

"i ti i .h/ˇ

ˇ

ˇ

t2T1

h2G t2T2 i2A

ˇ

ˇ

r

ˇX

ˇ

ˇ

ˇ

6 sup ktk1 C

E sup sup ˇ

ti i .h/gi ˇ

ˇ

2 h2G t2T2 ˇ i2I

t2T1

ˇ

ˇ!

ˇX

ˇ

ˇ

ˇ

6 sup ktk1 C C g.T2 / C sup E sup ˇ

ti i .h/gi ˇ

ˇ

ˇ

t2T1

t2T2

h2G i2I

!

X

ti i .h/gi j :

6 CL b.T/ C sup E sup j

t2T2



h2G



(1.9)



(1.10)



i2I



The final step is the Marcus–Pisier estimate [12] (see Theorem 3.2.12 in [15])

ˇ

ˇ

ˇ

ˇ

ˇX

ˇ

ˇX

ˇ

ˇ

ˇ

ˇ

ˇ

sup E sup ˇ

ti i .h/gi ˇ 6 M sup E sup ˇ

ti i .h/"i ˇ :

ˇ

ˇ

t2T2

h2G ˇ

t2T2

h2G ˇ

i2I



(1.11)



i2I



Note that (1.11) is deeply based on the translational invariance of the distance

dt .g; h/ D



X

i2I



2



jti j j i .g/



i .h/j



2



! 12

g; h 2 G:



(1.12)



332



W. Bednorz



Since we may assume that T2



T



T1 we get



ˇ

ˇ

ˇX

ˇ

ˇ

ˇ

sup E sup ˇ

ti i .h/"i ˇ

ˇ

t2T2

h2G ˇ i2I

ˇ

ˇ

ˇ

ˇ

ˇX

ˇ

ˇX

ˇ

ˇ

ˇ

ˇ

ˇ

ti i .h/"i ˇ C sup E sup ˇ

ti i .h/"i ˇ

6 sup E sup ˇ

ˇ t2T1 h2G ˇ

ˇ

t2T

h2G ˇ i2I

i2I

ˇ

ˇ

ˇX

ˇ

ˇ

ˇ

6 sup E sup ˇ

ti i .h/"i ˇ C Lb.T/:

ˇ

ˇ

t2T

h2G



(1.13)



i2I



Combining (1.9) with (1.11) and (1.13) we get the following result.

Theorem 1.3 Suppose that 0 2 T

`2 .I/. For any compact group G and a

collection of vectors vi 2 F in a complex Banach space .F; k k/ and characters i

on G the following holds

ˇ

ˇ

ˇ

ˇ!

ˇX

ˇ

ˇX

ˇ

ˇ

ˇ

ˇ

ˇ

ti i .h/"i ˇ 6 K b.T/ C sup E sup ˇ

ti i .h/"i ˇ :

E sup sup ˇ

ˇ

ˇ

ˇ

ˇ

h2G t2T

t2T

h2G

i2I



i2I



The aim of this note is to explore the questions described above in a unified

language. We consider random processes X.u; t/, .u; t/ 2 U T with values in

R or C, which means that we study stochastic processes defined on product index

sets. In particular we cover all canonical processes in this way. Indeed, suppose that

U RI or CI and T RI or CI are such that for any u 2 U and t 2 T we have that

P

2

i2I jui ti j < 1. Then for any family of independent random variables Xi such that

EXi D 0, EjXi j2 D 1,

X.u; t/ D



X



ui ti Xi ; u 2 U; t 2 T



i2I



is a well defined process. As we have already mentioned, our main class of examples

includes Gaussian canonical processes, where Xi D gi , i 2 I are standard normal

variables and Bernoulli canonical processes, where Xi D

P"i , i 2 I are random signs.

In particular, our goal is to findP

bounds for E supu2U k i2I ui vi "i k, where vi 2 F,

i 2 I, formulated in terms of Ek i2I vi "i k. One of our results is an application of the

shattering dimension introduced by Mendelson and Vershynin [14], which enables

us to generalize Theorem 1.2. In this way we deduce that under mild conditions on

U RI we have

E sup

u2U



X

i2I



ui vi Xi 6 KE



X

i2I



vi Xi



Bounds for Stochastic Processes on Product Index Spaces



333



for any independent symmetric r.v.’s Xi , i 2 I. We show how to apply the result to

the analysis of convex bodies and their volume in high dimensional spaces. On the

other hand we can use our approach to study processes X.t/ D .Xi .t//i2I , t 2 Œ0; 1,

which take values in RI or CI . For example to check whether paths t ! X.t/ belong

to `2 we should consider

X

X.u; t/ D

ui Xi .t/; u D .ui /i2I 2 U; t 2 Œ0; 1;

i2I



P

2

where U is the unit ball in `2 .I/, i.e. U D fu 2 RI W

i2I jui j 6 1g. The finiteness

of kX.t/k2 < 1 is equivalent to the finiteness of supu2U jX.u; t/j. Similarly we can

treat a well known question in the theory of empirical processes. Suppose that .E; B/

is a measurable space and F a countable family of measurable real functions on E.

Let X1 ; X2 ; : : : ; XN be independent random variables, which take values in .E; B/,

we may define

X.u; f / D



N

X



ui f .Xi /; u D .ui /NiD1 2 U; f 2 F ;



iD1



where U D BN .0; 1/ D fu 2 RN W



PN



sup jX.u; f /j2 D

u2U



iD1

N

X



jui j2 6 1g. Then it is clear that



jf .Xi /j2 ; for all f 2 F :



iD1



In the last section we give a short proof of Mendelson–Paouris result [13] that

provides an upper bound for E supu2U supf 2F jX.u; t/j.



2 Upper Bounds

For the sake of exposition we shall give an idea how to bound stochastic processes.

The approach we present slightly extends results of Latala [9, 10] and Mendelson–

Paouris [13]. Suppose that EjX.t/ X.s/j < 1 for all s; t 2 T. For each s; t 2 T

and n > 0 we define qN n .s; t/ as the smallest q > 0 such that

Fq;n .s; t/ D Eq 1 .jX.t/ X.s/j q/C

Z 1

D

P.jX.t/ X.s/j > qt/dt 6 Nn 1 :



(2.1)



1



We shall prove the following observation.

‘Note that the theory we describe below can be extended to the case of arbitrary

increasing numbers Nn .’ but for our purposes it is better to work in the type

n

exponential case where Nn D 22 for n > 0 and N0 D 1.



334



W. Bednorz



Lemma 2.1 Function qN n .s; t/, s; t 2 T is a distance on T, namely is symmetric,

satisfies the triangle inequality and qN n .s; t/ D 0 if and only if X.s/ D X.t/ a.s.

Proof Obviously qN n .s; t/ is finite and symmetric qN n .s; t/ D qN n .t; s/. To see that it

equals 0 if and only if P.jX.t/ X.s/j > 0/ > 0 note that if X.s/ Ô X.t/ then

EjX.t/ X.s/j > 0. The function q ! Fq;n .s; t/ is decreasing continuous and a.s.

Fq;n .s; t/ ! 1 if q ! 0 and EjX.t/ X.s/j > 0. Moreover Fq;n .s; t/ is strictly

decreasing on the interval fq > 0 W Fq;n .s; t/ > 0g and consequently qN .s; t/ is the

unique solution of the equation Fq;n .s; t/ D Nn 1 , namely

E.Nqn .s; t// 1 .jX.t/



X.s/j



qN n .s; t//C D Nn 1 :



Finally we show that qN satisfies the triangle inequality. Indeed for any u; v; w 2 T

either qN .u; v/ D 0 or qN .v; w/ D 0 or qN .u; w/ D 0 and the inequality is trivial or all

the quantities are positive and then

FqN n .u;v/;n .u; v/ D FqN n .v;w/;n .v; w/ D FqN n .u;w/;n .u; w/ D Nn 1 :

It suffices to observe

1

E .jX.u/ X.w/j qN n .u; v/

qN n .u; v/ C qN n .v; w/

Ã

Â

jX.u/ X.w/j C jX.w/ X.v/j

1

6E

:

qN n .u; v/ C qN n .w; v/

C

The function x ! .x



qN n .w; v//C



1/C is convex which implies that



.px C qy



1/C 6 p.x



1/C C q.y



1/C



for p; q > 0, p C q D 1 and x; y > 0. We use (2.2) for

xD



jX.v/ X.w/j

jX.u/ X.v/j

; yD

qN n .u; v/

qN n .v; w/



and

pD



qN n .v; w/

qN n .u; v/

; qD

:

qN n .u; v/ C qN n .w; v/

qN n .u; v/ C qN n .w; v/



Therefore

1

E .jX.u/ X.w/j qN n .u; v/ qN n .w; v//C

qN n .u; v/ C qN n .v; w/

Ã

Ã

Â

Â

jX.u/ X.v/j

jX.v/ X.w/j

1

1

6 pE

C qE

qN n .u; v/

qN n .v; w/

C

C

6 pNn 1 C qNn 1 D Nn 1



(2.2)



Tài liệu bạn tìm kiếm đã sẵn sàng tải về

Appendix. Some Generalities on Orlicz Young Functions and Orlicz Spaces

Tải bản đầy đủ ngay(0 tr)

×