ARemarkonthe1=H-Variation of the Fractional Brownian Motion
|
|
- Adele Gilbert
- 7 years ago
- Views:
Transcription
1 ARemarothe1=H-Variatio of the Fractioal Browia Motio Maurizio Pratelli Abstract We give a elemetary proof of the followig property of H-fractioal Browia motio: almost all sample paths have ifiite 1/H-variatio o every iterval. Keywords Fractioal Browia motio p-variatio Ergodictheorem 1 Itroductio ad Statemet of the Result Let.B t / t0 be the Fractioal Browia Motio with Hurst (or self-similarity) parameter H; 0 < H < 1 (we refer for istace to [2] or[5] or[8] p. 273 for the defiitios): fix t>0ad let t D t for iteger ad D 0;:::;: It is well ow (see e.g. [5]or[9]) that lim!1 D0 X 1 p Bt C1 B t L 1. / D However, if we defie the radom variable 8 ˆ< C1 p<1=h te B11=H p D 1=H : ˆ: 0 p > 1=H V.!/ D V 1=H Œ0;t.!/ D ;0t 1 <t 2 <:::<t t X 1 BtiC1.!/ B.!/1=H ti id1 the V.!/ D C 1 a.s. (ote that V is measurable sice the paths of the fractioal Browia motio are cotiuous ad therefore the ca be tae over ratioals t i ). M. Pratelli ( ) Dipartimeto di Matematica, Largo B. Potecorvo 5, Pisa, Italy pratelli@dm.uipi.it C. Doati-Marti et al. (eds.), Sémiaire de Probabilités XLIII, Lecture Notes i Mathematics 2006, DOI / , c Spriger-Verlag Berli Heidelberg
2 216 M. Pratelli This property is well ow i the case of stadard Browia Motio (i.e. H D 1=2): it was stated (but without a rigorous proof) by P. Lévy i [6] p. 190, ad also the excellet boo Revuz-Yor quotes the result without a proof (see [8] p. 28). A setch of the proof (always i the case H D 1=2) was give by D. Freedma i [4] p. 48. For the stadard Browia Motio, there is a more precise (ad more techical) result due to Taylor (see [11]): give a icreasig fuctio W Œ0; C1/! Œ0; C1/, we ca defie the variatio of the fuctio f o the iterval Œa; b as ;adt 1 <t 2 <:::<t Db jf.tic1 / f.t i /j (whe.t/ D t p it is called the p variatio). Taylor showed that the correct fuctio for the variatio of the paths of the BM is the fuctio 1.s/ D s 2 =2 log log s (where log s D max.1 ; j log sj/) ithe sese that V ;Œa;b.!/ D ;adt 1 <t 2 <:::<t Db jbtic1.!/ B ti.!/j is a fiite r.v. but is ifiite if 1 is replaced by ay fuctio such that.s/= 1.s/!C1as s! 0C. The impact of the p-variatio of the paths for (stochastic) itegratio is well highlighted by L. Couti (see [2]) for the case of FBM ad by Dudley ad Norvaisa (see [3]) for more geeral stochastic processes. I the geeral case of the Fractioal Browia Motio, it is well ow that p D 1=H is a limit case, ad that sample paths are -hölder cotiuous for ay <H. The result of Theorem 1 is ow sice it is a cosequece of Theorem IV.5.1 of [1]: their proof, however, is based o a complex techology (the theory of Besov spaces). The aim of this short ote is to give a elemetary complete proof suggested by the argumet preseted i [4]; I wat to tha Sara Biagii ad Giorgio Letta for a discussio o the subject. Let us fix H with 0<H <1,let.B t / t0 be a FBM with parameter H ad cotiuous sample paths: defie V Œa;b.!/ D The statemet of the result is as follows BtiC1.!/ B.!/1=H ti ;adt 1 <t 2 <:::<t Db Theorem 1. There exists a ull-set N such that, if! N, the for every a<b, V Œa;b.!/ DC1.
3 A Remar o the 1=H -Variatio of the Fractioal Browia Motio The Proof I the sequel, is the Lebesgue measure o IR C ad p is a shorthad for 1=H. Let U be a fiite uio of disjoit ope itervals s i ;t i Œ of IR C with s i ;t i 2 Q ad let U be the collectio of such subsets of IR C. For U D [ id1 s i ;t i Œ ( 0 s 1 < t 1 s 2 < ::: < t ), let q U.!/ D P id1 jb t i.!/ B si.!/j p ; ote that V Œa;b.!/ D q U.!/ U 2U ;UŒa;b Lemma 1. Fix m > 0 ad let p m D P.jB 1 j p m/. Let Z D I fjb B 1 j p mg :them D Z 1CCZ coverges a.s. to EŒZ 1 D p m. Proof. The sequece of oe step icremets of B, X D B B 1, is statioary, cetered Gaussia ad with covariace fuctio R./ D EŒX 1 X C1 which teds to 0 whe goes to ifiity (see e.g. [7] p. 274): therefore.x/ 1 is ergodic (see [10] p. 413). Now Z ca be writte i the form Z D g.x / with a borel fuctio g ad therefore also.z / 1 is ergodic. As a cosequece of the ergodic theorem (Theorem 3.3 p. 413 of [10]), the sequece of the empirical meas M D.M / 1 coverges a.s. (ad i L 1 )toeœz 1. The ey of the proof is the followig result: Lemma 2. Let I D s; tœ be a ope iterval with s; t 2 Q C ad fix m>0:let p m D PfjB 1 j p mg ad r<p m. The there exists a measurable A m with P.A m / D 1 such that for all! 2 A m there exists U! 2 U with the properties: 1. U! I 2..U! />r.i/ 3. q U!.!/ m.u! / Proof. For >1ad i D 0;:::;let t i S D X id1 D s C i.t s/ ad J i D ti 1 ;t i Œ.Set I jb t B t ic1 i jp m.t s/ S.!/ couts the umber of subitervals Ji o which q J i.!/ > m.ji /. Thas to P the self-similarity property of fbm (see e.g. [7] p. 275), S is distributed as Z D id1 I fjb ic1 B i j p mg. By the Lemma 1, Z Modulo a subsequece,! p m almost surely, whece S lim S D p m a.s. teds to p m i probability. Call A m the set o which the above sequece coverges: if! 2 A m the there exists! such that S.!/ > r for!.
4 218 M. Pratelli Select! ad amog the subitervals Ji ad let U! be their uio. The m.t s/ exactly those such that q J i.!/.u! / D S.!/ t s >r.i/ ad.t s/ q U!.!/ S.!/ m D m.u! /: The same result holds evidetly for a elemet U 2 U.SiceU i coutable, we have immediately the followig result Corollary 1. Fix m>0ad set r D p m =2 : there exists a measurable set C m with P.C m / D 1 such that, if! 2 C m ad V 2 U, there exists U! 2 U ad U! V such that.u! />r.v/ad q U!.!/ m.u! /. Lemma 3. Fix 0 a<bi a; b 2 Q :thev Œa;b.!/ DC1a.s. Proof. Choose m>0adapplylemma2 to I D a; bœ : the for every! 2 C m there exists U! 1 I such that q U! 1.!/ m.u1! / ad.u! 1 / r.i/. Now iterate the procedure, that is apply Corollary 1 to I U 1 1! U! is the closure of U! 1 : thus there exists U 2! I U 1! with qu 2.!/ m.u2!! / ad.u! I 2 />r U 1!. At the. C1/-th step, we have a subset U! C1 of I.U 1! [ :::[ U! / such that.u! C1 />r I U 1! [ :::[ U! ad qu C1.!/ m.u C1!! /. Call V! D U! 1 [ :::[ U!,theV! 2 U ad q V!.!/ m.v! / Moreover, by iductio.i V! /.1 r/.b a/ ad therefore q V!.!/ m lim.v! / D m.b a/ Now, the itersectio C D T m2in C m has probability oe ad ay! 2 C satisfies V Œa;b.!/ m.b a/ 8m 2 IN whece the thesis. If N Œa;b is the ull-set! 2 VŒa;b.!/ < C1, the the coutable uio of all N Œa;b with a<bi a; b 2 Q, satisfies the hypothesis of Theorem 1.
5 A Remar o the 1=H -Variatio of the Fractioal Browia Motio 219 Refereces 1. Ciesielsi, Z., Keryacharia, G., Royette, B.: Quelques espaces foctioelles associés a des processus Gaussies. Studia Math. 107(2), (1993) 2. Couti, L.: A itroductio to (stochastic) calculus with respect to fractioal Browia motio. I: Sémiaire de ProbabilitésXL, Lecture NotesiMathematics, vol. 1899, pp Spriger (2007) 3. Dudley, N.R.: A itroductio to P -variatio ad Youg itegrals. Tech. rep. 1., Maphysto, Ceter for Mathematical Physics ad Stochastics, Uiversity of Aarhus (1998) 4. Freedma, D.: Browia Motio ad Diffusio. Holde-Day (1971) 5. Guerra, J., Nualart, D.: The 1=H -variatio of the divergece itegral with respect to the fractioal Browia motio for H>1=2ad fractioal Bessel processes. Stoch. Process. Appl. 115(1), (2005) 6. Lévy, P.: Processus stochastiques et Mouvemet Browie. Gauthier-Villars (1965) 7. Nualart, D.: The Malliavi Calculus ad Related Topics. Spriger, New Yor (2006) 8. Revuz, D., Yor, M.: Cotiuous Martigales ad Browia Motio, 3rd ed. Spriger, Berli (1999) 9. Rogers, L.C.G.: Arbitrage with fractioal Browia motio. Math. Fiace 7, (1997) 10. Shiryaev, A.N.: Probability, 2d ed. Spriger, Berli (1996) 11. Taylor, S.J.: Exact asymptotic estimates of Browia path variatio. Due Math. J. 39, (1972)
SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx
SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval
More informationOverview of some probability distributions.
Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability
More informationTheorems About Power Series
Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius
More informationProperties of MLE: consistency, asymptotic normality. Fisher information.
Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout
More informationAsymptotic Growth of Functions
CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll
More informationIn nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008
I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces
More informationTHE ABRACADABRA PROBLEM
THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected
More informationConvexity, Inequalities, and Norms
Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for
More informationInfinite Sequences and Series
CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...
More informationActa Acad. Paed. Agriensis, Sectio Mathematicae 29 (2002) 77 87. ALMOST SURE FUNCTIONAL LIMIT THEOREMS IN L p( ]0, 1[ ), WHERE 1 p <
Acta Acad. Paed. Agriesis, Sectio Mathematicae 29 22) 77 87 ALMOST SUR FUNCTIONAL LIMIT THORMS IN L ], [ ), WHR < József Túri Nyíregyháza, Hugary) Dedicated to the memory of Professor Péter Kiss Abstract.
More informationSequences and Series
CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their
More informationLecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem
Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits
More informationSection 11.3: The Integral Test
Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult
More informationMARTINGALES AND A BASIC APPLICATION
MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this
More informationON THE DENSE TRAJECTORY OF LASOTA EQUATION
UNIVERSITATIS IAGELLONICAE ACTA MATHEMATICA, FASCICULUS XLIII 2005 ON THE DENSE TRAJECTORY OF LASOTA EQUATION by Atoi Leo Dawidowicz ad Najemedi Haribash Abstract. I preseted paper the dese trajectory
More informationOur aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series
8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series
More informationChapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:
Chapter 7 - Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries
More informationFactors of sums of powers of binomial coefficients
ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the
More informationLecture 5: Span, linear independence, bases, and dimension
Lecture 5: Spa, liear idepedece, bases, ad dimesio Travis Schedler Thurs, Sep 23, 2010 (versio: 9/21 9:55 PM) 1 Motivatio Motivatio To uderstad what it meas that R has dimesio oe, R 2 dimesio 2, etc.;
More information4.3. The Integral and Comparison Tests
4.3. THE INTEGRAL AND COMPARISON TESTS 9 4.3. The Itegral ad Compariso Tests 4.3.. The Itegral Test. Suppose f is a cotiuous, positive, decreasig fuctio o [, ), ad let a = f(). The the covergece or divergece
More informationOn the L p -conjecture for locally compact groups
Arch. Math. 89 (2007), 237 242 c 2007 Birkhäuser Verlag Basel/Switzerlad 0003/889X/030237-6, ublished olie 2007-08-0 DOI 0.007/s0003-007-993-x Archiv der Mathematik O the L -cojecture for locally comact
More informationApproximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find
1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.
More informationMetric, Normed, and Topological Spaces
Chapter 13 Metric, Normed, ad Topological Spaces A metric space is a set X that has a otio of the distace d(x, y) betwee every pair of poits x, y X. A fudametal example is R with the absolute-value metric
More informationWHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?
WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This
More informationDepartment of Computer Science, University of Otago
Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly
More informationQuaderni di Dipartimento. Rate of Convergence of Predictive Distributions for Dependent Data. Patrizia Berti (Università di Modena e Reggio Emilia)
Quaderi di Dipartimeto Rate of Covergece of Predictive Distributios for Depedet Data Patrizia Berti Uiversità di Modea e Reggio Emilia Iree Crimaldi Uiversità di Bologa Luca Pratelli Accademia Navale di
More informationTrigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is
0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values
More information0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5
Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.
More informationA probabilistic proof of a binomial identity
A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two
More informationPART TWO. Measure, Integration, and Differentiation
PART TWO Measure, Itegratio, ad Differetiatio Émile Félix-Édouard-Justi Borel (1871 1956 Émile Borel was bor at Sait-Affrique, Frace, o Jauary 7, 1871, the third child of Hooré Borel, a Protestat miister,
More informationIrreducible polynomials with consecutive zero coefficients
Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem
More informationLecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)
18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the
More informationBuilding Blocks Problem Related to Harmonic Series
TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite
More informationLECTURE NOTES ON DONSKER S THEOREM
LECTURE NOTES ON DONSKER S THEOREM DAVAR KHOSHNEVISAN ABSTRACT. Some course otes o Dosker s theorem. These are for Math 7880-1 Topics i Probability, taught at the Deparmet of Mathematics at the Uiversity
More informationPROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM
PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics
More informationDiscrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13
EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may
More informationClass Meeting # 16: The Fourier Transform on R n
MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,
More informationThe following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles
The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio
More information3. Greatest Common Divisor - Least Common Multiple
3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd
More informationOn Formula to Compute Primes. and the n th Prime
Applied Mathematical cieces, Vol., 0, o., 35-35 O Formula to Compute Primes ad the th Prime Issam Kaddoura Lebaese Iteratioal Uiversity Faculty of Arts ad cieces, Lebao issam.kaddoura@liu.edu.lb amih Abdul-Nabi
More informationInverse Gaussian Distribution
5 Kauhisa Matsuda All rights reserved. Iverse Gaussia Distributio Abstract Kauhisa Matsuda Departmet of Ecoomics The Graduate Ceter The City Uiversity of New York 65 Fifth Aveue New York NY 6-49 Email:
More informationChapter 5: Inner Product Spaces
Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples
More informationChapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity
More informationDegree of Approximation of Continuous Functions by (E, q) (C, δ) Means
Ge. Math. Notes, Vol. 11, No. 2, August 2012, pp. 12-19 ISSN 2219-7184; Copyright ICSRS Publicatio, 2012 www.i-csrs.org Available free olie at http://www.gema.i Degree of Approximatio of Cotiuous Fuctios
More informationNOTES ON PROBABILITY Greg Lawler Last Updated: March 21, 2016
NOTES ON PROBBILITY Greg Lawler Last Updated: March 21, 2016 Overview This is a itroductio to the mathematical foudatios of probability theory. It is iteded as a supplemet or follow-up to a graduate course
More informationAP Calculus AB 2006 Scoring Guidelines Form B
AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success
More informationINFINITE SERIES KEITH CONRAD
INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal
More informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationChapter 14 Nonparametric Statistics
Chapter 14 Noparametric Statistics A.K.A. distributio-free statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they
More informationTHE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE
THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE JAVIER CILLERUELO Abstract. We obtai, for ay irreducible quadratic olyomial f(x = ax 2 + bx + c, the asymtotic estimate log l.c.m. {f(1,..., f(} log. Whe
More informationA note on weak convergence of the sequential multivariate empirical process under strong mixing SFB 823. Discussion Paper.
SFB 823 A ote o weak covergece of the sequetial multivariate empirical process uder strog mixig Discussio Paper Axel Bücher Nr. 17/2013 A ote o weak covergece of the sequetial multivariate empirical process
More informationChapter 6: Variance, the law of large numbers and the Monte-Carlo method
Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value
More informationTHE HEIGHT OF q-binary SEARCH TREES
THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average
More information1. MATHEMATICAL INDUCTION
1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1
More informationAnalysis Notes (only a draft, and the first one!)
Aalysis Notes (oly a draft, ad the first oe!) Ali Nesi Mathematics Departmet Istabul Bilgi Uiversity Kuştepe Şişli Istabul Turkey aesi@bilgi.edu.tr Jue 22, 2004 2 Cotets 1 Prelimiaries 9 1.1 Biary Operatio...........................
More informationHow To Solve The Homewor Problem Beautifully
Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log
More informationA Note on Sums of Greatest (Least) Prime Factors
It. J. Cotemp. Math. Scieces, Vol. 8, 203, o. 9, 423-432 HIKARI Ltd, www.m-hikari.com A Note o Sums of Greatest (Least Prime Factors Rafael Jakimczuk Divisio Matemática, Uiversidad Nacioal de Luá Bueos
More informationFIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix
FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if
More informationConfidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
More informationSoving Recurrence Relations
Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree
More informationLECTURE 13: Cross-validation
LECTURE 3: Cross-validatio Resampli methods Cross Validatio Bootstrap Bias ad variace estimatio with the Bootstrap Three-way data partitioi Itroductio to Patter Aalysis Ricardo Gutierrez-Osua Texas A&M
More informationAnnuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL.
Auities Uder Radom Rates of Iterest II By Abraham Zas Techio I.I.T. Haifa ISRAEL ad Haifa Uiversity Haifa ISRAEL Departmet of Mathematics, Techio - Israel Istitute of Techology, 3000, Haifa, Israel I memory
More informationA Recursive Formula for Moments of a Binomial Distribution
A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,
More informationWeek 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable
Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5
More informationPlug-in martingales for testing exchangeability on-line
Plug-i martigales for testig exchageability o-lie Valetia Fedorova, Alex Gammerma, Ilia Nouretdiov, ad Vladimir Vovk Computer Learig Research Cetre Royal Holloway, Uiversity of Lodo, UK {valetia,ilia,alex,vovk}@cs.rhul.ac.uk
More information3 Basic Definitions of Probability Theory
3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio
More information1. C. The formula for the confidence interval for a population mean is: x t, which was
s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value
More informationNon-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring
No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy
More informationMath 113 HW #11 Solutions
Math 3 HW # Solutios 5. 4. (a) Estimate the area uder the graph of f(x) = x from x = to x = 4 usig four approximatig rectagles ad right edpoits. Sketch the graph ad the rectagles. Is your estimate a uderestimate
More informationUC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006
Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam
More informationInteger Factorization Algorithms
Iteger Factorizatio Algorithms Coelly Bares Departmet of Physics, Orego State Uiversity December 7, 004 This documet has bee placed i the public domai. Cotets I. Itroductio 3 1. Termiology 3. Fudametal
More informationCS103X: Discrete Structures Homework 4 Solutions
CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least
More informationA sharp Trudinger-Moser type inequality for unbounded domains in R n
A sharp Trudiger-Moser type iequality for ubouded domais i R Yuxiag Li ad Berhard Ruf Abstract The Trudiger-Moser iequality states that for fuctios u H, 0 (Ω) (Ω R a bouded domai) with Ω u dx oe has Ω
More informationSampling Distribution And Central Limit Theorem
() Samplig Distributio & Cetral Limit Samplig Distributio Ad Cetral Limit Samplig distributio of the sample mea If we sample a umber of samples (say k samples where k is very large umber) each of size,
More informationExample 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).
BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly
More informationLecture 4: Cheeger s Inequality
Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular
More informationSequences and Series Using the TI-89 Calculator
RIT Calculator Site Sequeces ad Series Usig the TI-89 Calculator Norecursively Defied Sequeces A orecursively defied sequece is oe i which the formula for the terms of the sequece is give explicitly. For
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis
Ruig Time ( 3.) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationAP Calculus BC 2003 Scoring Guidelines Form B
AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet
More informationCase Study. Normal and t Distributions. Density Plot. Normal Distributions
Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca
More informationInstallment Joint Life Insurance Actuarial Models with the Stochastic Interest Rate
Iteratioal Coferece o Maagemet Sciece ad Maagemet Iovatio (MSMI 4) Istallmet Joit Life Isurace ctuarial Models with the Stochastic Iterest Rate Nia-Nia JI a,*, Yue LI, Dog-Hui WNG College of Sciece, Harbi
More informationA note on the boundary behavior for a modified Green function in the upper-half space
Zhag ad Pisarev Boudary Value Problems (015) 015:114 DOI 10.1186/s13661-015-0363-z RESEARCH Ope Access A ote o the boudary behavior for a modified Gree fuctio i the upper-half space Yulia Zhag1 ad Valery
More informationON AN INTEGRAL OPERATOR WHICH PRESERVE THE UNIVALENCE
Proceedigs of the Iteratioal Coferece o Theory ad Applicatios of Mathematics ad Iformatics ICTAMI 3, Alba Iulia ON AN INTEGRAL OPERATOR WHICH PRESERVE THE UNIVALENCE by Maria E Gageoea ad Silvia Moldoveau
More informationUniversal coding for classes of sources
Coexios module: m46228 Uiversal codig for classes of sources Dever Greee This work is produced by The Coexios Project ad licesed uder the Creative Commos Attributio Licese We have discussed several parametric
More informationPart - I. Mathematics
Part - I Mathematics CHAPTER Set Theory. Objectives. Itroductio. Set Cocept.. Sets ad Elemets. Subset.. Proper ad Improper Subsets.. Equality of Sets.. Trasitivity of Set Iclusio.4 Uiversal Set.5 Complemet
More informationNATIONAL SENIOR CERTIFICATE GRADE 12
NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS
More informationarxiv:0908.3095v1 [math.st] 21 Aug 2009
The Aals of Statistics 2009, Vol. 37, No. 5A, 2202 2244 DOI: 10.1214/08-AOS640 c Istitute of Mathematical Statistics, 2009 arxiv:0908.3095v1 [math.st] 21 Aug 2009 ESTIMATING THE DEGREE OF ACTIVITY OF JUMPS
More informationAn Efficient Polynomial Approximation of the Normal Distribution Function & Its Inverse Function
A Efficiet Polyomial Approximatio of the Normal Distributio Fuctio & Its Iverse Fuctio Wisto A. Richards, 1 Robi Atoie, * 1 Asho Sahai, ad 3 M. Raghuadh Acharya 1 Departmet of Mathematics & Computer Sciece;
More informationARTICLE IN PRESS. Statistics & Probability Letters ( ) A Kolmogorov-type test for monotonicity of regression. Cecile Durot
STAPRO 66 pp: - col.fig.: il ED: MG PROD. TYPE: COM PAGN: Usha.N -- SCAN: il Statistics & Probability Letters 2 2 2 2 Abstract A Kolmogorov-type test for mootoicity of regressio Cecile Durot Laboratoire
More information1 The Gaussian channel
ECE 77 Lecture 0 The Gaussia chael Objective: I this lecture we will lear about commuicatio over a chael of practical iterest, i which the trasmitted sigal is subjected to additive white Gaussia oise.
More information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More informationChapter 7 Methods of Finding Estimators
Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of
More informationI. Why is there a time value to money (TVM)?
Itroductio to the Time Value of Moey Lecture Outlie I. Why is there the cocept of time value? II. Sigle cash flows over multiple periods III. Groups of cash flows IV. Warigs o doig time value calculatios
More informationSwaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps
Swaps: Costat maturity swaps (CMS) ad costat maturity reasury (CM) swaps A Costat Maturity Swap (CMS) swap is a swap where oe of the legs pays (respectively receives) a swap rate of a fixed maturity, while
More informationSOME GEOMETRY IN HIGH-DIMENSIONAL SPACES
SOME GEOMETRY IN HIGH-DIMENSIONAL SPACES MATH 57A. Itroductio Our geometric ituitio is derived from three-dimesioal space. Three coordiates suffice. May objects of iterest i aalysis, however, require far
More informationBasic Elements of Arithmetic Sequences and Series
MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic
More informationESTIMATING THE DEGREE OF ACTIVITY OF JUMPS IN HIGH FREQUENCY DATA
The Aals of Statistics 2009, Vol. 37, No. 5A, 2202 2244 DOI: 10.1214/08-AOS640 Istitute of Mathematical Statistics, 2009 ESTIMATING THE DEGREE OF ACTIVITY OF JUMPS IN HIGH FREQUENCY DATA BY YACINE AÏT-SAHALIA
More informationEkkehart Schlicht: Economic Surplus and Derived Demand
Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/
More informationNormal Distribution.
Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued
More informationVirtile Reguli And Radiational Optaprints
RANDOM GRAPHS WITH FORBIDDEN VERTEX DEGREES GEOFFREY GRIMMETT AND SVANTE JANSON Abstract. We study the radom graph G,λ/ coditioed o the evet that all vertex degrees lie i some give subset S of the oegative
More information