Almost unbiased exponential estimator for the finite population mean

Size: px
Start display at page:

Download "Almost unbiased exponential estimator for the finite population mean"

Transcription

1 Almos ubasd poal smaor for f populao ma Rajs Sg, Pakaj aua, ad rmala Saa, Scool of Sascs, DAVV, Idor (M.P., Ida (rsgsa@aoo.com Flor Smaradac ar of Dparm of Mamacs, Uvrs of Mco, Gallup, USA (smarad@um.du Absrac I s papr av proposd a almos ubasd rao ad produc p poal smaor for f populao ma. I as b so a Bal ad Tuja (99 rao ad produc p poal smaors ar parcular mmbrs of proposd smaor. Emprcal sud s carrd o dmosra supror of proposd smaor. Kords: Aular formao, bas, ma-squard rror, poal smaor.. Iroduco I s ll ko a us of aular formao sampl survs rsuls subsaal mprovm prcso of smaors of populao ma. Rao, produc ad dffrc mods of smao ar good ampls s co. Rao mod of smao s qu ffcv r s a g posv corrlao b sud ad aular varabls. O or ad, f s corrlao s gav (g, produc mod of smao ca b mplod ffcvl. osdr a f populao us ( U, U,..., U for ac of c formao s avalabl o aular varabl. L a sampl of sz b dra

2 smpl radom samplg ou rplacm (SRSWOR o sma populao ma of caracr udr sud. L (, b sampl ma smaor of (, X populao mas of ad rspcvl. I ordr o av a surv sma of populao ma of sud caracr, assumg koldg of populao ma X of aular caracr, Bal ad Tuja (99 suggsd rao ad produc p poal smaor X p (. X X p (. X Up o frs ordr of appromao, bas ad ma-squard rror (MSE of ad ar rspcvl gv b B( K (.3 MSE ( K (.4 4 B( K (.5 MSE ( K (.6 4 r S, ( ( S, ( ( X S, S X, K ρ, S ρ, ( S S S ( ( ( X.

3 From (.3 ad (.5, s a smaors ad suggsd b Bal ad Tuja (99 ar basd smaor. I som applcaos bas s dsadvaagous. Follog Sg ad Sg (993 ad Sg ad Sg (6 av proposd almos ubasd smaors of.. Almos ubasd smaor Suppos X X, p, p X X suc a,, H, r H dos s of all possbl smaors for smag populao ma. B dfo, s H s a lar var f H (. for, R (. r (,, dos sascal cosas ad R dos s of ral umbrs. To oba bas ad MSE of, r (, X( suc a. E ( E (. E(, E(., E( ρ Eprssg rms of s, av ( p p (.3

4 Epadg rg ad sd of (.3 ad rag rms up o scod pors of s, av ( 8 8 (.4 Takg pcaos of bo sds of (.4 ad subracg from bo sds, g bas of smaor, up o frs ordr of appromao as ( ( K 4 ( B (.5 From (.4, av ( (.6 r -. (.7 Squarg bo sds of (.7 ad akg pcaos, g MSE of smaor, up o frs ordr of appromao, as K 4 MSE( (.8 c s mmum K. (.9 Pug s valu of K (. av opmum valu of smaor as (opmum. Tus mmum MSE of s gv b (.MSE( m ρ (. c s sam as a of radoal lar rgrsso smaor. From (.7 ad (.9, av

5 - K. (. From (. ad (., av ol o quaos r ukos. I s o possbl o fd uqu valus for s,,,. I ordr o g uqu valus of s, sall mpos lar rsrco B(. (. r B( dos bas smaor. Equaos (., (. ad (. ca b r mar form as B( B( K (.3 Usg (.3, g uqu valus of s(,, as 4K K K K K (.4 Us of s s (,, rmov bas up o rms of ordr o( - a (.. 3. To pas samplg W populao ma X of s o ko, s of smad from a prlmar larg sampl o c ol aular caracrsc s obsrvd. T valu of populao ma X of aular caracr s rplacd b s sma. Ts cqu s ko as doubl samplg or o-pas samplg. T o-pas samplg apps o b a porful ad cos ffcv (coomcal procdur for fdg rlabl sma frs pas sampl for uko

6 paramrs of aular varabl ad c as m rol o pla surv samplg, for sac, s; Hdroglou ad Sardal (998. W X s uko, s somms smad from a prlmar larg sampl of sz o c ol caracrsc s masurd. T a scod pas sampl of sz ( < s dra o c bo ad caracrscs ar masurd. L do sampl ma of basd o frs pas sampl of sz ; ad scod pas of sz. b sampl mas of ad rspcvl basd o I doubl (or o-pas samplg, suggs follog modfd poal rao ad produc smaors for, rspcvl, as p (3. d p (3. d To oba bas ad MSE of d ad d, r suc a ad (, X(, X( ( E( E( E E (, f E (, f E (, f E( fρ,

7 E(, f ρ E (. f r f, f. Follog sadard procdur oba B ( d f 3 ρ 8 (3.3 B ( d f 3 ρ 8 (3.4 MSE ( ρ d f f 3 4 (3.5 MSE ( ρ d f f 3 4 (3.6 r f 3. From (3.3 ad (3.4 obsrv a proposd smaors d ad d ar basd, c s a draback of a smaor s som applcaos. 4. Almos ubasd o-pas smaor Suppos, d ad d as dfd (3. ad (3. suc a, d, d W, r W dos s of all possbl smaors for smag populao ma. B dfo, s W s a lar var f W W. (4.

8 for, R. (4. r (,, dos sascal cosas ad R dos s of ral umbrs. To oba bas ad MSE of, usg oaos of sco 3 ad prssg rms of s, av ( p p (4.3 ( ( ( [ ( ] (4.4 r. (4.5 Takg pcaos of bo sds of (4.4 ad subracg from bo sds, g bas of smaor, up o frs ordr f appromao as ρ 3 8 f ( Bas (4.6 From (4.4, av ( (4.7 Squarg bo sds of (4.7 ad akg pcao, g MSE of smaor, up o frs ordr of appromao, as

9 MSE ( f f K c s mmum 3 (4.8 4 K. (4.9 Tus mmum MSE of s gv b m.mse( [ f f ρ ] 3 (4. c s sam as a of o-pas lar rgrsso smaor. From (4.5 ad (4.9, av K (4. From (4. ad (4., av ol o quaos r ukos. I s o possbl o fd uqu valus for 's(,, 's, sall mpos lar rsrco. I ordr o g uqu valus of B( d (4. r B ( d dos bas smaor. Equaos (4., (4. ad (4. ca b r mar form as B( d B( d K (4.3 Solvg (4.3, g uqu valus of 's(,, as 8K K 4K K 4K (4.4

10 Us of s 's(,, 5. Emprcal sud rmovs bas up o rms of ordr ( o a (4.. T daa for mprcal sud ar ak from o aural populao daa ss cosdrd b ocra (977 ad Rao (983. Populao I: ocra ( ,.445, ρ Populao II: Rao (983.46,.8, ρ I abl (5., valus of scalar s (,, ar lsd. Tabl (5.: Valus of s (,, Scalars Populao I II Usg s valus of s (,, gv abl 5., o ca rduc bas o ordr o ( - smaor a (.. I abl 5., Prc rlav ffcc (PRE of,, ad ( opmum cas ar compud rspc o. Tabl 5.: PRE of dffr smaors of rspc o.

11 Esmaors PRE (., Populao I Populao II (opmum Tabl 5. clarl sos a suggsd smaor s opmum codo s br a usual ubasd smaor, Bal ad Tuja (99 smaors ad. For purpos of llusrao for o-pas samplg, cosdr follog populaos: Populao III: Mur (967 : Oupu : umbr of orkrs.354,. 9484, ρ. 95, 8,, 8. Populao IV: Sl ad Torr(96.483,. 7493, ρ. 4996, 3,, 4. I abl 5.3 valus of scalars 's(,, Tabl 5.3: Valus of 's(,, ar lsd. Scalars Populao I Populao II

12 o ordr ( Usg s valus of 's(,, o smaor a 5.3. gv abl 5.3 o ca rduc bas I abl 5.4 prc rlav ffcc (PRE of, d, d ad ( opmum cas ar compud rspc o. Tabl 5.4: PRE of dffr smaors of rspc o. Esmaors PRE (., Populao I Populao II d d Rfrcs Bal, S. ad Tuja, R.K. (99: Rao ad produc p poal smaor. Iformao ad opmzao sccs, (, ocra (977:

Almost Unbiased Exponential Estimator for the Finite Population Mean

Almost Unbiased Exponential Estimator for the Finite Population Mean Rajs Sg, Pakaj aua, rmala Saa Scool of Sascs, DAVV, Idor (M.P., Ida Flor Smaradac Uvrs of Mco, USA Almos Ubasd Epoal Esmaor for F Populao Ma Publsd : Rajs Sg, Pakaj aua, rmala Saa, Flor Smaradac (Edors

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug

More information

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS 3. INTRODUCTION Th Ivrs Expoal dsrbuo was roducd by Kllr ad Kamah (98) ad has b sudd ad dscussd as a lfm modl. If a radom varabl

More information

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23 BIO53 Bosascs Lcur 04: Cral Lm Thorm ad Thr Dsrbuos Drvd from h Normal Dsrbuo Dr. Juchao a Cr of Bophyscs ad Compuaoal Bology Fall 06 906 3 Iroduco I hs lcur w wll alk abou ma cocps as lsd blow, pcd valu

More information

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao

More information

EMPIRICAL STUDY IN FINITE CORRELATION COEFFICIENT IN TWO PHASE ESTIMATION

EMPIRICAL STUDY IN FINITE CORRELATION COEFFICIENT IN TWO PHASE ESTIMATION MPIRIAL TDY I FIIT ORRLATIO OFFIIT I TWO PHA TIMATIO M. Khohva Lcurr Grffh vry chool of Accoug ad Fac Aurala. F. Kaymarm Aa Profor Maachu Iu of Tchology Dparm of Mchacal grg A; currly a harf vry Thra Ira.

More information

IMPUTATION USING REGRESSION ESTIMATORS FOR ESTIMATING POPULATION MEAN IN TWO-PHASE SAMPLING

IMPUTATION USING REGRESSION ESTIMATORS FOR ESTIMATING POPULATION MEAN IN TWO-PHASE SAMPLING Joural of Rlal ad asal uds; I (Pr: 097-80, (Ol:9- ol., Issu (0: - IPUAIO UIG RGRIO IAOR FOR IAIG POPUAIO A I WO-PHA APIG ardra gh hakur, Kalpaa adav ad harad Pahak r for ahmaal s (, Baashal Uvrs, Rajasha,

More information

Algorithms to Solve Singularly Perturbed Volterra Integral Equations

Algorithms to Solve Singularly Perturbed Volterra Integral Equations Avalabl a hp://pvamudu/aam Appl Appl Mah ISSN: 9-9 Vol Issu Ju pp 9-8 Prvousl Vol Issu pp Applcaos ad Appld Mahmacs: A Iraoal Joural AAM Algorhms o Solv Sgularl Prurbd Volrra Igral Equaos Marwa Tasr Alqura

More information

An Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys

An Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys "cece as True Here" Joural of Mahemacs ad ascal cece, Volume 06, 78-88 cece gpos Publshg A Effce Dual o Rao ad Produc Esmaor of Populao Varace ample urves ubhash Kumar Yadav Deparme of Mahemacs ad ascs

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient 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 information

Improvement of the Reliability of a Series-Parallel System Subject to Modified Weibull Distribution with Fuzzy Parameters

Improvement of the Reliability of a Series-Parallel System Subject to Modified Weibull Distribution with Fuzzy Parameters Joural of Mahmacs ad Sascs Rsarch Arcls Improvm of h Rlably of a Srs-Paralll Sysm Subjc o Modfd Wbull Dsrbuo wh Fuzzy Paramrs Nama Salah Youssf Tmraz Mahmacs Dparm, Faculy of Scc, Taa Uvrsy, Taa, Egyp

More information

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals MECE 330 MECE 330 Masurms & Isrumao Sac ad Damc Characrscs of Sgals Dr. Isaac Chouapall Dparm of Mchacal Egrg Uvrs of Txas Pa Amrca MECE 330 Sgal Cocps A sgal s h phscal formao abou a masurd varabl bg

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Las squars ad moo uo Vascoclos ECE Dparm UCSD Pla for oda oda w wll dscuss moo smao hs s rsg wo was moo s vr usful as a cu for rcogo sgmao comprsso c. s a gra ampl of las squars problm w wll also wrap

More information

IMPROVED RATIO AND PRODUCT TYPE ESTIMATORS OF FINITE POPULATION MEAN IN SIMPLE RANDOM SAMPLING

IMPROVED RATIO AND PRODUCT TYPE ESTIMATORS OF FINITE POPULATION MEAN IN SIMPLE RANDOM SAMPLING REVISTA IVESTIGAIO OPERAIOAL VOL. 6, O., 7-76, 6 IMPROVED RATIO AD PRODUT TPE ESTIMATORS OF FIITE POPULATIO MEA I SIMPLE RADOM SAMPLIG Gajndra K. Vshwaarma, Ravndra Sngh, P.. Gupa, Sarla Par Dparmn of

More information

Inference on Curved Poisson Distribution Using its Statistical Curvature

Inference on Curved Poisson Distribution Using its Statistical Curvature Rsarch Joural of Mahacal ad Sascal Sccs ISSN 3 647 ol. 5 6-6 Ju 3 Rs. J. Mahacal ad Sascal Sc. Ifrc o Curvd Posso Dsrbuo Usg s Sascal Curvaur Absrac Sal Babulal ad Sadhu Sachaya Dpar of sascs Th Uvrsy

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces Srs of Nw Iforao Dvrgcs, Proprs ad Corrspodg Srs of Mrc Spacs K.C.Ja, Praphull Chhabra Profssor, Dpar of Mahacs, Malavya Naoal Isu of Tchology, Japur (Rajasha), Ida Ph.d Scholar, Dpar of Mahacs, Malavya

More information

Variable Satellite Usage in GPS Receivers

Variable Satellite Usage in GPS Receivers Procdgs of h orld Cogrss o grg ad Compur Scc 00 Vol I CCS 00, Ocobr 0-, 00, Sa Fracsco, USA Varabl Sall Usag GPS Rcvrs L Dg, Hyosop L, Ha Yu, Drrc Crwsy, X L, ad Crag C. Douglas, Mmbr, IANG Absrac Cosumr

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Extinction risk depends strongly on factors contributing to stochasticity

Extinction risk depends strongly on factors contributing to stochasticity co rs dpds srogly o facors corbug o sochascy r A. Mlbour & Ala Hasgs 2 parm of cology ad voluoary ology Uvrsy of Colorado ouldr CO 839 USA 2 parm of vromal Scc ad Polcy Uvrsy of Calfora avs CA 9566 USA

More information

Control Systems (Lecture note #6)

Control Systems (Lecture note #6) 6.5 Corol Sysms (Lcur o #6 Las Tm: Lar algbra rw Lar algbrac quaos soluos Paramrzao of all soluos Smlary rasformao: compao form Egalus ad gcors dagoal form bg pcur: o brach of h cours Vcor spacs marcs

More information

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,

More information

Mean Estimation with Imputation in Two- Phase Sampling

Mean Estimation with Imputation in Two- Phase Sampling Iaoal Joual of o gg sac (IJ) www.jm.com ol. Issu.5 p-oc. 0 pp-56-5 I: 4-6645 a smao w Impuao wo- Pas amplg aa g au Kalpaa aav aa Paa * fo amacal ccs () aasal Uvsaasal ajasa * pam of amacs a ascs. H.. Gou

More information

Survival Analysis for Randomized Clinical Trials II Cox Regression. Ziad Taib Biostatistics AstraZeneca February 26, 2008

Survival Analysis for Randomized Clinical Trials II Cox Regression. Ziad Taib Biostatistics AstraZeneca February 26, 2008 Survval alyss for Raomz Clcal rals II Cox Rgrsso a ab osascs sraca Fbruary 6, 8 la Irouco o proporoal azar mol H aral lkloo Comparg wo groups umrcal xampl Comparso w log-rak s mol xp z + + k k z Ursag

More information

Mixture Ratio Estimators Using Multi-Auxiliary Variables and Attributes for Two-Phase Sampling

Mixture Ratio Estimators Using Multi-Auxiliary Variables and Attributes for Two-Phase Sampling Opn Journal of Sascs 04 4 776-788 Publshd Onln Ocobr 04 n Scs hp://scrporg/ournal/os hp://ddoorg/0436/os0449073 Mur ao Esmaors Usng Mul-Aular Varabls and Arbus for To-Phas Samplng Paul Mang Waru John Kung

More information

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao

More information

ISSN No. (Print) :

ISSN No. (Print) : Iraoal Joural o Emrgg Tchologs (Scal Issu NCETST-07) 8(): 88-94(07) (Publshd by Rsarch Trd, Wbs: www.rsarchrd.) ISSN No. (Pr) : 0975-8364 ISSN No. (Ol) : 49-355 Comarso bw Baysa ad Mamum Lklhood Esmao

More information

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system 8. Quug sysms Cos 8. Quug sysms Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs lc8. S-38.45 Iroduco o Tlraffc Thory Srg 5 8. Quug sysms 8.

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Lecture 12: Introduction to nonlinear optics II.

Lecture 12: Introduction to nonlinear optics II. Lcur : Iroduco o olar opcs II r Kužl ropagao of srog opc sgals propr olar ffcs Scod ordr ffcs! Thr-wav mxg has machg codo! Scod harmoc grao! Sum frqucy grao! aramrc grao Thrd ordr ffcs! Four-wav mxg! Opcal

More information

Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences Ausrala Joural of Basc ad Appld Sccs 8 Spcal 4 Pags: 8-5 AENSI Jourals Ausrala Joural of Basc ad Appld Sccs ISSN:99-878 Joural hom pag:.aas.com Emprcal Mod Dcomposo for Rvr Flo Forcasg Shuhada Ismal ad

More information

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair Mor ppl Novmbr 8 N-Compo r Rparabl m h Rparma Dog Ohr ork a ror Rpar Jag Yag E-mal: jag_ag7@6om Xau Mg a uo hg ollag arb Normal Uvr Yaq ua Taoao ag uppor b h Fouao or h aural o b prov o Cha 5 uppor b h

More information

On the Existence and uniqueness for solution of system Fractional Differential Equations

On the Existence and uniqueness for solution of system Fractional Differential Equations OSR Jourl o Mhms OSR-JM SSN: 78-578. Volum 4 ssu 3 Nov. - D. PP -5 www.osrjourls.org O h Es d uquss or soluo o ssm rol Drl Equos Mh Ad Al-Wh Dprm o Appld S Uvrs o holog Bghdd- rq Asr: hs ppr w d horm o

More information

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore

More information

A NOVEL DIFFERENCE EQUATION REPRESENTATION FOR AUTOREGRESSIVE TIME SERIES

A NOVEL DIFFERENCE EQUATION REPRESENTATION FOR AUTOREGRESSIVE TIME SERIES Joural of Thorcal ad Appld Iformao Tchology h Spmbr 4. Vol. 67 No. 5-4 JATIT & LLS. All rghs rsrvd. ISSN: 99-8645 www.a.org E-ISSN: 87-395 A NOVEL DIFFERENCE EQUATION REPRESENTATION FOR AUTOREGRESSIVE

More information

STRUCTURAL FAULT DETECTION OF BRIDGES BASED ON LINEAR SYSTEM PARAMETER AND MTS METHOD

STRUCTURAL FAULT DETECTION OF BRIDGES BASED ON LINEAR SYSTEM PARAMETER AND MTS METHOD Joural of JSCE, Vol., 3-43, 03 STRUCTURAL FAULT DETECTION OF BRIDGES BASED ON LINEAR SYSTEM PARAMETER AND MTS METHOD Chul-Woo KIM, Ro ISEMOTO, Kuomo SUGIURA 3 ad Msuo KAWATANI 4 Mmbr of JSCE, Profssor,

More information

ε = R d ρ v ρ d ρ m CIVE322 BASIC HYDROLOGY I O = ds dt e s ( 1 T )] (T ) = 611exp[ L R v = = P + R 1 ΔS s + R g R 2 T s I E s

ε = R d ρ v ρ d ρ m CIVE322 BASIC HYDROLOGY I O = ds dt e s ( 1 T )] (T ) = 611exp[ L R v = = P + R 1 ΔS s + R g R 2 T s I E s CVE3 BSC HYDROOGY Hydrlgc Scc ad Egrg Cvl ad Evrmal Egrg Dparm Fr Clls, CO 853-37 Fall (97 49-76 Hydrlgc Budg Equas O ds d S ds d O d S ΔS s P + R R + R g E s s Saura vapr prssur s ( 6xp[ ( 73.5 ] ε.6

More information

SAMPLING STRATEGIES FOR FINITE POPULATION

SAMPLING STRATEGIES FOR FINITE POPULATION ajsh Sgh Flor Saradach (dors ajsh Sgh Flor Saradach (dors SMING STTEGIES FO FINITE OUTION USING UXIIY INFOMTION Th Educaoal ublshr olubus, 5 Salg Srags or F oulao Usg uxlar Iorao ajsh Sgh Flor Saradach

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Density estimation III. Linear regression.

Density 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 information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Delay-Dependent State Estimation for Time Delay Systems

Delay-Dependent State Estimation for Time Delay Systems WSEAS TRANSACTIONS o SYSTEMS ad CONTROL Mohammad Al Pakzad, Bja Moav Dlay-Dpd Sa Esmao for Tm Dlay Sysms MOHAMMAD ALI PAKZAD Dparm of Elcrcal Egrg Scc ad Rsarch Brach, Islamc Azad Uvrsy Thra IRAN m.pakzad@srbau.ac.r

More information

ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM RISK MODEL WITH CONSTANT INTEREST RATE

ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM RISK MODEL WITH CONSTANT INTEREST RATE Joural of Mahmacs ad Sascs 3: 339-357 4 ISSN: 549-3644 4 Scc Publcaos do:.3844/mssp.4.339.357 Publshd Ol 3 4 hp://www.hscpub.com/mss.oc ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM

More information

Multi-fluid magnetohydrodynamics in the solar atmosphere

Multi-fluid magnetohydrodynamics in the solar atmosphere Mul-flud magohydrodyams h solar amoshr Tmuraz Zaqarashvl თეიმურაზ ზაქარაშვილი Sa Rsarh Isu of Ausra Aadmy of Ss Graz Ausra ISSI-orksho Parally ozd lasmas asrohyss 6 Jauary- Fbruary 04 ISSI-orksho Parally

More information

Use of Non-Conventional Measures of Dispersion for Improved Estimation of Population Mean

Use of Non-Conventional Measures of Dispersion for Improved Estimation of Population Mean Amerca Joural of Operaoal esearch 06 6(: 69-75 DOI: 0.59/.aor.06060.0 Use of o-coveoal Measures of Dsperso for Improve Esmao of Populao Mea ubhash Kumar aav.. Mshra * Alok Kumar hukla hak Kumar am agar

More information

Chapter 4. Continuous Time Markov Chains. Babita Goyal

Chapter 4. Continuous Time Markov Chains. Babita Goyal Chapr 4 Couous Tm Markov Chas Baba Goyal Ky words: Couous m sochasc procsss, Posso procss, brh procss, dah procss, gralzd brh-dah procss, succssv occurrcs, r-arrval m. Suggsd radgs:. Mdh, J. (996, Sochasc

More information

Asymptotic Behavior of Finite-Time Ruin Probability in a By-Claim Risk Model with Constant Interest Rate

Asymptotic Behavior of Finite-Time Ruin Probability in a By-Claim Risk Model with Constant Interest Rate Th Uvrsy of Souhr Msssspp Th Aqula Dgal Commuy Sud ublcaos 8-5-4 Asympoc Bhavor of F-Tm Ru robably a By-Clam Rs Modl wh Cosa Irs Ra L Wag Uvrsy of Souhr Msssspp Follow hs ad addoal wors a: hps://aqula.usm.du/sud_pubs

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least 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 information

y y y

y y y Esimaors Valus of α Valus of α PRE( i) s 0 0 00 0 09.469 5 49.686 8 5.89 MSE( 9)mi 6.98-0.8870 854.549 THE EFFIIET USE OF SUPPLEMETARY IFORMATIO I FIITE POPULATIO SAMPLIG Rajs Sig Dparm of Saisics, BHU,

More information

Akpan s Algorithm to Determine State Transition Matrix and Solution to Differential Equations with Mixed Initial and Boundary Conditions

Akpan s Algorithm to Determine State Transition Matrix and Solution to Differential Equations with Mixed Initial and Boundary Conditions IOSR Joural o Elcrcal ad Elcrocs Egrg IOSR-JEEE -ISSN: 78-676,p-ISSN: 3-333, Volu, Issu 5 Vr. III Sp - Oc 6, PP 9-96 www.osrourals.org kpa s lgorh o Dr Sa Traso Marx ad Soluo o Dral Euaos wh Mxd Ial ad

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

More information

A Stochastic Approximation Iterative Least Squares Estimation Procedure

A Stochastic Approximation Iterative Least Squares Estimation Procedure Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar

More information

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it. oyrh I. Rrd from " PRODING Aual RLIAILITY ad MAINTAINAILITY ymosum" UA Jauary -. Ths maral s osd hr wh rmsso of h I. uch rmsso of h I dos o ay way mly I dorsm of ay of Rlaof ororao's roducs or srvcs. Iral

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

Chain DOUBLE PITCH TYPE RS TYPE RS POLY-STEEL TYPE

Chain DOUBLE PITCH TYPE RS TYPE RS POLY-STEEL TYPE d Fr Flw OULE IC YE YE OLY-EEL YE Oubard wh d s (d ) s usd fr fr flw vya. Usually w srads ar usd h qupm. d s basd sadard rllr ha wh sd rllrs salld xdd ps. hr ar hr yps f bas ha: (1) ubl ph rllr ha wh sadard

More information

(1) Cov(, ) E[( E( ))( E( ))]

(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 information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder Nolr Rgrsso Mjor: All Egrg Mjors Auhors: Aur Kw, Luk Sydr hp://urclhodsgusfdu Trsforg Nurcl Mhods Educo for STEM Udrgrdus 3/9/5 hp://urclhodsgusfdu Nolr Rgrsso hp://urclhodsgusfdu Nolr Rgrsso So populr

More information

Note on the Computation of Sample Size for Ratio Sampling

Note on the Computation of Sample Size for Ratio Sampling Not o th Computato of Sampl Sz for ato Samplg alr LMa, Ph.D., PF Forst sourcs Maagmt Uvrst of B.C. acouvr, BC, CANADA Sptmbr, 999 Backgroud ato samplg s commol usd to rduc cofdc trvals for a varabl of

More information

Solution set Stat 471/Spring 06. Homework 2

Solution 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 information

Two-Dimensional Quantum Harmonic Oscillator

Two-Dimensional Quantum Harmonic Oscillator D Qa Haroc Oscllaor Two-Dsoal Qa Haroc Oscllaor 6 Qa Mchacs Prof. Y. F. Ch D Qa Haroc Oscllaor D Qa Haroc Oscllaor ch5 Schrödgr cosrcd h cohr sa of h D H.O. o dscrb a classcal arcl wh a wav ack whos cr

More information

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm

More information

A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling

A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling Gloal Journal of Scnc Fronr Rsarch Mahmacs and Dcson Scncs Volum Issu 4 Vrson.0 ar 0 p : Doul Blnd Pr Rvwd Inrnaonal Rsarch Journal Pulshr: Gloal Journals Inc. USA Onln ISSN: 49-466 & Prn ISSN: 0975-5896

More information

A Class of Harmonic Meromorphic Functions of Complex Order

A Class of Harmonic Meromorphic Functions of Complex Order Borg Irol Jourl o D Mg Vol 2 No 2 Ju 22 22 A Clss o rmoc Mromorpc Fucos o Complx Ordr R Elrs KG Surm d TV Sudrs Asrc--- T sml work o Clu d Sl-Smll [3] o rmoc mppgs gv rs o suds o suclsss o complx-vlud

More information

NHPP and S-Shaped Models for Testing the Software Failure Process

NHPP and S-Shaped Models for Testing the Software Failure Process Irol Jourl of Ls Trds Copug (E-ISSN: 45-5364 8 Volu, Issu, Dcr NHPP d S-Shpd Modls for Tsg h Sofwr Flur Procss Dr. Kr Arr Asss Profssor K.J. Soy Isu of Mg Suds & Rsrch Vdy Ngr Vdy Vhr Mu. Id. dshuh_3@yhoo.co/rrr@ssr.soy.du

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

The rise of neural networks. Deep networks. Why many layers? Why many layers? Why many layers? 24/03/2017

The rise of neural networks. Deep networks. Why many layers? Why many layers? Why many layers? 24/03/2017 Th rs of ural ors I h md-s, hr has b a rsurgc of ural ors, mal du o rasos: hgh compuaoal por bcam avalabl a lo cos va gral-purpos graphcs procssg us (GPGPUs). maor plars l Googl, crosof, ad Facboo, dd

More information

Suzan Mahmoud Mohammed Faculty of science, Helwan University

Suzan Mahmoud Mohammed Faculty of science, Helwan University Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION

More information

Chap 2: Reliability and Availability Models

Chap 2: Reliability and Availability Models Chap : lably ad valably Modls lably = prob{s s fully fucog [,]} Suppos from [,] m prod, w masur ou of N compos, of whch N : # of compos oprag corrcly a m N f : # of compos whch hav fald a m rlably of h

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm 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 information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

As evident from the full-sample-model, we continue to assume that individual errors are identically and

As 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 information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

On the Class of New Better than Used. of Life Distributions

On the Class of New Better than Used. of Life Distributions Appld Mahacal Sccs, Vol. 6, 22, o. 37, 689-687 O h Class of Nw Br ha Usd of Lf Dsrbos Zohd M. Nofal Dpar of Sascs Mahacs ad Israc Facl of Corc Bha Uvrs, Egp dr_zofal@hoal.co Absrac So w rsls abo NBU3 class

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some 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 information

REVISTA INVESTIGACIÓN OPERACIONAL VOL., 34, NO 1, 35-57, 2013

REVISTA INVESTIGACIÓN OPERACIONAL VOL., 34, NO 1, 35-57, 2013 EVISTA INVESTIGAIÓN OEAIONAL VOL., 34, NO, 35-57, 03 ON SOME MODIFIED ATIO AND ODUT TE ESTIMATOS-EVISITED A K Swa Former rofessor of Sascs, Ual Uvers,Bhubaeswar-75004, Ida ABSTAT I hs paper dffere modfed

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

The R Package PK for Basic Pharmacokinetics

The R Package PK for Basic Pharmacokinetics Wolfsggr, h R Pacag PK St 6 h R Pacag PK for Basc Pharmacotcs Mart J. Wolfsggr Dpartmt of Bostatstcs, Baxtr AG, Va, Austra Addrss of th author: Mart J. Wolfsggr Dpartmt of Bostatstcs Baxtr AG Wagramr Straß

More information

Modeling of stock indices with HMM-SV models

Modeling of stock indices with HMM-SV models Thorcal ad Appld Ecoomcs Volum XXIV 7 No. 6 Summr pp. 45-6 Modlg of sock dcs wh HMM-SV modls E.B. NKEMNOLE Urs of Lagos Ngra kmol@ulag.du.g J.T. WULU Urs of Marlad Urs Collg USA joh.wulu@facul.umuc.du

More information

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns Summary: Solvng a Homognous Sysm of Two Lnar Frs Ordr Equaons n Two Unknowns Gvn: A Frs fnd h wo gnvalus, r, and hr rspcv corrspondng gnvcors, k, of h coffcn mar A Dpndng on h gnvalus and gnvcors, h gnral

More information

SOME IMPUTATION METHODS IN DOUBLE SAMPLING SCHEME FOR ESTIMATION OF POPULATION MEAN

SOME IMPUTATION METHODS IN DOUBLE SAMPLING SCHEME FOR ESTIMATION OF POPULATION MEAN aoal Joual of Mod Egg Rsach (JMER) www.jm.com ol. ssu. Ja-F 0 pp-00-07 N: 9- OME MPUTATON METHOD N DOUBLE AMPLNG HEME FOR ETMATON OF POPULATON MEAN ABTRAT Nada gh Thaku Kalpaa adav fo Mahmacal ccs (M)

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

Periodic Solutions of Periodic Delay Lotka Volterra Equations and Systems

Periodic Solutions of Periodic Delay Lotka Volterra Equations and Systems Joural of ahacal Aalyss ad Applcaos 255, 2628 Ž 2 do:6aa27248, avalabl ol a hp:wwwdalbraryco o Prodc Soluos of Prodc Dlay LokaVolrra Equaos ad Syss Yogku L Dpar of ahacs, Yua Ursy, Kug, Yua 659, Popl s

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Mellin Transform Method for the Valuation of the American Power Put Option with Non-Dividend and Dividend Yields

Mellin Transform Method for the Valuation of the American Power Put Option with Non-Dividend and Dividend Yields Joural of Mahmacal Fac, 5, 5, 49-7 Publshd Ol Augus 5 ScRs. h://www.scr.org/joural/jmf h://dx.do.org/.436/jmf.5.533 Mll Trasform Mhod for h Valuao of h Amrca Powr Pu Oo wh No-Dvdd ad Dvdd Ylds Suday Emmaul

More information

ELEN E4830 Digital Image Processing

ELEN E4830 Digital Image Processing ELEN E48 Dgal Imag Procssng Mrm Eamnaon Sprng Soluon Problm Quanzaon and Human Encodng r k u P u P u r r 6 6 6 6 5 6 4 8 8 4 P r 6 6 P r 4 8 8 6 8 4 r 8 4 8 4 7 8 r 6 6 6 6 P r 8 4 8 P r 6 6 8 5 P r /

More information

Imputation Based on Local Linear Regression for Nonmonotone Nonrespondents in Longitudinal Surveys

Imputation Based on Local Linear Regression for Nonmonotone Nonrespondents in Longitudinal Surveys Ope Joural of Sascs, 6, 6, 38-54 p://www.scrp.org/joural/ojs SSN Ole: 6-798 SSN Pr: 6-78X mpuao Based o Local Lear Regresso for Nomoooe Norespodes Logudal Surves Sara Pee, Carles K. Sego, Leo Odogo, George

More information

CHAPTER 7. X and 2 = X

CHAPTER 7. X and 2 = X CHATR 7 Sco 7-7-. d r usd smors o. Th vrcs r d ; comr h S vrc hs cs / / S S Θ Θ Sc oh smors r usd mo o h vrcs would coclud h s h r smor wh h smllr vrc. 7-. [ ] Θ 7 7 7 7 7 7 [ ] Θ ] [ 7 6 Boh d r usd sms

More information

AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 )

AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 ) AR() Procss Th firs-ordr auorgrssiv procss, AR() is whr is WN(0, σ ) Condiional Man and Varianc of AR() Condiional man: Condiional varianc: ) ( ) ( Ω Ω E E ) var( ) ) ( var( ) var( σ Ω Ω Ω Ω E Auocovarianc

More information

The Linear Regression Of Weighted Segments

The 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 information

(heat loss divided by total enthalpy flux) is of the order of 8-16 times

(heat loss divided by total enthalpy flux) is of the order of 8-16 times 16.51, Rok Prolson Prof. Manl Marnz-Sanhz r 8: Convv Ha ransfr: Ohr Effs Ovrall Ha oss and Prforman Effs of Ha oss (1) Ovrall Ha oss h loal ha loss r n ara s q = ρ ( ) ngrad ha loss s a S, and sng m =

More information

Computational Simulations and Experiments on Vibration Control of a Flexible Two-link Manipulator Using a Piezoelectric Actuator

Computational Simulations and Experiments on Vibration Control of a Flexible Two-link Manipulator Using a Piezoelectric Actuator Egrg Lrs, 3:3, EL_3_3_ Compuaoal Smulaos ad Exprms o Vbrao Corol of a Flxbl Two-lk Mapulaor Usg a Pzolcrc Acuaor Abdul Kadr Muhammad, Shgo Okamoo, Ja Hoo L, Mmbrs, IAENG Absrac Th purposs of hs rsarch

More information

CHAPTER Let "a" denote an acceptable power supply Let "f","m","c" denote a supply with a functional, minor, or cosmetic error, respectively.

CHAPTER Let a denote an acceptable power supply Let f,m,c denote a supply with a functional, minor, or cosmetic error, respectively. CHAPTER Sco - -. L "a", "b" do a par abov, blow h spccao S aaa, aab, aba, abb, baa, bab, bba, bbb { } -. L "" do a b rror L "o" do a b o rror "o" dos okay, o, o, oo, o, oo, oo, ooo, S o, oo, oo, ooo, oo,

More information