A NECESSARY MOMENT CONDITION FOR THE FRACTIONAL FUNCTIONAL CENTRAL LIMIT THEOREM

Size: px
Start display at page:

Download "A NECESSARY MOMENT CONDITION FOR THE FRACTIONAL FUNCTIONAL CENTRAL LIMIT THEOREM"

Transcription

1 Ecoometric Theory, 28, 2012, doi: /s A NECESSARY MOMENT CONDITION FOR THE FRACTIONAL FUNCTIONAL CENTRAL LIMIT THEOREM SØREN JOHANSEN Uiversity of Copehage ad CREATES MORTEN ØRREGAARD NIELSEN Quee s Uiversity ad CREATES We discuss the momet coditio for the fractioal fuctioal cetral limit theorem (FCLT for partial sums of x t = d u t, where d ( 1 2, 1 2 is the fractioal itegratio parameter ad u t is weakly depedet. The classical coditio is existece of q 2 ad q > ( d momets of the iovatio sequece. Whe d is close to 1 2 this momet coditio is very strog. Our mai result is to show that whe d ( 1 2,0 ad uder some relatively weak coditios o u t, the existece of q ( d momets is i fact ecessary for the FCLT for fractioally itegrated processes ad that q > ( d momets are ecessary for more geeral fractioal processes. Davidso ad de Jog (2000, Ecoometric Theory 16, preseted a fractioal FCLT where oly q > 2 fiite momets are assumed. As a corollary to our mai theorem we show that their momet coditio is ot sufficiet ad hece that their result is icorrect. 1. INTRODUCTION The fractioal fuctioal cetral limit theorem (FCLT is give i Davydov (1970 for partial sums of the fractioally itegrated process d ε t, where d = (1 L d is the fractioal differece operator defied i (3 i our Sectio 2 ad ε t is idepedet ad idetically distributed (i.i.d. with mea zero. Davydov We are grateful to Beedikt Pötscher, three aoymous referees, ad James Davidso for commets ad to the Social Scieces ad Humaities Research Coucil of Caada (SSHRC grat ad the Ceter for Research i Ecoometric Aalysis of Time Series, (CREATES, fuded by the Daish Natioal Research Foudatio for fiacial support. Address correspodece to Morte Ørregaard Nielse, Departmet of Ecoomics, Duig Hall Room 307, Quee s Uiversity, 94 Uiversity Aveue, Kigsto, Otario K7L 3N6, Caada; mo@eco.queesu.ca. c Cambridge Uiversity Press

2 672 SØREN JOHANSEN AND MORTEN ØRREGAARD NIELSEN (1970 proved the fractioal FCLT uder a momet coditio of the form E ε t q < for q 4 ad q > 4d/ ( d + 1 2, which was subsequetly improved by Taqqu (1975 to q 2 ad q > q 0 = ( d The stadard momet coditio from Dosker s theorem is q 2 (see Billigsley, 1968, Ch. 2, Sect. 10, ad because the coditio q q 0 is oly stroger tha q 2 whe d < 0 we cosider oly d ( 1 2,0. The fractioal FCLT has bee exteded ad geeralized i umerous directios. For example, Mariucci ad Robiso (2000 replace ε t by a class of liear processes, assumig the momet coditio q > q 0. The latter authors proved FCLTs for so-called type II fractioal processes, whereas Davydov (1970 ad Taqqu (1975 discussed type I fractioal processes, but the distictio betwee type I ad type II processes is ot relevat for our discussio of the momet coditio. Davidso ad de Jog (2000, heceforth DDJ claim i their Theorem 3.1 that for some ear-epoch depedet (NED processes with uiformly bouded qth momet the fractioal FCLT holds, but (icorrectly, as we shall see subsequetly they assume a much weaker momet coditio tha previous results, amely, q > 2. To the best of our kowledge, Theorem 3.1 of DDJ is the oly fractioal FCLT that claims a momet coditio that is weaker tha the earlier coditio. I the ext sectio we give some defiitios ad costruct a i.i.d. sequece ad a fractioal liear process that are cetral to our results. I Sectio 3 we preset our mai results, which state that if the fractioal FCLT holds for ay class of processes U(q with uiformly bouded qth momet cotaiig these two processes, the it follows that q q 0 if the fractioal FCLT is based o fractioal coefficiets ad q > q 0 if the coefficiets are more geeral. The proofs of both results are based o couterexamples that are costructed i a similar way as a couterexample i Wu ad Shao (2006, Rmk I Sectio 4 we discuss the results ad give two applicatios. I particular, it follows from our results that if the FCLT holds for NED processes with uiformly bouded q momets, the q q 0. Hece DDJ s Theorem 3.1 ad all their subsequet results do ot hold uder the assumptios stated i their theorem. Throughout, c deotes a geeric fiite costat, which may take differet values i differet places. 2. DEFINITIONS DEFINITION 1. We defie the class U 0 (q as the class of processes u t that satisfy the momet coditio sup E u t q < for some q 2 (1 <t< ad have log-ru variace

3 NECESSARY MOMENT CONDITION FOR FRACTIONAL FCLT 673 σ 2 u = lim T T E ( T 2 u t, 0 <σu 2 <. (2 For such processes we defie two subclasses of U 0 (q as follows. (i U LIN (q U 0 (q is the class of liear processes u t U 0 (q satisfyig u t = =0 τ ε t, where =0 j= τ j 2 < ad ε t is i.i.d. with mea zero ad variace σε 2 > 0. (ii U NED (q U 0 (q is the class of zero mea covariace statioary processes u t U 0 (q that are L 2 -N E D of size 1 2 o v t with d t = 1, where v t is either a α-mixig sequece of size q/(1 q or a φ-mixig sequece of size q/(2(1 q; see Assumptio 1 of DDJ. Note that the coditio =0 j= τ j 2 < i the defiitio of the class U LIN (q either implies or is implied by (2. Also ote that if u t U LIN (q the u t is zero mea ad statioary. Next we defie the fractioal ad geeral fractioal processes. DEFINITION 2. For ay u t U 0 (q we we defie the fractioal process x t = d u t = j=0 b j (du t j for 1 < d < 0, (3 2 where b j (d = ( j( d j = d(d (d + j 1/j! cj d are the fractioal coefficiets, i.e., the coefficiets i the biomial expasio of (1 z d, ad meas that the ratio of the left- ad right-had sides coverges to oe. The geeral fractioal process is defied by x t = j=0 a j (du t j for 1 < d < 0, (4 2 where a j (d cl( j j d ad l( j is a (ormalized slowly varyig fuctio; see Bigham, Goldie, ad Teugels (1989, p. 15. Note that the b j (d coefficiets from the fractioal differece filter are a special case of a j (d. The processes (3 ad (4 are well defied because, with x 2 deotig the L 2 -orm, we have from (1 that x t 2 u t 2 + c j=1 l( j j d u t j 2 c for d < 0 usig Karamata s theorem; see Bigham, Goldie, ad Teugels (1989, p. 26. We base our results o the costructio of the followig two specific processes. DEFINITION 3. Let ε t be i.i.d. with mea zero, variace σε 2 > 0, ad fiite qth momet for some q 2 to be chose later. For such ε t we defie the followig two processes.

4 674 SØREN JOHANSEN AND MORTEN ØRREGAARD NIELSEN (i u 1t = ε t. (ii u 2t = ε t + 1+d ε t. For these two processes we ote the followig coectio with the classes U LIN (q ad U NED (q. LEMMA 1. For d ( 1 2,0 ad for i = 1,2,u it U LIN (q U NED (q, ad the log-ru variace of u it is σ 2 ε. Proof. Clearly, u 1t = ε t is cotaied i both U LIN (q ad U NED (q ad has log-ru variace σε 2. The process u 2t = ε t + 1+d ε t = ε t + j=0 b j( d 1ε t j is a liear process ad =0 j= b j ( d 1 2 c =1 j= j 2d 4 c =1 2d 3 c so that u 2t is i U LIN (q. To see that u 2t is i U NED (q, we calculate u 2t E(u 2t ε t m,...,ε t+m 2 = =m+1 c ( =m+1 b ( d 1ε t 2 2d 4 1/2 cm d 3/2. for d ( 12,0, Because 3 2 +d > 1 2 for d ( 1 2,0, this shows that u 2t is L 2 -NED of size 1 2 o ε t, ad hece u 2t is also i U NED (q. The geeratig fuctio for u 2t is f (z = 1 + (1 z 1+d, ad for z = 1 we fid because 1 + d > 0 that f (1 = 1. Therefore the log-ru variace of u 2t is lim T T E T ( ( εt + 1+d 2 ε t = f (1 2 Var(ε t = σε 2. We ext give a geeral formulatio of the FCLT for fractioal processes. For this purpose we defie the scaled partial sum process X T (ξ = σt [T ξ] x t, 0 ξ 1, (5 where σt 2 = E(T x t 2 ad [z] is the iteger part of the real umber z. FRACTIONAL FCLT FOR U(q. Let X T (ξ be give by (5 ad suppose x t is liear i u t. We say that the FCLT for fractioal Browia motio holds for a set U(q U 0 (q of processes if, for all u t U(q, it holds that X T (ξ D X (ξ i D[0,1], (6 where X (ξ is fractioal Browia motio.

5 NECESSARY MOMENT CONDITION FOR FRACTIONAL FCLT 675 D Here, deotes covergece i distributio (weak covergece i D[0, 1] edowed with the metric d 0 i Billigsley (1968, Ch. 3, Sect. 14, which iduces the Skorokhod topology ad uder which D[0,1] is complete. 3. THE NECESSITY RESULTS Our first mai result is the followig theorem. THEOREM 1. Let X T (ξ be defied by (5 for x t give by the fractioal coefficiets b j (d i (3 with 1 2 < d < 0, ad let U(q U 0(q be such that u it U(q for i = 1,2. If the fractioal FCLT holds for all u t i U(q for some q 2, the q q 0. Proof. We prove the theorem by assumig that there is a q 1 [2,q 0 for which the FCLT holds for U(q 1 ad show that this leads to a cotradictio by a careful costructio of ε t ad therefore u 1t ad u 2t. For u it,i = 1,2, we defie x it ad X it by (3 ad (5. Because u it is i U(q 1 the fractioal FCLT holds by the maitaied assumptio for u it, ad hece X it (ξ coverges i distributio to fractioal Browia motio. (a The ormalizig variace for X 1T. The variace of T x 1t = T d u 1t = T d ε t ca be foud i Davydov (1970; see also Lemma 3.2 of DDJ: σ 2 1T = E ( T x 1t 2 σ 2 ε V d T 2d+1, (7 where ( 1 1 V d = Ɣ(d d ((1 + τ d τ d 2 dτ. 0 (b The ormalizig variace for X 2T. We write x 2t ad X 2T i terms of x 1t ad X 1T, usig (3 ad (5: x 2t = d u 2t = x 1t + ε t ε t, (8 X 2T (ξ = σ2t [T ξ] x 2t = σ 1T σ 2T X 1T (ξ + σ 2T (ε [T ξ] ε 0. (9 We ext fid that the variace of T x 2t = T d u 2t = T (x 1t +ε t ε t is ( T 2 T 2 ( 2 σ2t 2 T = E x 2t = E( (x 1t + ε t ε t = E ε T ε 0 + x 1t T 2 T = E(ε T ε E( x 1t + 2E( d ε t (ε T ε 0.

6 676 SØREN JOHANSEN AND MORTEN ØRREGAARD NIELSEN The first term is costat, the ext is σ1t 2, ad lettig 1 {A} deote the idicator fuctio of the evet A, the last term cosists of T E( d T ε t ε T = b k (de(ε t k ε T k=0 = σ 2 ε ad T E( d u 1t ε 0 = Therefore, T k=0 T k=0 = σ 2 ε T k=0 b k (d1 {k=t T } = σ 2 ε b 0(d = σ 2 ε b k (de(ε t k ε 0 b k (d1 {k=t} c T t d c for d < 0. σ 2 2T σ 2 1T + c. (10 (c The cotradictio. We ow costruct the i.i.d. process ε t so that it has o momet higher tha q 1, i.e., E ε t q =for q > q 1, by choosig the tail to satisfy P( ε t q 1 1 c c(logc 2 as c. (11 I this case we still have E ε t q 1 <. We the fid P(σ 1T max ε t < c = P(σ1T ε 1 < c T = P( ε 1 q 1 < c q 1 σ q 1 1 t T 1T T = (1 P( ε 1 q 1 c q 1 T q 1/q 0 T ( 1 1 c q 1T q 1/q 0 (q 1 (logc + q0 log T 2 ( T 1 q 1/q 0 exp c q 1(q 1 (logc + q0 0 log T 2 as T because q 1 < q 0. Thus, σ1t max 1 t T ε t because P the ormalizig costat σ 1T = σ ε V 1/2 d T 1/q 0 = σ ε V 1/2 d T 1/2+d <σ ε V 1/2 d T 1/q 1 is too small to ormalize max 1 t T ε t correctly. The defiitio (9 implies the evaluatio max 0 ξ 1 ε [T ξ] max 0 ξ 1 ε [T ξ] ε 0 + ε 0 max 0 ξ 1 σ 2T X 2T (ξ + max 0 ξ 1 σ 1T X 1T (ξ + ε 0 T

7 such that σ 1T NECESSARY MOMENT CONDITION FOR FRACTIONAL FCLT 677 max ε [T ξ] max 0 ξ 1 0 ξ 1 σ 1T σ 2T X 2T (ξ + max 0 ξ 1 X 1T (ξ +σ 1T ε 0. (12 We have see i (7 ad (10 that σ2t 2 σ 1T 2 +c ad σ 1T 2 σ ε 2V d T 1+2d for d ( 1 2,0, so that σ 1T σ2t 1. Therefore, both σ1t σ 2T X 2T (ξ ad X 1T (ξ coverge i distributio by the previous results, ad it follows from (12 that σ1t max 0 ξ 1 ε [T ξ] is O P (1. This cotradicts that σ1t max P 1 t T ε t ad hece completes the proof of Theorem 1. The proof of Theorem 1 implies that the issue is that the rate of covergece, T (d+1/2 [T ξ],of d u 1t ca be very slow for d close to 1 2. Thus, more cotrol o the tail behavior of the u t sequece is eeded whe d ( 1 2,0, ad this is achieved through the momet coditio (1. We ed this sectio by givig a result that shows whe the momet coditio q > q 0 is ecessary istead of q q 0. The former is the momet coditio applied by, e.g., Taqqu (1975 ad Mariucci ad Robiso (2000. THEOREM 2. Let X T (ξ be defied by (5 for x t give by the geeral fractioal coefficiets i (4 with 1 2 < d < 0, ad let U(q U 0(q be such that u it U(q for i = 1,2. If the fractioal FCLT holds for all slowly varyig fuctios l( ad all u t i U(q for some q 2, the q > q 0. Proof. We assume that there is a q 1 [2,q 0 ] for which the FCLT holds for U(q 1 ad show that this leads to a cotradictio. For u it,i = 1,2, we defie x it ad X it by (4 ad (5 ad use the proof of Theorem 1 with the followig modificatios. (a From Karamata s theorem (see Bigham, Goldie, ad Teugels, 1989, p. 26, we fid that the ormalizig variace is σ1t 2 cl(t 2 T 2d+1 = cl(t 2 T 1/q 0. (c We choose the tail of ε t as i (11 i the proof of Theorem 1 ad take l(t = (log T ad fid P(σ 1T max ε t < c 1 t T = P(σ 1T ε 1 < c T = P( ε 1 q 1 < c q 1 σ q 1 1T T = (1 P( ε 1 q 1 c q 1 T q 1/q 0 l(t q 1 T ( 1 1 c q 1T q 1/q 0 l(t q 1(q 1 (logc + q0 log T + logl(t 2 ( T 1 q 1/q 0 l(t q 1 exp c q 1(q 1 (logc + q0 0 log T + logl(t 2 T

8 678 SØREN JOHANSEN AND MORTEN ØRREGAARD NIELSEN as T because q 1 q 0. Note that eve with q 1 = q 0 (ad q 0 > 2 because d < 0 we have the factor exp( c(log T q1 2 0, which esures the covergece to zero. The cotradictio follows exactly as i the proof of Theorem DISCUSSION I this sectio we preset two corollaries that demostrate how our results apply to the processes i Mariucci ad Robiso (2000 ad to those i DDJ, respectively. We first discuss the implicatios of Theorem 1 for the results of DDJ, who state (i our otatio the followig claim (give as Theorem 3.1 i DDJ. DDJ CLAIM. If X T (ξ is defied by (3 ad (5, where d < 1 2 ad u t U NED (q for q > 2, the X T (ξ D X (ξ i D[0,1], where X (ξ is fractioal Browia motio. It is oteworthy that U NED (q allows u t to have a very geeral depedece structure through the NED assumptio, but i particular that DDJ assume oly that sup t E u t q < for q > 2, which is weaker tha q q 0 if d < 0. The followig corollary to Theorem 1 shows how our result disproves Theorem 3.1 i DDJ. COROLLARY 1. Let X T (ξ be defied by (3 ad (5 with 1 2 < d < 0.Ifthe fractioal FCLT holds for all u t i U NED (q the q q 0. Proof. From Lemma 1 we kow that u 1t ad u 2t are i U NED (q, which by Theorem 1 proves the corollary. It follows from Corollary 1 that Theorem 3.1 of DDJ (ad their subsequet results relyig o Theorem 3.1 does ot hold uder their Assumptio 1. We fiish with a applicatio of our results to the processes i Mariucci ad Robiso (2000. COROLLARY 2. Let X T (ξ be defied by (4 ad (5 with 1 2 < d < 0.Ifthe fractioal FCLT holds for all slowly varyig fuctios l( ad all u t i U LIN (q the q > q 0. Thus, the momet coditio (1 with q > q 0 is both ecessary ad sufficiet for Theorem 1 of Mariucci ad Robiso (2000. Proof. The ecessity statemet follows from Theorem 2 because u 1t ad u 2t are i U LIN (q by Lemma 1. The sufficiecy statemet follows because the uivariate versio of Assumptio A of Mariucci ad Robiso (2000 (traslated to type I processes was i fact used to defie the class U LIN (q ad the coefficiets a j (d. It follows from Corollary 2 that q > q 0 is both ecessary ad sufficiet for the fractioal FCLT whe usig the coefficiets a j (d to defie a geeral fractioal process ad u t is a liear process of the type i U LIN (q. However, it does ot follow from our results that q q 0 is ecessary for the FCLT whe u t is a i.i.d. or

9 NECESSARY MOMENT CONDITION FOR FRACTIONAL FCLT 679 autoregressive movig average (ARMA process because the process u 2t eeded i the costructio is either i.i.d. or ARMA. REFERENCES Billigsley, P. (1968 Covergece of Probability Measures. Wiley. Bigham, N.H., C.M. Goldie, & J.L. Teugels (1989 Regular Variatio. Cambridge Uiversity Press. Davidso, J. & R.M. de Jog (2000 The fuctioal cetral limit theorem ad weak covergece to stochastic itegrals II: Fractioally itegrated processes. Ecoometric Theory 16, Davydov, Y.A. (1970 The ivariace priciple for statioary processes. Theory of Probability ad Its Applicatios 15, Mariucci, D. & P.M. Robiso (2000 Weak covergece of multivariate fractioal processes. Stochastic Processes ad Their Applicatios 86, Taqqu, M.S. (1975 Weak covergece to fractioal Browia motio ad to the Roseblatt process. Zeitschrift für Wahrscheilichkeitstheorie ud Verwadte Gebiete 31, Wu, W.B. & X. Shao (2006 Ivariace priciples for fractioally itegrated oliear processes. IMS Lecture Notes Moograph Series: Recet Developmets i Noparametric Iferece ad Probability 50,

QED. Queen s Economics Department Working Paper No. 1244

QED. Queen s Economics Department Working Paper No. 1244 QED Queen s Economics Department Working Paper No. 1244 A necessary moment condition for the fractional functional central limit theorem Søren Johansen University of Copenhagen and CREATES Morten Ørregaard

More information

Mi-Hwa Ko and Tae-Sung Kim

Mi-Hwa Ko and Tae-Sung Kim J. Korea Math. Soc. 42 2005), No. 5, pp. 949 957 ALMOST SURE CONVERGENCE FOR WEIGHTED SUMS OF NEGATIVELY ORTHANT DEPENDENT RANDOM VARIABLES Mi-Hwa Ko ad Tae-Sug Kim Abstract. For weighted sum of a sequece

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 Fuctioal Law of Large Numbers. Costructio of the Wieer Measure Cotet. 1. Additioal techical results o weak covergece

More information

A Weak Law of Large Numbers Under Weak Mixing

A Weak Law of Large Numbers Under Weak Mixing A Weak Law of Large Numbers Uder Weak Mixig Bruce E. Hase Uiversity of Wiscosi Jauary 209 Abstract This paper presets a ew weak law of large umbers (WLLN) for heterogeous depedet processes ad arrays. The

More information

Sequences and Series of Functions

Sequences and Series of Functions Chapter 6 Sequeces ad Series of Fuctios 6.1. Covergece of a Sequece of Fuctios Poitwise Covergece. Defiitio 6.1. Let, for each N, fuctio f : A R be defied. If, for each x A, the sequece (f (x)) coverges

More information

Kolmogorov-Smirnov type Tests for Local Gaussianity in High-Frequency Data

Kolmogorov-Smirnov type Tests for Local Gaussianity in High-Frequency Data Proceedigs 59th ISI World Statistics Cogress, 5-30 August 013, Hog Kog (Sessio STS046) p.09 Kolmogorov-Smirov type Tests for Local Gaussiaity i High-Frequecy Data George Tauche, Duke Uiversity Viktor Todorov,

More information

Research Article Moment Inequality for ϕ-mixing Sequences and Its Applications

Research Article Moment Inequality for ϕ-mixing Sequences and Its Applications Hidawi Publishig Corporatio Joural of Iequalities ad Applicatios Volume 2009, Article ID 379743, 2 pages doi:0.55/2009/379743 Research Article Momet Iequality for ϕ-mixig Sequeces ad Its Applicatios Wag

More information

Berry-Esseen bounds for self-normalized martingales

Berry-Esseen bounds for self-normalized martingales Berry-Essee bouds for self-ormalized martigales Xiequa Fa a, Qi-Ma Shao b a Ceter for Applied Mathematics, Tiaji Uiversity, Tiaji 30007, Chia b Departmet of Statistics, The Chiese Uiversity of Hog Kog,

More information

Regression with an Evaporating Logarithmic Trend

Regression with an Evaporating Logarithmic Trend Regressio with a Evaporatig Logarithmic Tred Peter C. B. Phillips Cowles Foudatio, Yale Uiversity, Uiversity of Aucklad & Uiversity of York ad Yixiao Su Departmet of Ecoomics Yale Uiversity October 5,

More information

7.1 Convergence of sequences of random variables

7.1 Convergence of sequences of random variables Chapter 7 Limit Theorems Throughout this sectio we will assume a probability space (, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite

More information

Asymptotic distribution of products of sums of independent random variables

Asymptotic distribution of products of sums of independent random variables Proc. Idia Acad. Sci. Math. Sci. Vol. 3, No., May 03, pp. 83 9. c Idia Academy of Scieces Asymptotic distributio of products of sums of idepedet radom variables YANLING WANG, SUXIA YAO ad HONGXIA DU ollege

More information

CREATES Research Paper A necessary moment condition for the fractional functional central limit theorem

CREATES Research Paper A necessary moment condition for the fractional functional central limit theorem CREATES Research Paper 2010-70 A necessary moment condition for the fractional functional central limit theorem Søren Johansen and Morten Ørregaard Nielsen School of Economics and Management Aarhus University

More information

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence Chapter 3 Strog covergece As poited out i the Chapter 2, there are multiple ways to defie the otio of covergece of a sequece of radom variables. That chapter defied covergece i probability, covergece i

More information

Definition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4.

Definition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4. 4. BASES I BAACH SPACES 39 4. BASES I BAACH SPACES Sice a Baach space X is a vector space, it must possess a Hamel, or vector space, basis, i.e., a subset {x γ } γ Γ whose fiite liear spa is all of X ad

More information

Kernel density estimator

Kernel density estimator Jauary, 07 NONPARAMETRIC ERNEL DENSITY ESTIMATION I this lecture, we discuss kerel estimatio of probability desity fuctios PDF Noparametric desity estimatio is oe of the cetral problems i statistics I

More information

Notes 27 : Brownian motion: path properties

Notes 27 : Brownian motion: path properties Notes 27 : Browia motio: path properties Math 733-734: Theory of Probability Lecturer: Sebastie Roch Refereces:[Dur10, Sectio 8.1], [MP10, Sectio 1.1, 1.2, 1.3]. Recall: DEF 27.1 (Covariace) Let X = (X

More information

This section is optional.

This section is optional. 4 Momet Geeratig Fuctios* This sectio is optioal. The momet geeratig fuctio g : R R of a radom variable X is defied as g(t) = E[e tx ]. Propositio 1. We have g () (0) = E[X ] for = 1, 2,... Proof. Therefore

More information

Lecture 8: Convergence of transformations and law of large numbers

Lecture 8: Convergence of transformations and law of large numbers Lecture 8: Covergece of trasformatios ad law of large umbers Trasformatio ad covergece Trasformatio is a importat tool i statistics. If X coverges to X i some sese, we ofte eed to check whether g(x ) coverges

More information

Central limit theorem and almost sure central limit theorem for the product of some partial sums

Central limit theorem and almost sure central limit theorem for the product of some partial sums Proc. Idia Acad. Sci. Math. Sci. Vol. 8, No. 2, May 2008, pp. 289 294. Prited i Idia Cetral it theorem ad almost sure cetral it theorem for the product of some partial sums YU MIAO College of Mathematics

More information

An almost sure invariance principle for trimmed sums of random vectors

An almost sure invariance principle for trimmed sums of random vectors Proc. Idia Acad. Sci. Math. Sci. Vol. 20, No. 5, November 200, pp. 6 68. Idia Academy of Scieces A almost sure ivariace priciple for trimmed sums of radom vectors KE-ANG FU School of Statistics ad Mathematics,

More information

1 Convergence in Probability and the Weak Law of Large Numbers

1 Convergence in Probability and the Weak Law of Large Numbers 36-752 Advaced Probability Overview Sprig 2018 8. Covergece Cocepts: i Probability, i L p ad Almost Surely Istructor: Alessadro Rialdo Associated readig: Sec 2.4, 2.5, ad 4.11 of Ash ad Doléas-Dade; Sec

More information

Convergence of random variables. (telegram style notes) P.J.C. Spreij

Convergence of random variables. (telegram style notes) P.J.C. Spreij Covergece of radom variables (telegram style otes).j.c. Spreij this versio: September 6, 2005 Itroductio As we kow, radom variables are by defiitio measurable fuctios o some uderlyig measurable space

More information

Lecture 19: Convergence

Lecture 19: Convergence Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may

More information

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors ECONOMETRIC THEORY MODULE XIII Lecture - 34 Asymptotic Theory ad Stochastic Regressors Dr. Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Asymptotic theory The asymptotic

More information

2.2. Central limit theorem.

2.2. Central limit theorem. 36.. Cetral limit theorem. The most ideal case of the CLT is that the radom variables are iid with fiite variace. Although it is a special case of the more geeral Lideberg-Feller CLT, it is most stadard

More information

7.1 Convergence of sequences of random variables

7.1 Convergence of sequences of random variables Chapter 7 Limit theorems Throughout this sectio we will assume a probability space (Ω, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite

More information

LECTURE 8: ASYMPTOTICS I

LECTURE 8: ASYMPTOTICS I LECTURE 8: ASYMPTOTICS I We are iterested i the properties of estimators as. Cosider a sequece of radom variables {, X 1}. N. M. Kiefer, Corell Uiversity, Ecoomics 60 1 Defiitio: (Weak covergece) A sequece

More information

A Note on the Kolmogorov-Feller Weak Law of Large Numbers

A Note on the Kolmogorov-Feller Weak Law of Large Numbers Joural of Mathematical Research with Applicatios Mar., 015, Vol. 35, No., pp. 3 8 DOI:10.3770/j.iss:095-651.015.0.013 Http://jmre.dlut.edu.c A Note o the Kolmogorov-Feller Weak Law of Large Numbers Yachu

More information

Diagonal approximations by martingales

Diagonal approximations by martingales Alea 7, 257 276 200 Diagoal approximatios by martigales Jaa Klicarová ad Dalibor Volý Faculty of Ecoomics, Uiversity of South Bohemia, Studetsa 3, 370 05, Cese Budejovice, Czech Republic E-mail address:

More information

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula Joural of Multivariate Aalysis 102 (2011) 1315 1319 Cotets lists available at ScieceDirect Joural of Multivariate Aalysis joural homepage: www.elsevier.com/locate/jmva Superefficiet estimatio of the margials

More information

Self-normalized deviation inequalities with application to t-statistic

Self-normalized deviation inequalities with application to t-statistic Self-ormalized deviatio iequalities with applicatio to t-statistic Xiequa Fa Ceter for Applied Mathematics, Tiaji Uiversity, 30007 Tiaji, Chia Abstract Let ξ i i 1 be a sequece of idepedet ad symmetric

More information

Notes 5 : More on the a.s. convergence of sums

Notes 5 : More on the a.s. convergence of sums Notes 5 : More o the a.s. covergece of sums Math 733-734: Theory of Probability Lecturer: Sebastie Roch Refereces: Dur0, Sectios.5; Wil9, Sectio 4.7, Shi96, Sectio IV.4, Dur0, Sectio.. Radom series. Three-series

More information

Precise Rates in Complete Moment Convergence for Negatively Associated Sequences

Precise Rates in Complete Moment Convergence for Negatively Associated Sequences Commuicatios of the Korea Statistical Society 29, Vol. 16, No. 5, 841 849 Precise Rates i Complete Momet Covergece for Negatively Associated Sequeces Dae-Hee Ryu 1,a a Departmet of Computer Sciece, ChugWoo

More information

A central limit theorem for moving average process with negatively associated innovation

A central limit theorem for moving average process with negatively associated innovation Iteratioal Mathematical Forum, 1, 2006, o. 10, 495-502 A cetral limit theorem for movig average process with egatively associated iovatio MI-HWA KO Statistical Research Ceter for Complex Systems Seoul

More information

A note on self-normalized Dickey-Fuller test for unit root in autoregressive time series with GARCH errors

A note on self-normalized Dickey-Fuller test for unit root in autoregressive time series with GARCH errors Appl. Math. J. Chiese Uiv. 008, 3(): 97-0 A ote o self-ormalized Dickey-Fuller test for uit root i autoregressive time series with GARCH errors YANG Xiao-rog ZHANG Li-xi Abstract. I this article, the uit

More information

Limit distributions for products of sums

Limit distributions for products of sums Statistics & Probability Letters 62 (23) 93 Limit distributios for products of sums Yogcheg Qi Departmet of Mathematics ad Statistics, Uiversity of Miesota-Duluth, Campus Ceter 4, 7 Uiversity Drive, Duluth,

More information

Riesz-Fischer Sequences and Lower Frame Bounds

Riesz-Fischer Sequences and Lower Frame Bounds Zeitschrift für Aalysis ud ihre Aweduge Joural for Aalysis ad its Applicatios Volume 1 (00), No., 305 314 Riesz-Fischer Sequeces ad Lower Frame Bouds P. Casazza, O. Christese, S. Li ad A. Lider Abstract.

More information

MA Advanced Econometrics: Properties of Least Squares Estimators

MA Advanced Econometrics: Properties of Least Squares Estimators MA Advaced Ecoometrics: Properties of Least Squares Estimators Karl Whela School of Ecoomics, UCD February 5, 20 Karl Whela UCD Least Squares Estimators February 5, 20 / 5 Part I Least Squares: Some Fiite-Sample

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 6 9/23/2013. Brownian motion. Introduction

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 6 9/23/2013. Brownian motion. Introduction MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/5.070J Fall 203 Lecture 6 9/23/203 Browia motio. Itroductio Cotet.. A heuristic costructio of a Browia motio from a radom walk. 2. Defiitio ad basic properties

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

Metric Space Properties

Metric Space Properties Metric Space Properties Math 40 Fial Project Preseted by: Michael Brow, Alex Cordova, ad Alyssa Sachez We have already poited out ad will recogize throughout this book the importace of compact sets. All

More information

MAT1026 Calculus II Basic Convergence Tests for Series

MAT1026 Calculus II Basic Convergence Tests for Series MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real

More information

4. Partial Sums and the Central Limit Theorem

4. Partial Sums and the Central Limit Theorem 1 of 10 7/16/2009 6:05 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 4. Partial Sums ad the Cetral Limit Theorem The cetral limit theorem ad the law of large umbers are the two fudametal theorems

More information

Math Solutions to homework 6

Math Solutions to homework 6 Math 175 - Solutios to homework 6 Cédric De Groote November 16, 2017 Problem 1 (8.11 i the book): Let K be a compact Hermitia operator o a Hilbert space H ad let the kerel of K be {0}. Show that there

More information

Notes 19 : Martingale CLT

Notes 19 : Martingale CLT Notes 9 : Martigale CLT Math 733-734: Theory of Probability Lecturer: Sebastie Roch Refereces: [Bil95, Chapter 35], [Roc, Chapter 3]. Sice we have ot ecoutered weak covergece i some time, we first recall

More information

17. Joint distributions of extreme order statistics Lehmann 5.1; Ferguson 15

17. Joint distributions of extreme order statistics Lehmann 5.1; Ferguson 15 17. Joit distributios of extreme order statistics Lehma 5.1; Ferguso 15 I Example 10., we derived the asymptotic distributio of the maximum from a radom sample from a uiform distributio. We did this usig

More information

Introduction to Extreme Value Theory Laurens de Haan, ISM Japan, Erasmus University Rotterdam, NL University of Lisbon, PT

Introduction to Extreme Value Theory Laurens de Haan, ISM Japan, Erasmus University Rotterdam, NL University of Lisbon, PT Itroductio to Extreme Value Theory Laures de Haa, ISM Japa, 202 Itroductio to Extreme Value Theory Laures de Haa Erasmus Uiversity Rotterdam, NL Uiversity of Lisbo, PT Itroductio to Extreme Value Theory

More information

Generalized Law of the Iterated Logarithm and Its Convergence Rate

Generalized Law of the Iterated Logarithm and Its Convergence Rate Stochastic Aalysis ad Applicatios, 25: 89 03, 2007 Copyright Taylor & Fracis Group, LLC ISSN 0736-2994 prit/532-9356 olie DOI: 0.080/073629906005997 Geeralized Law of the Iterated Logarithm ad Its Covergece

More information

Research Article On the Strong Laws for Weighted Sums of ρ -Mixing Random Variables

Research Article On the Strong Laws for Weighted Sums of ρ -Mixing Random Variables Hidawi Publishig Corporatio Joural of Iequalities ad Applicatios Volume 2011, Article ID 157816, 8 pages doi:10.1155/2011/157816 Research Article O the Strog Laws for Weighted Sums of ρ -Mixig Radom Variables

More information

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3 MATH 337 Sequeces Dr. Neal, WKU Let X be a metric space with distace fuctio d. We shall defie the geeral cocept of sequece ad limit i a metric space, the apply the results i particular to some special

More information

Slide Set 13 Linear Model with Endogenous Regressors and the GMM estimator

Slide Set 13 Linear Model with Endogenous Regressors and the GMM estimator Slide Set 13 Liear Model with Edogeous Regressors ad the GMM estimator Pietro Coretto pcoretto@uisa.it Ecoometrics Master i Ecoomics ad Fiace (MEF) Uiversità degli Studi di Napoli Federico II Versio: Friday

More information

The variance of a sum of independent variables is the sum of their variances, since covariances are zero. Therefore. V (xi )= n n 2 σ2 = σ2.

The variance of a sum of independent variables is the sum of their variances, since covariances are zero. Therefore. V (xi )= n n 2 σ2 = σ2. SAMPLE STATISTICS A radom sample x 1,x,,x from a distributio f(x) is a set of idepedetly ad idetically variables with x i f(x) for all i Their joit pdf is f(x 1,x,,x )=f(x 1 )f(x ) f(x )= f(x i ) The sample

More information

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002 ECE 330:541, Stochastic Sigals ad Systems Lecture Notes o Limit Theorems from robability Fall 00 I practice, there are two ways we ca costruct a ew sequece of radom variables from a old sequece of radom

More information

Chapter 6 Infinite Series

Chapter 6 Infinite Series Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat

More information

Lecture 20: Multivariate convergence and the Central Limit Theorem

Lecture 20: Multivariate convergence and the Central Limit Theorem Lecture 20: Multivariate covergece ad the Cetral Limit Theorem Covergece i distributio for radom vectors Let Z,Z 1,Z 2,... be radom vectors o R k. If the cdf of Z is cotiuous, the we ca defie covergece

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

EFFECTIVE WLLN, SLLN, AND CLT IN STATISTICAL MODELS

EFFECTIVE WLLN, SLLN, AND CLT IN STATISTICAL MODELS EFFECTIVE WLLN, SLLN, AND CLT IN STATISTICAL MODELS Ryszard Zieliński Ist Math Polish Acad Sc POBox 21, 00-956 Warszawa 10, Polad e-mail: rziel@impagovpl ABSTRACT Weak laws of large umbers (W LLN), strog

More information

STA Object Data Analysis - A List of Projects. January 18, 2018

STA Object Data Analysis - A List of Projects. January 18, 2018 STA 6557 Jauary 8, 208 Object Data Aalysis - A List of Projects. Schoeberg Mea glaucomatous shape chages of the Optic Nerve Head regio i aimal models 2. Aalysis of VW- Kedall ati-mea shapes with a applicatio

More information

The Central Limit Theorem

The Central Limit Theorem Chapter The Cetral Limit Theorem Deote by Z the stadard ormal radom variable with desity 2π e x2 /2. Lemma.. Ee itz = e t2 /2 Proof. We use the same calculatio as for the momet geeratig fuctio: exp(itx

More information

COMPLETE CONVERGENCE AND COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF ROWWISE END RANDOM VARIABLES

COMPLETE CONVERGENCE AND COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF ROWWISE END RANDOM VARIABLES GLASNIK MATEMATIČKI Vol. 4969)2014), 449 468 COMPLETE CONVERGENCE AND COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF ROWWISE END RANDOM VARIABLES Yogfeg Wu, Mauel Ordóñez Cabrera ad Adrei Volodi Toglig Uiversity

More information

Hyun-Chull Kim and Tae-Sung Kim

Hyun-Chull Kim and Tae-Sung Kim Commu. Korea Math. Soc. 20 2005), No. 3, pp. 531 538 A CENTRAL LIMIT THEOREM FOR GENERAL WEIGHTED SUM OF LNQD RANDOM VARIABLES AND ITS APPLICATION Hyu-Chull Kim ad Tae-Sug Kim Abstract. I this paper we

More information

BIRKHOFF ERGODIC THEOREM

BIRKHOFF ERGODIC THEOREM BIRKHOFF ERGODIC THEOREM Abstract. We will give a proof of the poitwise ergodic theorem, which was first proved by Birkhoff. May improvemets have bee made sice Birkhoff s orgial proof. The versio we give

More information

Distribution of Random Samples & Limit theorems

Distribution of Random Samples & Limit theorems STAT/MATH 395 A - PROBABILITY II UW Witer Quarter 2017 Néhémy Lim Distributio of Radom Samples & Limit theorems 1 Distributio of i.i.d. Samples Motivatig example. Assume that the goal of a study is to

More information

MAXIMAL INEQUALITIES AND STRONG LAW OF LARGE NUMBERS FOR AANA SEQUENCES

MAXIMAL INEQUALITIES AND STRONG LAW OF LARGE NUMBERS FOR AANA SEQUENCES Commu Korea Math Soc 26 20, No, pp 5 6 DOI 0434/CKMS20265 MAXIMAL INEQUALITIES AND STRONG LAW OF LARGE NUMBERS FOR AANA SEQUENCES Wag Xueju, Hu Shuhe, Li Xiaoqi, ad Yag Wezhi Abstract Let {X, } be a sequece

More information

Asymptotic Results for the Linear Regression Model

Asymptotic Results for the Linear Regression Model Asymptotic Results for the Liear Regressio Model C. Fli November 29, 2000 1. Asymptotic Results uder Classical Assumptios The followig results apply to the liear regressio model y = Xβ + ε, where X is

More information

Properties of Fuzzy Length on Fuzzy Set

Properties of Fuzzy Length on Fuzzy Set Ope Access Library Joural 206, Volume 3, e3068 ISSN Olie: 2333-972 ISSN Prit: 2333-9705 Properties of Fuzzy Legth o Fuzzy Set Jehad R Kider, Jaafar Imra Mousa Departmet of Mathematics ad Computer Applicatios,

More information

LIMIT THEOREMS FOR WEIGHTED SUMS OF INFINITE VARIANCE RANDOM VARIABLES ATTRACTED TO INTEGRALS OF LINEAR FRACTIONAL STABLE MOTIONS

LIMIT THEOREMS FOR WEIGHTED SUMS OF INFINITE VARIANCE RANDOM VARIABLES ATTRACTED TO INTEGRALS OF LINEAR FRACTIONAL STABLE MOTIONS LIMIT THEOREMS FOR WEIGHTED SUMS OF INFINITE VARIANCE RANDOM VARIABLES ATTRACTED TO INTEGRALS OF LINEAR FRACTIONAL STABLE MOTIONS MAKOTO MAEJIMA (KEIO UNIVERSITY) AND SAKURAKO SUZUKI (OCHANOMIZU UNIVERSITY)

More information

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss ECE 90 Lecture : Complexity Regularizatio ad the Squared Loss R. Nowak 5/7/009 I the previous lectures we made use of the Cheroff/Hoeffdig bouds for our aalysis of classifier errors. Hoeffdig s iequality

More information

Lecture 4. We also define the set of possible values for the random walk as the set of all x R d such that P(S n = x) > 0 for some n.

Lecture 4. We also define the set of possible values for the random walk as the set of all x R d such that P(S n = x) > 0 for some n. Radom Walks ad Browia Motio Tel Aviv Uiversity Sprig 20 Lecture date: Mar 2, 20 Lecture 4 Istructor: Ro Peled Scribe: Lira Rotem This lecture deals primarily with recurrece for geeral radom walks. We preset

More information

1 The Haar functions and the Brownian motion

1 The Haar functions and the Brownian motion 1 The Haar fuctios ad the Browia motio 1.1 The Haar fuctios ad their completeess The Haar fuctios The basic Haar fuctio is 1 if x < 1/2, ψx) = 1 if 1/2 x < 1, otherwise. 1.1) It has mea zero 1 ψx)dx =,

More information

Boundaries and the James theorem

Boundaries and the James theorem Boudaries ad the James theorem L. Vesely 1. Itroductio The followig theorem is importat ad well kow. All spaces cosidered here are real ormed or Baach spaces. Give a ormed space X, we deote by B X ad S

More information

A Hilbert Space Central Limit Theorem for Geometrically Ergodic Markov Chains

A Hilbert Space Central Limit Theorem for Geometrically Ergodic Markov Chains A Hilbert Space Cetral Limit Theorem for Geometrically Ergodic Marov Chais Joh Stachursi Research School of Ecoomics, Australia Natioal Uiversity Abstract This ote proves a simple but useful cetral limit

More information

Establishing conditions for weak convergence to stochastic integrals

Establishing conditions for weak convergence to stochastic integrals Establishig coditios for weak covergece to stochastic itegrals August 7, 26 Jiagya Peg School of Mathematical Scieces School of Maagemet ad Ecoomics Uiversity of Electroic Sciece ad Techology of Chia,

More information

Overview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions

Overview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions Chapter 9 Slide Ifereces from Two Samples 9- Overview 9- Ifereces about Two Proportios 9- Ifereces about Two Meas: Idepedet Samples 9-4 Ifereces about Matched Pairs 9-5 Comparig Variatio i Two Samples

More information

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function. MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied

More information

ON POINTWISE BINOMIAL APPROXIMATION

ON POINTWISE BINOMIAL APPROXIMATION Iteratioal Joural of Pure ad Applied Mathematics Volume 71 No. 1 2011, 57-66 ON POINTWISE BINOMIAL APPROXIMATION BY w-functions K. Teerapabolar 1, P. Wogkasem 2 Departmet of Mathematics Faculty of Sciece

More information

Appendix to: Hypothesis Testing for Multiple Mean and Correlation Curves with Functional Data

Appendix to: Hypothesis Testing for Multiple Mean and Correlation Curves with Functional Data Appedix to: Hypothesis Testig for Multiple Mea ad Correlatio Curves with Fuctioal Data Ao Yua 1, Hog-Bi Fag 1, Haiou Li 1, Coli O. Wu, Mig T. Ta 1, 1 Departmet of Biostatistics, Bioiformatics ad Biomathematics,

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Complete Convergence for Asymptotically Almost Negatively Associated Random Variables

Complete Convergence for Asymptotically Almost Negatively Associated Random Variables Applied Mathematical Scieces, Vol. 12, 2018, o. 30, 1441-1452 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2018.810142 Complete Covergece for Asymptotically Almost Negatively Associated Radom

More information

Singular Continuous Measures by Michael Pejic 5/14/10

Singular Continuous Measures by Michael Pejic 5/14/10 Sigular Cotiuous Measures by Michael Peic 5/4/0 Prelimiaries Give a set X, a σ-algebra o X is a collectio of subsets of X that cotais X ad ad is closed uder complemetatio ad coutable uios hece, coutable

More information

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample. Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized

More information

Analytic Continuation

Analytic Continuation Aalytic Cotiuatio The stadard example of this is give by Example Let h (z) = 1 + z + z 2 + z 3 +... kow to coverge oly for z < 1. I fact h (z) = 1/ (1 z) for such z. Yet H (z) = 1/ (1 z) is defied for

More information

1 Introduction to reducing variance in Monte Carlo simulations

1 Introduction to reducing variance in Monte Carlo simulations Copyright c 010 by Karl Sigma 1 Itroductio to reducig variace i Mote Carlo simulatios 11 Review of cofidece itervals for estimatig a mea I statistics, we estimate a ukow mea µ = E(X) of a distributio by

More information

SOME GENERALIZATIONS OF OLIVIER S THEOREM

SOME GENERALIZATIONS OF OLIVIER S THEOREM SOME GENERALIZATIONS OF OLIVIER S THEOREM Alai Faisat, Sait-Étiee, Georges Grekos, Sait-Étiee, Ladislav Mišík Ostrava (Received Jauary 27, 2006) Abstract. Let a be a coverget series of positive real umbers.

More information

32 estimating the cumulative distribution function

32 estimating the cumulative distribution function 32 estimatig the cumulative distributio fuctio 4.6 types of cofidece itervals/bads Let F be a class of distributio fuctios F ad let θ be some quatity of iterest, such as the mea of F or the whole fuctio

More information

Solutions to HW Assignment 1

Solutions to HW Assignment 1 Solutios to HW: 1 Course: Theory of Probability II Page: 1 of 6 Uiversity of Texas at Austi Solutios to HW Assigmet 1 Problem 1.1. Let Ω, F, {F } 0, P) be a filtered probability space ad T a stoppig time.

More information

6. Uniform distribution mod 1

6. Uniform distribution mod 1 6. Uiform distributio mod 1 6.1 Uiform distributio ad Weyl s criterio Let x be a seuece of real umbers. We may decompose x as the sum of its iteger part [x ] = sup{m Z m x } (i.e. the largest iteger which

More information

Random Walks on Discrete and Continuous Circles. by Jeffrey S. Rosenthal School of Mathematics, University of Minnesota, Minneapolis, MN, U.S.A.

Random Walks on Discrete and Continuous Circles. by Jeffrey S. Rosenthal School of Mathematics, University of Minnesota, Minneapolis, MN, U.S.A. Radom Walks o Discrete ad Cotiuous Circles by Jeffrey S. Rosethal School of Mathematics, Uiversity of Miesota, Mieapolis, MN, U.S.A. 55455 (Appeared i Joural of Applied Probability 30 (1993), 780 789.)

More information

REAL ANALYSIS II: PROBLEM SET 1 - SOLUTIONS

REAL ANALYSIS II: PROBLEM SET 1 - SOLUTIONS REAL ANALYSIS II: PROBLEM SET 1 - SOLUTIONS 18th Feb, 016 Defiitio (Lipschitz fuctio). A fuctio f : R R is said to be Lipschitz if there exists a positive real umber c such that for ay x, y i the domai

More information

An Introduction to Randomized Algorithms

An Introduction to Randomized Algorithms A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

Assignment 5: Solutions

Assignment 5: Solutions McGill Uiversity Departmet of Mathematics ad Statistics MATH 54 Aalysis, Fall 05 Assigmet 5: Solutios. Let y be a ubouded sequece of positive umbers satisfyig y + > y for all N. Let x be aother sequece

More information

Lecture Chapter 6: Convergence of Random Sequences

Lecture Chapter 6: Convergence of Random Sequences ECE5: Aalysis of Radom Sigals Fall 6 Lecture Chapter 6: Covergece of Radom Sequeces Dr Salim El Rouayheb Scribe: Abhay Ashutosh Doel, Qibo Zhag, Peiwe Tia, Pegzhe Wag, Lu Liu Radom sequece Defiitio A ifiite

More information

ST5215: Advanced Statistical Theory

ST5215: Advanced Statistical Theory ST525: Advaced Statistical Theory Departmet of Statistics & Applied Probability Tuesday, September 7, 2 ST525: Advaced Statistical Theory Lecture : The law of large umbers The Law of Large Numbers The

More information

IRRATIONALITY MEASURES, IRRATIONALITY BASES, AND A THEOREM OF JARNÍK 1. INTRODUCTION

IRRATIONALITY MEASURES, IRRATIONALITY BASES, AND A THEOREM OF JARNÍK 1. INTRODUCTION IRRATIONALITY MEASURES IRRATIONALITY BASES AND A THEOREM OF JARNÍK JONATHAN SONDOW ABSTRACT. We recall that the irratioality expoet µα ( ) of a irratioal umber α is defied usig the irratioality measure

More information

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1. Eco 325/327 Notes o Sample Mea, Sample Proportio, Cetral Limit Theorem, Chi-square Distributio, Studet s t distributio 1 Sample Mea By Hiro Kasahara We cosider a radom sample from a populatio. Defiitio

More information

Large holes in quasi-random graphs

Large holes in quasi-random graphs Large holes i quasi-radom graphs Joaa Polcy Departmet of Discrete Mathematics Adam Mickiewicz Uiversity Pozań, Polad joaska@amuedupl Submitted: Nov 23, 2006; Accepted: Apr 10, 2008; Published: Apr 18,

More information

A RANK STATISTIC FOR NON-PARAMETRIC K-SAMPLE AND CHANGE POINT PROBLEMS

A RANK STATISTIC FOR NON-PARAMETRIC K-SAMPLE AND CHANGE POINT PROBLEMS J. Japa Statist. Soc. Vol. 41 No. 1 2011 67 73 A RANK STATISTIC FOR NON-PARAMETRIC K-SAMPLE AND CHANGE POINT PROBLEMS Yoichi Nishiyama* We cosider k-sample ad chage poit problems for idepedet data i a

More information

Statistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons

Statistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons Statistical Aalysis o Ucertaity for Autocorrelated Measuremets ad its Applicatios to Key Comparisos Nie Fa Zhag Natioal Istitute of Stadards ad Techology Gaithersburg, MD 0899, USA Outlies. Itroductio.

More information

INFINITE SEQUENCES AND SERIES

INFINITE SEQUENCES AND SERIES 11 INFINITE SEQUENCES AND SERIES INFINITE SEQUENCES AND SERIES 11.4 The Compariso Tests I this sectio, we will lear: How to fid the value of a series by comparig it with a kow series. COMPARISON TESTS

More information