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

Size: px
Start display at page:

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

Transcription

1 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 egeerg surace markeg sesmolog socal scece ad survval aalss are eremel large; cosequel samplg mehods have o be coduced for characerzg hose pulaos. Rao esmaors are commol used o oba more effce esmaes for he pulao mea f he sud varable s hghl correlaed wh he aular varable. I s well kow ha he use of he pulao formao of aular varable mproves he precso of he esmae(s of he parameer(s. Rao esmaors are based o a sample whose dsrbuo s o cosdered. However here are suaos whch Posso dsrbued pulao ma be approprae. Ths paper proses geeralzed class of rao esmaors from Posso dsrbued pulao. The mea square error (MSE equaos of prosed esmaors are compared applcao wh usual rao esmaor. B hese comparsos we fd ha rao esmaors usg Posso dsrbuo characerscs as aular varable formao s beer ha usual rao esmaors. The codos are also foud ha prosed esmaors are more effce. The fdgs are supred b umercal llusrao wh earhquake daa of Turke. Ke words: Rao-pe esmaors; Smple radom samplg; Mea square error; Posso dsrbuo; Effcec. JEL ode: Iroduco Smple radom samplg (SRS from a fe pulao has araced much of he researchers ad pracoers workg surves. Rao esmaors are commol used he SRS o oba more effce esmaes for he pulao mea f he sud varable s hghl correlaed wh he aular varable. I s well kow ha he use of he pulao formao of aular varable mproves he precso of he esmae(s of he parameer(s he SRS. Several auhors cludg Ssoda ad wved (98 Upadhaa ad Sgh (999 Kadlar ad g (004 Gupa ad Shabbr ( Koucu ad Kadlar (009 Sgh ad Vshwakarma (00 Shabbr ad Gupa (0 obaed a large 070

2 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 umber of mproved rao esmaors/classes of esmaors for he pulao mea of he sud varable usg aular varable formao he SRS. The problem of esmag he pulao mea or oal he presece of a aular varable has bee wdel dscussed he SRS whou cosderg he dsrbuo. However he Posso dsrbuo s geerall used for he aural pulaos o epress he probabl of a gve umber of rare eves ad here has bee o effor devoed o he developme of rao esmaors for a Posso dsrbued pulao. No-esece of he rao esmaors for he Posso dsrbued sample obsacles usage of hem samplg heor self ad s applcaos (Ozel ad Ial 008. The am of hs sud s o derve ew rao esmaors for he pulao mea from a Posso dsrbued pulao. We also eame he behavor of he esmaors of mea for he rao esmaors he SRS. The earhquake daa s used for he umercal eample sce earhquakes are rare eves ad geerall follows a Posso dsrbuo (Ozel 0a. Suggesed Esmaors for he Posso srbued Populao osder a fe pulao U (u u...u cossg of N defable ad dsc us. Le ad respecvel be he sud ad aular varables assocaed wh each u N ( j... N of he pulao. Assume ha s are kow us ad s are ukow us for all he pulao. Supse ha a sample of sze s seleced accordg o he SRS. The aure of he samplg dsrbuo depeds o he aure of he pulao from whch he radom sample s draw. Le us assume ha he pare pulao has a Posso dsrbuo. Ths meas ha he radom samples whch are draw from a Posso dsrbued pulao follow also a Posso dsrbuo. The le us selec he observaos (... from a Posso dsrbued pulao. B hs wa we sugges ha he followg a geeralzed class of rao esmaors for he pulao mea of he sud varable from Posso dsrbued pulao as Rˆ (a b Rˆ where a b 0 a b a b. Here / / are he sample meas of a b he sud ad aular varables from Posso dsrbued pulao respecvel. Noe ha ( a b are eher cosas or fuco of kow parameers of he pulao such as a or ( ( ad. As meoed before he de of dsperso s used for he frs me samplg heor for he aular varable formao. Le he aular 07 u j

3 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 varable has a Posso dsrbuo wh parameer 0 he S ( ad ( of he aular varable are gve b S ( S S 0 ( ( N 04 ( respecvel where 0 N ( 0 0 / 0 / N 0 N ( 4 ad ( ( N 04 N for he Posso dsrbued pulao. Le he sud varable has a Posso dsrbuo wh parameer ad le he aular varable has a Posso dsrbuo wh paramaer he he coeffce of correlao bewee he sud varable ad aular varable s obaed b he rvarae reduco mehod (Ozel 0b. A bvarae Posso dsrbuo of ad s geeraed b seg m z ad w z.... Assumg ha he parameers of m w ad z are ad he coeffce of he correlao bewee ad equals cov( ( ( ( ( Po ( S S ( ( ( ( where ( [( ( ] ( ( ( ( ( E E ad (. A obvous properes of S E S s ha he correlao s resrced o be srcl sve sce ad 0. Sce we selec he observaos (... from a Posso dsrbued pulao wh parameers ad he we ge (m z / ad (w z /. The covarace of ad s cov( ( ( ( ( ( ( ( where E( ad E(. The MSE of he prosed esmaor ca be foud usg Talor seres mehod defed as 07

4 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 g( g(cd g(cd g( ( ( ( c d where g( Rˆ ad g( R. Eq. ( ca be appled o he geeralzed class of rao esmaors order o oba he MSE equao ad we have MSE(Rˆ a R V( ar cov( V( (4 (a b a R ( ar ( (a b where R. Thus we oba he MSE of he prosed esmaor as a b MSE( (a b MSE(Rˆ a R ( ar ( Sce ( ( ad we have MSE( a R ar. (5 Effcec omparsos The rao esmaors preseed Table wll be compared wh each oher accordg o her MSE equaos he heor. Table Some members of he class of rao esmaor for ad Usual Rao Esmaors a b Rao Esmaors a b ( ( ( ( ( ( PR ( 6 ( ( 07 ( ( ( ( 4 ( ( 5 6 ( 7 ( (

5 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 ( 7 ( 8 PR 9 0 ( ( ( PR 4 PR4 5 ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( 6 ( ( 7 ( ( ( PR5 PR6 PR7 ( ( ( PR8 ( PR9 ( ( ( 8 ( ( 9 0 ( ( ( ( ( ( ( ( ( ( ( ( ( 4 ( ( ( 5 ( ( ( ( 6 ( ( ( ( ( 7 ( ( ( 8 ( ( 9 ( 0 ( ( ( ( ( p ( 4 ( ( ( 5 ( ( 6 074

6 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 We frs compare... 6 for a wh j for a Table o oba j he effcec comparso as follows: Usg Eq. (5 we ca wre MSE( MSE(. (6 j R R a R ar From hs equal we have ( a ( a 0 where ( a 0. R ( a The we have R 0 ( a( a. I s wre as ( ar. Hece he effcec codo for Eq. (6 s foud as. ( ar 0.Ths codo s alwas sasfed sce R are alwas sve whe a 0 ad 0. Hece we ca fer ha he prosed rao esmaors j are more effce ha he esmaors... 6 usg he j aular varable formao. 5. Numercal Illusrao I he sud we cosder he earhquake daa of Turke for he umercal comparsos of he prosed ad oher rao esmaors he SRS. We cosder mashocks ha occured Turke bewee 900 ad 0 havg surface wave magudes M S 5. 0 her foreshocks wh fve das wh M S. 0 ad afershocks wh oe moh wh M S I hs area 0 mashocks wh surface magude M S 5. 0 have occured bewee 900 ad 0. The pulao cosss of he desrucve earhquakes. I he pulao daa se he umber of afershocks s a sud varable ad he umber of foreshocks s a aular varable. The MSE values of usual rao esmaors r r... 7 ad PR PR9 obaed from Eq. (4 whou cosderg he dsrbuos of he sud ad aular varables. The summar sascs for he pulao are gve. The he MSE values of he prosed esmaors... are compued from Eq. (5 wh cosderg he dsrbuos ( 6 of he sud ad aular varables. Several sudes modeled earhquakes as a Posso dsrbuo (Ozel 0a b. To oba he dsrbuo of hese varables we f Posso are

7 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 dsrbuo o he earhquake daase. The Posso dsrbuo provded a adequae f wh p-value <0.0 ad ch-square value ( for he goodess of f es. Ths meas ha he Posso dsrbuo wh parameer (ear fs he probabl fuco of he aular varable ad he epeced umber of foreshocks of a ma shock appromael equals o seve per ear. Afer obag he frequec dsrbuo of afershocks ad goodess of f es ( p-value= <0.0 s see ha he sud varable has a Posso dsrbuo wh parameer The summar sascs for he Posso dsrbued pulao are gve. To oba for he Posso dsrbued daa Turke s dvded o hree ma eoecoc domas based o he eoecoc zoes of Turke. The foreshocks Turke are separaed accordg o hese eoecoc zoes. B hs wa he parameers ad are obaed. Accordg o he goodess of f es s see ha he Posso dsrbuo fs he umber of shocks for area Rego wh parameer 4.8 ( p - value 0.04 wh parameer ( 0.04 p - value 0.0 for Rego ad. ( 0.0 p - value 0.05 for Rego. The he correlao bewee he sud varable ad aular varable s sve ( 0. 7 ad ca sad ha he umber of foreshocks s relaed o he umber of afershocks. Therefore he rao esmaors ca be used for he esmao of he pulao mea he SRS. The MSE values of he usual rao esmaors r r... 7 ad PR... PR9 are obaed ad he prosed mea esmaors (... 6 are compued usg SRS ad Table. 076

8 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Table R values ad MSE equaos for he rao esmaors of he Posso dsrbued pulao Rao Esmaors ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( R MSE Equaos ( ( ( ( ( ( ( ( ( ( ( ( ( MSE ( MSE ( ( MSE ( ( ( ( MSE ( 4 ( ( ( MSE ( 5 ( MSE ( 6 ( MSE ( 7 ( ( ( MSE ( 8 ( ( ( MSE ( 9 ( MSE ( 0 ( ( ( ( MSE ( (( ( ( ( ( MSE ( ( ( ( ( ( ( ( ( MSE ( ( ( ( ( ( ( MSE ( 4 (( ( ( ( ( MSE ( 5 (( ( ( ( ( MSE ( 6 ( ( ( ( ( ( ( ( MSE( 7 ( ( ( 077

9 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember ( ( ( ( 0 ( ( ( ( ( ( 4 ( ( 5 6 ( ( ( ( ( ( MSE ( 8 (( ( MSE ( 9 ( MSE ( 0 ( ( ( MSE ( ( ( ( MSE ( ( MSE ( ( ( MSE ( 4 ( ( ( ( MSE ( 5 ( ( ( ( MSE ( 6 ( ( We use he followg epresso o fd he relave effcec (RE of rao esmaors usg he characerscs of Posso dsrbuo whe compared wh he usual rao esmaors. The he prosed ad usual rao esmaors are compared wh respec o her MSE ad RE values. We foud ha ( he prosed faml rao esmaors usg characerscs of Posso dsrbuo perform beer ha usual faml usual rao esmaors ( he relave effcec of he prosed faml rao esmaors are appromael 56 mes more ha he usual rao esmaors for he Posso dsrbued daa ( he larges ga effcec s observed b usg ( ad wh ( f er-group comparso of for he prosed esmaors s doe for he Posso dsrbued daa. (v he MSE value of he prosed -faml rao esmaor usg ad 078 ogeher s smaller ha he oher usual - faml rao esmaors (v he prosed rao esmaors... have he same value of ( 6 MSE wh... sce 5. However f here s a pulao for dffere dsrbued pulaos de of dsperso wll dffer from. I such a case... 6 eld dffere MSE values from Thus he class of he prosed rao esmaors s o be preferred o usual rao esmaors for he Posso dsrbued pulao he SRS.

10 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 ocluso I hs sud frs we suggesed rao esmaors for he pulao mea usg de of dsperso as a aular varable. The we have developed ew rao esmaors usg characerscs of Posso dsrbued aular varable for he pulao mea SRS ad obaed her MSE equaos. ffere classes of rao esmaors are also prosed usg he aular varable formao wh cosderg he dsrbuo of pulao. B MSE equaos ad RE values he MSE values are compared ad s foud ha he prosed faml esmaors are alwas more effce ha he usual -faml esmaors for he Posso dsrbued earhquake daa. Ths heorecal resul s also supred b a umercal eample based o a earhquake daa of Turke. I he forhcomg sudes we hope o develop ew esmaors for he pulao mea for he Posso dsrbued pulao usg oher samplg mehods. Refereces Gupa S Shabbr J (007 O he use of rasformed aular varable esmag pulao mea. J Sa Pla Iferece 7(5: Gupa S Shabbr J (008 O mproveme esmag he pulao mea smple radom samplg. Joural of Appled Sascs 5(5: Kadlar g H (004 Rao esmaors smple radom samplg. Appled Mahemacs ad ompuao 5: Koucu N Kadlar (009 Rao ad produc esmaors srafed radom samplg. J Sa Pla Iferece 9(8: Ozel G Ial (008 The probabl fuco of he comud Posso process ad a applcao afershock sequeces Turke Evromercs 9(: Ozel G (0a A bvarae comud Posso model for he occurrece of foreshock ad afershock sequeces Turke Evromercs (7: Ozel G (0b O cera properes of a class of bvarae comud Posso dsrbuos ad a applcao o earhquake daa Revsa olombaa Esadsca 4(: Shabbr J Gupa S (0 O esmag he fe pulao mea smple ad srafed radom samplg ommucao Sascs: Theor ad Mehods 40(0: Sgh HP Vshwakarma GK (00 A geeral procedure for esmag he pulao mea srafed samplg usg aular formao. Mero 68(:

11 The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Ssoda BVS wved VK (98 A modfed rao esmaor usg coeffce of varao of aular varable. Joural of he Ida Soce of Agrculural Sascs (: -8. Upadhaa LN Sgh HP (999 Use of rasformed aular varable esmag he fe pulao mea. Bomercal Joural 4(5: oac Gamze Özel Haceepe Uvers eparme of Sascs Akara Turke gamzeozl@haceepe.edu.r 080

Generalized Estimators Using Characteristics of Poisson distribution. Prayas Sharma, Hemant K. Verma, Nitesh K. Adichwal and *Rajesh Singh

Generalized Estimators Using Characteristics of Poisson distribution. Prayas Sharma, Hemant K. Verma, Nitesh K. Adichwal and *Rajesh Singh Geeralzed Esaors Usg Characerscs of osso dsrbuo raas Shara, Hea K. Vera, Nesh K. Adchwal ad *Rajesh Sgh Depare of Sascs, Baaras Hdu Uvers Varaas(U..), Ida-5 * Corresdg auhor rsghsa@gal.co Absrac I hs arcle,

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

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

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

(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

14. Poisson Processes

14. 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 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

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

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

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

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = 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 information

COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION

COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION Eldesoky E. Affy. Faculy of Eg. Shbee El kom Meoufa Uv. Key word : Raylegh dsrbuo, leas squares mehod, relave leas squares, leas absolue

More information

The Poisson Process Properties of the Poisson Process

The 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 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

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments 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 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

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

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International 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

Chapter 8. Simple Linear Regression

Chapter 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 information

Key words: Fractional difference equation, oscillatory solutions,

Key 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 information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least 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 information

Final Exam Applied Econometrics

Final Exam Applied Econometrics Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The 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 information

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination 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 information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL 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 information

Available online Journal of Scientific and Engineering Research, 2014, 1(1): Research Article

Available online  Journal of Scientific and Engineering Research, 2014, 1(1): Research Article Avalable ole wwwjsaercom Joural o Scec ad Egeerg Research, 0, ():0-9 Research Arcle ISSN: 39-630 CODEN(USA): JSERBR NEW INFORMATION INEUALITIES ON DIFFERENCE OF GENERALIZED DIVERGENCES AND ITS APPLICATION

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED 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 information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution 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 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

Mixed Integral Equation of Contact Problem in Position and Time

Mixed 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 information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-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 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

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

Regression Approach to Parameter Estimation of an Exponential Software Reliability Model

Regression Approach to Parameter Estimation of an Exponential Software Reliability Model Amerca Joural of Theorecal ad Appled Sascs 06; 5(3): 80-86 hp://www.scecepublshggroup.com/j/ajas do: 0.648/j.ajas.060503. ISSN: 36-8999 (Pr); ISSN: 36-9006 (Ole) Regresso Approach o Parameer Esmao of a

More information

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,

VARIATIONAL 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 information

Real-time Classification of Large Data Sets using Binary Knapsack

Real-time Classification of Large Data Sets using Binary Knapsack Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule

More information

Redundancy System Fault Sampling Under Imperfect Maintenance

Redundancy System Fault Sampling Under Imperfect Maintenance A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

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 information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. 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 information

Calibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling

Calibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling I.J.Curr.crobol.App.Sc (08) 7(): 808-85 Ieraoal Joural of Curre crobolog ad Appled Scece ISS: 39-7706 olue 7 uber 0 (08) Joural hoepage: hp://www.jca.co Orgal Reearch Arcle hp://do.org/0.0546/jca.08.70.9

More information

An Exact Solution for the Differential Equation. Governing the Lateral Motion of Thin Plates. Subjected to Lateral and In-Plane Loadings

An Exact Solution for the Differential Equation. Governing the Lateral Motion of Thin Plates. Subjected to Lateral and In-Plane Loadings Appled Mahemacal Sceces, Vol., 8, o. 34, 665-678 A Eac Soluo for he Dffereal Equao Goverg he Laeral Moo of Th Plaes Subjeced o Laeral ad I-Plae Loadgs A. Karmpour ad D.D. Gaj Mazadara Uvers Deparme of

More information

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square

Density 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 information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR 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 information

To Estimate or to Predict

To Estimate or to Predict Raer Schwabe o Esmae or o Predc Implcaos o he esg or Lear Mxed Models o Esmae or o Predc - Implcaos o he esg or Lear Mxed Models Raer Schwabe, Marya Prus raer.schwabe@ovgu.de suppored by SKAVOE Germa ederal

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

Fault 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 information

Integral Equations and their Relationship to Differential Equations with Initial Conditions

Integral 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 information

Linear Regression Linear Regression with Shrinkage

Linear 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 information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

A Generalized Class of Ratio-Cum-Dual to Ratio Estimators of Finite Population Mean Using Auxiliary Information in Sample Surveys

A Generalized Class of Ratio-Cum-Dual to Ratio Estimators of Finite Population Mean Using Auxiliary Information in Sample Surveys Math Sc Lett 5 o 3- (6) 3 Mathematcal Sceces Letters A Iteratoal Joural http://ddog/8576/msl/55 A Geeralzed lass of ato-um-dual to ato Estmats of Fte Populato Mea Usg Aular Ifmato Sample Surves Housla

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

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity

Bianchi 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 information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 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 information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. 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 information

Density estimation III.

Density 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 information

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files) . Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals 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 information

Numerical approximatons for solving partial differentıal equations with variable coefficients

Numerical 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 information

Cyclone. Anti-cyclone

Cyclone. 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 information

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae

More information

A Generalized Class of Dual to Product-Cum-Dual to Ratio Type Estimators of Finite Population Mean In Sample Surveys

A Generalized Class of Dual to Product-Cum-Dual to Ratio Type Estimators of Finite Population Mean In Sample Surveys Appl Math If Sc Lett 4 o 5-33 (6) 5 Appled Mathematcs & Ifmato Sceces Letters A Iteratoal Joural http://ddog/8576/amsl/45 A Geeralzed lass of Dual to Product-um-Dual to Rato Tpe stmats of Fte Populato

More information

Complementary Tree Paired Domination in Graphs

Complementary Tree Paired Domination in Graphs IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X Volume 2, Issue 6 Ver II (Nov - Dec206), PP 26-3 wwwosrjouralsorg Complemeary Tree Pared Domao Graphs A Meeaksh, J Baskar Babujee 2

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

The algebraic immunity of a class of correlation immune H Boolean functions

The 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 information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 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 information

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October ISSN

International 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 information

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE Joural o Mathematcs ad tatstcs 9 (3): 49-55, 03 I: 549-3644 03 cece Publcatos do:0.3844/jmssp.03.49.55 Publshed Ole 9 (3) 03 (http://www.thescpub.com/jmss.toc) ADAPTIVE CLUTER AMPLIG UIG AUXILIARY VARIABLE

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The 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 information

Nature and Science, 5(1), 2007, Han and Xu, Multi-variable Grey Model based on Genetic Algorithm and its Application in Urban Water Consumption

Nature and Science, 5(1), 2007, Han and Xu, Multi-variable Grey Model based on Genetic Algorithm and its Application in Urban Water Consumption Naure ad Scece, 5, 7, Ha ad u, ul-varable Grey odel based o Geec Algorhm ad s Applcao Urba Waer Cosumpo ul-varable Grey odel based o Geec Algorhm ad s Applcao Urba Waer Cosumpo Ha Ya*, u Shguo School of

More information

Fully Fuzzy Linear Systems Solving Using MOLP

Fully 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 information

ON TESTING EXPONENTIALITY AGAINST NBARFR LIFE DISTRIBUTIONS

ON TESTING EXPONENTIALITY AGAINST NBARFR LIFE DISTRIBUTIONS STATISTICA, ao LII,. 4, ON TESTING EPONENTIALITY AGAINST NBARR LIE DISTRIBUTIONS M. A. W. Mahmoud, N. A. Abdul Alm. INTRODUCTION AND DEINITIONS Tesg expoealy agas varous classes of lfe dsrbuos has go a

More information

4. THE DENSITY MATRIX

4. 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 information

Optimal Eye Movement Strategies in Visual Search (Supplement)

Optimal Eye Movement Strategies in Visual Search (Supplement) Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case

More information

Pricing of CDO s Based on the Multivariate Wang Transform*

Pricing of CDO s Based on the Multivariate Wang Transform* Prcg of DO s Based o he Mulvarae Wag Trasform* ASTIN 2009 olloquum @ Helsk 02 Jue 2009 Masaak Kma Tokyo Meropola versy/ Kyoo versy Emal: kma@mu.ac.p hp://www.comp.mu.ac.p/kmam * Jo Work wh Sh-ch Moomya

More information

Model for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts

Model for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts Joural of Evromeal cece ad Egeerg A 7 (08) 8-45 do:0.765/6-598/08.06.00 D DAVID UBLIHING Model for Opmal Maageme of he pare ars ock a a Irregular Dsrbuo of pare ars veozar Madzhov Fores Research Isue,

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

Continuous Time Markov Chains

Continuous 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 information

Almost unbiased exponential estimator for the finite population mean

Almost unbiased exponential estimator for the finite population mean 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

More information

Research on portfolio model based on information entropy theory

Research on portfolio model based on information entropy theory Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceucal esearch, 204, 6(6):286-290 esearch Arcle ISSN : 0975-7384 CODEN(USA) : JCPC5 esearch o porfolo model based o formao eropy heory Zhag Jusha,

More information

Mathematical Formulation

Mathematical Formulation Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg

More information

The Signal, Variable System, and Transformation: A Personal Perspective

The Signal, Variable System, and Transformation: A Personal Perspective The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The 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 information

EE 6885 Statistical Pattern Recognition

EE 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 information

Other Topics in Kernel Method Statistical Inference with Reproducing Kernel Hilbert Space

Other 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 information

Some Chain Type Estimators for Population Variance in Two Phase Sampling

Some Chain Type Estimators for Population Variance in Two Phase Sampling Ieraal Jural Rece ad Iva Treds Cmpu ad Cmmuca I: 3-869 me Cha Tpe Esmars fr Ppula Varace Tw Phase ampl A. Badpadha, P. Parchha ad Pambar Das. Deparme f Mahemacs, Asasl Eeer Cllee, Asasl 7335, Ida. Emal:

More information

A note on Turán number Tk ( 1, kn, )

A note on Turán number Tk ( 1, kn, ) A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

SYRIAN SEISMIC CODE :

SYRIAN SEISMIC CODE : SYRIAN SEISMIC CODE 2004 : Two sac mehods have bee ssued Syra buldg code 2004 o calculae he laeral sesmc forces he buldg. The Frs Sac Mehod: I s he same mehod he prevous code (995) wh few modfcaos. I s

More information

Reliability Equivalence of a Parallel System with Non-Identical Components

Reliability Equivalence of a Parallel System with Non-Identical Components Ieraoa Mahemaca Forum 3 8 o. 34 693-7 Reaby Equvaece of a Parae Syem wh No-Ideca ompoe M. Moaer ad mmar M. Sarha Deparme of Sac & O.R. oege of Scece Kg Saud Uvery P.O.ox 455 Ryadh 45 Saud raba aarha@yahoo.com

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum 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 information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The 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 information

Asymptotic Regional Boundary Observer in Distributed Parameter Systems via Sensors Structures

Asymptotic 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 information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

The Bernstein Operational Matrix of Integration

The Bernstein Operational Matrix of Integration Appled Mahemacal Sceces, Vol. 3, 29, o. 49, 2427-2436 he Berse Operaoal Marx of Iegrao Am K. Sgh, Vee K. Sgh, Om P. Sgh Deparme of Appled Mahemacs Isue of echology, Baaras Hdu Uversy Varaas -225, Ida Asrac

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval 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 information

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda Pankaj Chauhan, Nrmala awan chool of ascs, DAVV, Indore (M.P.), Inda Florenn marandache Deparmen of Mahemacs, Unvers of New Meco, Gallup, UA

More information

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision

Solving 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 information

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Partial Molar Properties of solutions

Partial 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 information