Internet Appendix to: Idea Sharing and the Performance of Mutual Funds
|
|
- Sheena Knight
- 5 years ago
- Views:
Transcription
1 Coes Iere Appedx o: Idea harg ad he Perforace of Muual Fuds Jule Cujea IA. Proof of Lea A IA. Proof of Lea A IA.3 Proof of Lea B IA.4 Populao dyacs he geeral seg of eco IA.5 Average rajecores of uber of deas IA.6 Proof of Proposo 9 (dsrbuo shf relave o luck) IA.7 Deals o copuaos eco 6 (fud flows ad fees) IA.. Proof of Lea A. Deoe by p( F ) he codoal desy of wh respec o F.Fxgae = k ad applyg Bayes rule, he codoal desy p( F ) sasfes he recursve relao p( F )= p( F )f( ) R R p(x F )f( x)dx (IA.) where f( ) deoes he desy of a vecor of sgals codoal o ad where p( F ) sasfes p( F )= o ( exp b! ) (IA.) sce, fro Theore A., p( F ) s codoally Gaussa for ay ( k, k ) (Lpser ad h hryaev (), Theore.6). Frs, le =... > be he vecor of sgals he sequece ( :applej apple j ). Codoal o, hese sgals are depede ad hus f( ) = ( ) Y j= j A A. (IA.3) Afer subsug (IA.3) (IA.) ad egrag, he codoal desy p( F )sexplcly gve by s p( F )= o B o b o P j= j C A o (IA.4) C A C A.
2 ecod, le = Y be he aggregae sgal, whch case f(y ) = exp Y. (IA.5) ubsug (IA.5) (IA.) ad egrag, he codoal desy (IA.4) becoes s p( F )= o o b o Y / o Clearly, he expressos (IA.4) ad (IA.6) are equvale f ad oly f Y =( ad, by duco, hs resul us be rue for all = k ad k {,,...,N T }. IA.. Proof of Lea A.3 (IA.6) ) P j= j! A. Observe ha he Browa oo B bc s adaped o F c ad, herefore, o F cobe (57) ad (74) ad oba F c. ecod, d b B =d b B c (,, (, a, )( b b c ))d. (IA.7) ce F c F, follows ha b b c =,,, whch, subsued (IA.7) ad usg (6) gves (86). Thrd, for EP bc [Z] = o hold,.e., for P b o be absoluely couous wh respec o bp c uder F, he Rado-Nkody dervave (85) us be a argale. A su ce codo uder whch Z s a argale s he Novkov codo (Karazas ad hreve (988), Proposo 5.): P E appleexp bc Z T (k ) d <, apple T apple (IA.8) where he process uder b P c sasfes d = o ( )k d k (o ( ) o c )d b B c. (IA.9) Observg ha he process (IA.9) s Gaussa, Exaple 3 (a) (Lpser ad hryaev (), p. 33) shows ha he Novkov codo (IA.8) bols dow o sup applet k E b P c <, I hs seup, he codos (IA.) splfy o supapplet k V b P c <. (IA.) sup applet k R k sds <, sup applet k R k sds <. (IA.) Usg (6) ad acpag o he equlbru resul of Lea C., he wo codos (IA.) are equvale o requrg ha he fuco ( ) be couous, whch s by assupo. Theore A.3 he follows fro Grsaov heore (Karazas ad hreve (988), Theore 5.).
3 IA.3. Proof of Lea B. Cosder frs he rgh-had sde of (38). Fro (34), I oba (, ) A> Q, A Q, BQ, (, ) =! >! k Furherore, fro (35), I ca wre o ()o ( ) = k o ()o ( ). (IA.) B,( )A Q, B,()A Q, A, (, ) = A, (, ) k B Q, B o ()o ( ), Q, (IA.3) whch, subsued (IA.) yelds B >,()A Q, (, ) = k o ()o ( ) (, ) A, B Q, = k o ()o ( ) k o () o ()o ( ) B,()A Q, A, (, ) B Q, (IA.4) (IA.5) where he secod equaly follows fro splfcaos based o (6). ecod, cosder he lef-had sde of (38) ad drecly d ereae (7) usg (69) o oba (, ) = o ()o ( ) k o ()o ( )(o ()o ( )). (IA.6) Fally, regroupg ers (IA.4) ad usg Eq. 68 shows ha (IA.4) ad (IA.6) cocde ad he d ereal equao (38) us herefore hold. IA.4. Populao dyacs he geeral seg of eco. I hs appedx, I derve he dyacs of forao sharg for he geeral seg of eco. (Appedx IA.4). I he geeral seg of eco., he dyacs of a aager s uber of deas sasfy d = dn,, ( ; ) (IA.7) where (N ) deoes a Posso process wh esy ( ). These dyacs ply a cera cross-secoal dsrbuo, µ, of uber of deas,.e., he dsrbuo µ us sasfy a cera Kologorov Forward Equao (KFE), whch I derve usg he resul forulaed Lea IA.4.. Lea IA.4.. Defe he expecao g E[f( )] = kn f(k)µ (k) (IA.8) 3
4 for a arbrary fuco f( ). The, he fuco g us sasfy he d ereal equao d d g = ()µ () f( ) (; ) N N ()µ ()f() N (IA.9) wh al codo g = P kn f(k) (k). Proof. Observe ha he geeraor of he process (IA.7) sasfes Af() = () N (; )(f( ) f()) (IA.) ad rewre he expecao (IA.8) as g = E[f( )] Z Z E[Af( s )]ds = E[f( )] Af()µ s ()ds. N (IA.) D ereag Eq. (IA.) wh respec o e ad rearragg yelds (IA.9). To oba he KFE (3), I he chage he suao order (IA.9). pecfcally, roduce he chage of varable k ad rewre he frs er (IA.9) as N N f( ) () (; )µ () = kn k = Pluggg (IA.) o (IA.9) ad rearragg, I oba N f() d d µ () = N f() = f(k) (k ) (; k )µ (k ). (IA.) ( ) (; )µ ( ) ()µ () Observg ha he fuco f( ) s arbrary, he KFE (3) follows.!. (IA.3) IA.5. Average rajecores of uber of deas I hs appedx, I derve a aager s average rajecory of uber of deas, whch I use for he a aalyss eco 5 (Appedx IA.5). I parcular, I oba he average rajecory of a aager s uber of deas codoal o aager holdg T = k deas a he horzo dae,.e., E T = k. Applyg Bayes rule, frs observe ha P = T = k = P[ = ]P[ T = k = ] P[ T = k] = µ () T (k; ) µ T (k) (IA.4) where s he probably ha aager ges k deas by he horzo dae codoal o holdg deas a e. To copue hs probably, apply he resul of Lea IA.4. o g s P N f() T s(; ) for s>wh al codo, g =, whchyelds: T (k; ) = {k } e (k )T e T k e (T ) {k } (IA.5) 4
5 uder dea sharg, whereas uder dea orgao: d d s(k; ) = s (k; ) s (k; ), (IA.6) wh al codo, (; ) = =,where = s a Drac ass a =. I he follows ha he average rajecory of aager s uber of deas s gve by E T = k = µ T (k) k µ () T (k; ), (IA.7) = whch has a closed-for soluo uder dea sharg: E T = k = et e k(t ) e k e T k e (T ). (IA.8) IA.6. Proof of Proposo 9 (dsrbuo shf relave o luck) I hs appedx I show ha he cross-secoal dsrbuo of sascs s shfed o he lef whe he dsrbuo of uber of deas s syerc or sasfes Corollary. I sar by copug he probably ha a aager s sasc s egave: P[, apple ] = = Z l (x) N µ ()erf xp R ()! dx (IA.9) p! µ ()a E[R p ]. (IA.3) k N Provg ha Eq. (43) holds s hus equvale o provg: µ ()a p ' <, (IA.3) N where ' > s posve a all fe es. Assug he fucoal for for µ Corollary, I sar by boudg a ( ) Eq. (IA.3) by a pecewse lear fuco: a ' p apple appleb c a (' ( )) >b c p, o g(), N, (IA.3) where he frs er explos ha he suppor s bouded a ad he secod er explos boh ha he fuco s cocave for all ad hus bouded above by s frs-order Taylor expaso ad ha a ( ) s bouded above a /. Alhough hs boud ca be gheed, s su ce o prove Eq. (IA.3) ad sple eough o copue explc expressos: rewrg µ Eq. (7) as µ () = ( ), (IA.33) 5
6 ad akg expecaos over he fuco g( ) Eq. (IA.3) yelds he explc boud: < b c ' b c p p ' l p p a (' ( ' p ' )) ' ( ) p (b c) C A. (IA.34) By assupo, >, whch ples ha > ad hus he er sde he bracke s oposve, fro whch he equaly Eq. (IA.3) follows. Noe ha he resul also obas whe µ s srcly syerc a all es. ce he fuco a ( ) Eq. (IA.3) s odd ad egave for all appleb c, I defe a ew fuco f :[b c, b c]! R by reflecg a ( ) Eq. (IA.3) frs horzoally abou zero ad he vercally abou o oba: f() = a (b ' c ), = b c,...,b c. (IA.35) p b c The cocavy of he skll-o-luck rao he ples ha f() > a ' p, = b c,...,b c. (IA.36) Usg hs resul I oba a src boud for Eq. (IA.3): < [,b c][(b c,...,) µ ()a p ' a all fe es. yery furher ples ha [,b c] µ ()a p ' ad ha µ has zero ass beyod wce s ea: (b c,b c] (b c,b c] µ ()f(), (IA.37) µ ()f() = (IA.38) µ () =, b c, (IA.39) sce s syerc ad s suppor s bouded fro he lef a. Hece, he syerc case Eq. (IA.37) drecly leads o Eq. (IA.3). IA.7. Deals o copuaos eco 6 (fud flows ad fees) I hs appedx I dscuss copuaoal deals relaed o he equlbru soluo he presece of fud flows ad fees. For he sake of brevy, I sply ppo resuls ha d er fro he basele odel. Noe frs ha flows ad fees do o a ec he resuls of Proposo. However, hey odfy porfolo sraeges. To see how, I go over he a seps of Appedx B he presece of fees ad 6
7 flows. Maxzg aager s expeced uly over copesao Eq. (48) ples: ax E exp f(( )W T B T ) F. (IA.4) The soluo o hs axzao proble s T (,,T )= f( ) (o T ()) > T,T,T T b T. (IA.4) whch subsued he value fuco yelds he boudary codo J(W, B,,,T )= exp f(( )W B) (o T ()) > T. (IA.4) The proble has a addoal sae varable, B, whch applyg Io s lea o Eq. (5) sasfes: db = b c, dp = dp! > dp. (IA.43), The assocaed HJB equao ow sasfes = ax J W A Q J WWBQ( ) B Q B ( ) > J W J WB BQ! > J J > A r(j B ( )B ( ) > )J B ( ( )E L( b Y, ) h J(W,B, ) >! > A Q J BBB Q( ) >! >! (, ) b Y,,) J(W,B, The frs-order codo he yelds he followg porfolo polcy: B Q! > (IA.44) B ( ) > J B (IA.45),,). (IA.46) (, )= J W A Q B Q B ( ) > J W J WB B Q!> J WW B Q. (IA.47) ubsug back he HJB equao, edous dervaos show he asaz of Theore B. becoes: J(W, B,,,)= exp f(( )W B) u () > R ()R () > > M (), (IA.48) where R ad M sasfy he syse of equaos Theore B.. The equao for u d ers, bu s rreleva for porfolo sraeges ad s hus oed. As a resul, he soluo for R ad M s decal o Lea B.3. ubsug hese expressos he opal polcy above yelds Eq. (5). To oba prce coe ces I ow go over he a seps Appedx C. Aggregag frs porfolos a he horzo dae: Z T d = N µ T () f (o T ()) > T T () T! T T =( )? T, (IA.49) 7
8 whch yelds he boudary codos,t = o c T T ( )( T o c T ) ad,t = f o c T ( T o c T ). (IA.5) larly, aggregag porfolos a dae, Eq. (84) becoes: N µ () A Q, B Q, (o ()) B,() > ()! =,, f( )B Q,, (IA.5) sce! >! =, whch yelds,, =, ad hus, f( ) o c = f( ) Z f( )! d ds s ds. (IA.5) Fally, spellg ou he secod equao of he syse Eq. (9) becoes d d, = k, B Q, BQ,, o c he soluo of whch s Eq. (5). Furherore, gog hrough he seps of Appedx D ad splfyg yelds b ( ) o c = f ( )( oc f o c ) ( ) k (f ( ) k o c ) ( ( )(f k ) k o c ) f ( ) o c f, (IA.53) p f ( ) (IA.54) (IA.55) a, ( ) a, ( ) a, ( ), (IA.56) where he secod le correspods o he fuco H( ) Eq. (53). To copue foraoal alphas, I subsue he equlbru soluo Eq. (8) o oba ad A Q, = k o c (f 4 ( )k o c ( oc )) ( )( oc )! fo c 4 k o c (k ( ) f) ( oc ) (IA.57) b, ( ) b, ( ), (IA.58) B Q, = oc ( )(k f ) k o c ( )( o c ). (IA.59) Wh hese expressos ca he copue sascs by sulaos usg Eq. (). Refereces Karazas, I., hreve,., 988. Browa Moo ad ochasc Calculus. prger, New York, Graduae Texs Maheacs. 8
9 Lpser, R.., hryaev, A. N.,. ascs of Rado Processes I. prger Verlag, New York. Lpser, R.., hryaev, A. N.,. ascs of Rado Processes II. prger Verlag, New York. 9
Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse
P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc
More informationSolution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations
Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare
More information14. Poisson Processes
4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationDelay-Dependent Robust Asymptotically Stable for Linear Time Variant Systems
Delay-Depede Robus Asypocally Sable for Lear e Vara Syses D. Behard, Y. Ordoha, S. Sedagha ABSRAC I hs paper, he proble of delay depede robus asypocally sable for ucera lear e-vara syse wh ulple delays
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More informationA Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition
SSN 76-7659 Eglad K Joural of forao ad Copug Scece Vol 7 No 3 pp 63-7 A Secod Kd Chebyshev olyoal Approach for he Wave Equao Subec o a egral Coservao Codo Soayeh Nea ad Yadollah rdokha Depare of aheacs
More informationProbability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract
Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.
More information4. THE DENSITY MATRIX
4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More informationAnalysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays
Ieraoal Coferece o Appled Maheac Sulao ad Modellg (AMSM 6) Aaly of a Sochac Loa-Volerra Copeve Sye wh Drbued Delay Xagu Da ad Xaou L School of Maheacal Scece of Togre Uvery Togre 5543 PR Cha Correpodg
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationAs evident from the full-sample-model, we continue to assume that individual errors are identically and
Maxmum Lkelhood smao Greee Ch.4; App. R scrp modsa, modsb If we feel safe makg assumpos o he sascal dsrbuo of he error erm, Maxmum Lkelhood smao (ML) s a aracve alerave o Leas Squares for lear regresso
More informationBrownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus
Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales
More informationQR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA
QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.
More informationFor the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.
The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe
More informationKey words: Fractional difference equation, oscillatory solutions,
OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg
More informationFORCED VIBRATION of MDOF SYSTEMS
FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me
More informationSome Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables
Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3
More informationFALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.
Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50
More informationClassificationofNonOscillatorySolutionsofNonlinearNeutralDelayImpulsiveDifferentialEquations
Global Joural of Scece Froer Research: F Maheacs ad Decso Sceces Volue 8 Issue Verso. Year 8 Type: Double Bld Peer Revewed Ieraoal Research Joural Publsher: Global Jourals Ole ISSN: 49-466 & Pr ISSN: 975-5896
More informationQuantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)
Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo
More informationThe Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting
Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad
More informationCyclone. Anti-cyclone
Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme
More informationProbability Bracket Notation, Probability Vectors, Markov Chains and Stochastic Processes. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA, USA
Probably Bracke Noao, Probably Vecors, Markov Chas ad Sochasc Processes Xg M. Wag Sherma Vsual Lab, Suyvale, CA, USA Table of Coes Absrac page1 1. Iroduco page. PBN ad Tme-depede Dscree Radom Varable.1.
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More informationAML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending
CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral
More informationLaplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.
Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o
More informationMoments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables
Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.
More informationSome probability inequalities for multivariate gamma and normal distributions. Abstract
-- Soe probably equales for ulvarae gaa ad oral dsrbuos Thoas oye Uversy of appled sceces Bge, Berlsrasse 9, D-554 Bge, Geray, e-al: hoas.roye@-ole.de Absrac The Gaussa correlao equaly for ulvarae zero-ea
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationInternational Journal of Scientific & Engineering Research, Volume 3, Issue 10, October ISSN
Ieraoal Joural of cefc & Egeerg Research, Volue, Issue 0, Ocober-0 The eady-ae oluo Of eral hael Wh Feedback Ad Reegg oeced Wh o-eral Queug Processes Wh Reegg Ad Balkg ayabr gh* ad Dr a gh** *Assoc Prof
More informationSolving Non-Linear Rational Expectations Models: Approximations based on Taylor Expansions
Work progress Solvg No-Lear Raoal Expecaos Models: Approxmaos based o Taylor Expasos Rober Kollma (*) Deparme of Ecoomcs, Uversy of Pars XII 6, Av. du Gééral de Gaulle; F-94 Créel Cedex; Frace rober_kollma@yahoo.com;
More informationAbstract. Lorella Fatone 1, Francesca Mariani 2, Maria Cristina Recchioni 3, Francesco Zirilli 4
Joural of Appled Maheacs ad Physcs 4 54-568 Publshed Ole Jue 4 cres. hp://www.scrp.org/joural/jap hp://dx.do.org/.436/jap.4.76 The ABR Model: Explc Forulae of he Moes of he Forward Prces/Raes Varable ad
More informationVARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,
Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs
More informationThe Properties of Probability of Normal Chain
I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co
More informationThe textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.
ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce
More informationMixed Integral Equation of Contact Problem in Position and Time
Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs
More informationPartial Molar Properties of solutions
Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a
More informationThe textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.
coomcs 435 Meze. Ch Fall 07 Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he ffce Markes Hypohess The rese Value Model Approach o Asse rcg The exbook expresses he sock prce as he prese dscoued
More informationReal-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF
EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae
More informationMarch 14, Title: Change of Measures for Frequency and Severity. Farrokh Guiahi, Ph.D., FCAS, ASA
March 4, 009 Tle: Chage of Measures for Frequecy ad Severy Farroh Guah, Ph.D., FCAS, ASA Assocae Professor Deare of IT/QM Zarb School of Busess Hofsra Uversy Hesead, Y 549 Eal: Farroh.Guah@hofsra.edu Phoe:
More informationBianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity
Ieraoal Joural of Mahemacs esearch. IN 0976-50 Volume 6, Number (0), pp. 6-7 Ieraoal esearch Publcao House hp://www.rphouse.com Bach ype II ff Flud led Cosmologcal Model Geeral elay B. L. Meea Deparme
More informationθ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:
Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log
More informationOptimal Control and Hamiltonian System
Pure ad Appled Maheacs Joural 206; 5(3: 77-8 hp://www.scecepublshggroup.co//pa do: 0.648/.pa.2060503.3 ISSN: 2326-9790 (Pr; ISSN: 2326-982 (Ole Opal Corol ad Haloa Syse Esoh Shedrack Massawe Depare of
More informationLinear Regression Linear Regression with Shrinkage
Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso
More informationChapter 8. Simple Linear Regression
Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple
More informationMidterm Exam. Tuesday, September hour, 15 minutes
Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.
More informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More information2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission
/0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power
More informationFully Fuzzy Linear Systems Solving Using MOLP
World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,
More informationFault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview
Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos
More informationA Parametric Kernel Function Yielding the Best Known Iteration Bound of Interior-Point Methods for Semidefinite Optimization
Aerca Joural of Appled Maheacs 6; 4(6): 36-33 hp://wwwscecepublshggroupco/j/aja do: 648/jaja6468 ISSN: 33-43 (Pr); ISSN: 33-6X (Ole) A Paraerc Kerel Fuco Yeldg he Bes Kow Ierao Boud of Ieror-Po Mehods
More informationFresnel Equations cont.
Lecure 12 Chaper 4 Fresel quaos co. Toal eral refleco ad evaesce waves Opcal properes of meals Laer: Famlar aspecs of he eraco of lgh ad maer Fresel quaos r 2 Usg Sell s law, we ca re-wre: r s s r a a
More informationDensity estimation III. Linear regression.
Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg
More informationInterval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X
ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce
More informationThe ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.
C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)
More informationStrong Convergence Rates of Wavelet Estimators in Semiparametric Regression Models with Censored Data*
8 The Ope ppled Maheacs Joural 008 8-3 Srog Covergece Raes of Wavele Esaors Separaerc Regresso Models wh Cesored Daa Hogchag Hu School of Maheacs ad Sascs Hube Noral Uversy Huagsh 43500 Cha bsrac: The
More informationDensity estimation III.
Lecure 4 esy esmao III. Mlos Hauskrec mlos@cs..edu 539 Seo Square Oule Oule: esy esmao: Mamum lkelood ML Bayesa arameer esmaes MP Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Eoeal
More informationEfficient Estimators for Population Variance using Auxiliary Information
Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav
More informationEE 6885 Statistical Pattern Recognition
EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,
More informationAsymptotic Regional Boundary Observer in Distributed Parameter Systems via Sensors Structures
Sesors,, 37-5 sesors ISSN 44-8 by MDPI hp://www.mdp.e/sesors Asympoc Regoal Boudary Observer Dsrbued Parameer Sysems va Sesors Srucures Raheam Al-Saphory Sysems Theory Laboraory, Uversy of Perpga, 5, aveue
More informationDensity estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square
Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc
More informationChapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)
Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were
More informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationA Consecutive Quasilinearization Method for the Optimal Boundary Control of Semilinear Parabolic Equations
Appled Maheacs 4 5 69-76 Publshed Ole March 4 ScRes hp://wwwscrporg/joural/a hp://dxdoorg/436/a45467 A Cosecuve Quaslearzao Mehod for he Opal Boudar Corol of Selear Parabolc Equaos Mohaad Dehgha aer *
More informationOptimal Tracking Control Design of Quantum Systems via Tensor Formal Power Series Method
5 he Ope Auoao ad Corol Syse Joural, 8,, 5-64 Ope Access Opal racg Corol Desg of Quau Syses va esor Foral Power Seres Mehod Bor-Se Che, *, We-Hao Che,, Fa Hsu ad Weha Zhag 3 Depare of Elecrcal Egeerg,
More informationSome Different Perspectives on Linear Least Squares
Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,
More informationNUMERICAL EVALUATION of DYNAMIC RESPONSE
NUMERICAL EVALUATION of YNAMIC RESPONSE Aalycal solo of he eqao of oo for a sgle degree of freedo syse s sally o ossble f he excao aled force or grod accelerao ü g -vares arbrarly h e or f he syse s olear.
More information1 Introduction and main results
The Spacgs of Record Values Tefeg Jag ad Dag L 2 Ocober 0, 2006 Absrac. Le {R ; } be he sequece of record values geeraed from a sequece of..d. couous radom varables, wh dsrbuo from he expoeal famly. I
More informationOther Topics in Kernel Method Statistical Inference with Reproducing Kernel Hilbert Space
Oher Topcs Kerel Mehod Sascal Iferece wh Reproducg Kerel Hlber Space Kej Fukumzu Isue of Sascal Mahemacs, ROIS Deparme of Sascal Scece, Graduae Uversy for Advaced Sudes Sepember 6, 008 / Sascal Learg Theory
More informationThe algebraic immunity of a class of correlation immune H Boolean functions
Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales
More informationSensors and Regional Gradient Observability of Hyperbolic Systems
Iellge Corol ad Auoao 3 78-89 hp://dxdoorg/436/ca3 Publshed Ole February (hp://wwwscrporg/oural/ca) Sesors ad Regoal Grade Observably of Hyperbolc Syses Sar Behadd Soraya Reab El Hassae Zerr Maheacs Depare
More informationThe Solutions of Initial Value Problems for Nonlinear Fourth-Order Impulsive Integro-Differential Equations in Banach Spaces
WSEAS TRANSACTIONS o MATHEMATICS Zhag Lglg Y Jgy Lu Juguo The Soluos of Ial Value Pobles fo Nolea Fouh-Ode Ipulsve Iego-Dffeeal Equaos Baach Spaces Zhag Lglg Y Jgy Lu Juguo Depae of aheacs of Ta Yua Uvesy
More informationSolution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.
ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh
More informationRandom Generalized Bi-linear Mixed Variational-like Inequality for Random Fuzzy Mappings Hongxia Dai
Ro Geeralzed B-lear Mxed Varaoal-lke Iequaly for Ro Fuzzy Mappgs Hogxa Da Depare of Ecooc Maheacs Souhweser Uversy of Face Ecoocs Chegdu 674 P.R.Cha Absrac I h paper we roduce sudy a ew class of ro geeralzed
More informationSolving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision
Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp
More informationANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2
Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads
More informationLinear Minimum Variance Unbiased Estimation of Individual and Population slopes in the presence of Informative Right Censoring
Ieraoal Joural of Scefc ad Research Pulcaos Volue 4 Issue Ocoer 4 ISSN 5-353 Lear Mu Varace Uased Esao of Idvdual ad Populao slopes he presece of Iforave Rgh Cesorg VswaahaN * RavaaR ** * Depare of Sascs
More informationUpper Bound For Matrix Operators On Some Sequence Spaces
Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah
More informationChapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I
CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao
More informationSolution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs
Aalable a hp://paed/aa Appl Appl Mah ISS: 9-9466 Vol 0 Isse (Deceber 0) pp 04- Applcaos ad Appled Maheacs: A Ieraoal Joral (AAM) Solo o Soe Ope Probles o E-sper Verex Magc Toal Labelg o Graphs G Marh MS
More informationInterval Regression Analysis with Reduced Support Vector Machine
Ieraoal DSI / Asa ad Pacfc DSI 007 Full Paper (July, 007) Ierval Regresso Aalyss wh Reduced Suppor Vecor Mache Cha-Hu Huag,), Ha-Yg ao ) ) Isue of Iforao Maagee, Naoal Chao Tug Uversy (leohkko@yahoo.co.w)
More informationSuppose we have observed values t 1, t 2, t n of a random variable T.
Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).
More informationSynopsis of Various Rates of Return
Syopss of Varous Raes of Reur (Noe: Much of hs s ake from Cuhberso) I he world of face here are may dffere ypes of asses. Whe aalysg hese, a ecoomc sese, we aemp o characerse hem by reducg hem o some of
More informationIntegral Φ0-Stability of Impulsive Differential Equations
Ope Joural of Appled Sceces, 5, 5, 65-66 Publsed Ole Ocober 5 ScRes p://wwwscrporg/joural/ojapps p://ddoorg/46/ojapps5564 Iegral Φ-Sably of Impulsve Dffereal Equaos Aju Sood, Sajay K Srvasava Appled Sceces
More informationOPTIMALITY AND SECOND ORDER DUALITY FOR A CLASS OF QUASI-DIFFERENTIABLE MULTIOBJECTIVE OPTIMIZATION PROBLEM
Yugoslav Joural of Oeraos Research 3 (3) Nuber, -35 DOI:.98/YJOR394S OPTIMALITY AND SECOND ORDER DUALITY FOR A CLASS OF QUASI-DIFFERENTIABLE MULTIOBJECTIVE OPTIMIZATION PROBLEM Rsh R. SAHAY Deare of Oeraoal
More informationContinuous Indexed Variable Systems
Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh
More informationDebabrata Dey and Atanu Lahiri
RESEARCH ARTICLE QUALITY COMPETITION AND MARKET SEGMENTATION IN THE SECURITY SOFTWARE MARKET Debabrata Dey ad Atau Lahr Mchael G. Foster School of Busess, Uersty of Washgto, Seattle, Seattle, WA 9895 U.S.A.
More information4 5 = So 2. No, as = ± and invariant factor 6. Solution 3 Each of (1, 0),(1, 2),(0, 2) has order 2 and generates a C
Soluos (page 7) ρ 5 4 4 Soluo = ρ as 4 4 5 = So ρ, ρ s a Z bass of Z ad 5 4 ( m, m ) = (,7) = (,9) No, as 5 7 4 5 6 = ± Soluo The 6 elemes of Z K are: g = K + e + e, g = K + e, g = K + e, 4g = K + e, 5g
More informationNumerical approximatons for solving partial differentıal equations with variable coefficients
Appled ad Copuaoal Maheacs ; () : 9- Publshed ole Februar (hp://www.scecepublshggroup.co/j/ac) do:.648/j.ac.. Nuercal approaos for solvg paral dffereıal equaos wh varable coeffces Ves TURUT Depare of Maheacs
More informationNilpotent Elements in Skew Polynomial Rings
Joural of Scece, Ilac epublc of Ira 8(): 59-74 (07) Uvery of Tehra, ISSN 06-04 hp://cece.u.ac.r Nlpoe Elee Sew Polyoal g M. Az ad A. Mouav * Depare of Pure Maheac, Faculy of Maheacal Scece, Tarba Modare
More informationSTOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION
The Bk of Thld Fcl Isuos Polcy Group Que Models & Fcl Egeerg Tem Fcl Mhemcs Foudo Noe 8 STOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION. ก Through he use of ordry d/or prl deres, ODE/PDE c rele
More informationASYMPTOTIC EQUIVALENCE OF NONPARAMETRIC REGRESSION AND WHITE NOISE. BY LAWRENCE D. BROWN 1 AND MARK G. LOW 2 University of Pennsylvania
The Aals of Sascs 996, Vol., No. 6, 38398 ASYMPTOTIC EQUIVALENCE OF NONPARAMETRIC REGRESSION AND WITE NOISE BY LAWRENCE D. BROWN AND MARK G. LOW Uversy of Pesylvaa The prcpal resul s ha, uder codos, o
More informationIntegral Equations and their Relationship to Differential Equations with Initial Conditions
Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp
More informationBounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy
Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled
More informationFundamentals of Speech Recognition Suggested Project The Hidden Markov Model
. Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces
More informationSome results and conjectures about recurrence relations for certain sequences of binomial sums.
Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have
More informationInternational Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.
www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra
More information