VARIABLE SECOND-ORDER INCLUSION PROBABILITIES AS A TOOL TO PREDICT THE SAMPLING VARIANCE
|
|
- Lee Ross
- 5 years ago
- Views:
Transcription
1 VARIABLE SECOND-ORDER INCLUSION PROBABILITIES AS A TOOL TO PREDICT THE SAPLING VARIANCE Bataan Geelhoed; Delft Unverty of Tehnology, ekelweg 5, 69 JB Delft, The Netherland; b.geelhoed@tudelft.nl ABSTRACT A generalzaton of Gy theory for the varane of the fundamental amplng error revewed. Pratal tuaton where the generalzed model potentally lead to more aurate varane etmate are dentfed a: luterng of partle, dfferene n dente or ze of the partle or repulve nter-partle fore. Two general approahe for etmatng an nput parameter for the generalzed model are dued. The frt approah ont of modellng baed on phyal properte of partle uh a ze, denty and eletrotat fore between partle. The eond approah ue mage analy of atual ample. Further reearh nto both method propoed and a uggeton made to ue lne-nterept amplng ombned wth arkov Chan modellng n the eond approah. It onluded that although, at the moment, t too early for a routne applaton of the generalzed theory, the generalzaton ha the potental of provdng more aurate varane etmate than are poble n the theory of Gy. Therefore, further reearh nto the development and expanon of the generalzed theory worthwhle.
2 VARIABLE SECOND-ORDER INCLUSION PROBABILITIES 83 INTRODUCTION Durng the prevou World Conferene on Samplng and Blendng, WCSB-, a generalzaton of Gy' model for the fundamental amplng error (ee Gy 979, 983) wa propoed and an equaton to predt the varane of the fundamental amplng error wa derved (Geelhoed, 005A). It wa dued that applaton of th equaton ould, at leat n theory, lead to more aurate predton of the varane of the fundamental error than are urrently provded n the theory of Gy. In th ontrbuton, the generalzaton and the new theoretal development ne t wa propoed at WCSB- wll be revewed. Stuaton where t expeted that the generalzed theory wll beome relevant wll be dued. An mportant new nput parameter of the new model the parameter for the dependent eleton of partle. Therefore, two general approahe of etmatng th nput parameter wll be dued. The frt approah a modellng approah baed on phyal properte of partle uh a ze, denty and eletrotat fore between partle. The eond approah ue mage analy of atual ample. Further reearh nto both approahe wll be propoed. ETHODOLOGY Gy' theory baed on the underlyng aumpton that amplng orrepond to Poon amplng: every partle ndependently ubeted to a Bernoull experment, where the -th partle n the bath ha a probablty of q of beomng part of the ample and a probablty of q of not beomng part of the ample. If amplng orretne aumed, whh requre that all partle of the amplng target have an equal probablty of beomng part of the ample, all probablte q beome equal and an therefore be denoted by a ngle ymbol q. The parameter q generally quantfed (etmated) ung the rato of the ample ze and the bath ze from whh the ample wa taken, whh an be nterpreted a the frt-order nluon probablte of the partle. A generalzaton of the above model wa propoed (Geelhoed, 005A). The generalzaton onern the eond-order nluon probablte,.e. the probablte that a par of partle and partle beome part of the ample. Beaue the theory of Gy baed on Poon amplng, the eondorder nluon probablte are gven n Gy' theory by q q. In the generalzed model, the eond-order nluon probablty, denoted a π, gven by: π = q q ( C ) (Eq. ) where C the "parameter for the dependent eleton of partle", whh form a ymmetr matrx (C = C ). It noted that the value for q, q and C are generally nfluened by the hoe of the amplng trategy, the ample ma and the partle properte, o the eond-order nluon probablte (and ultmately the amplng varane) wll alo depend on thee rumtane. An equaton for the varane of the fundamental amplng error wa derved ung () and the followng fve general and pratally reaonable ondton (Geelhoed, 005A):. The partle an be lafed ung a fnte number of lae.. The ze of the bath from whh the ample drawn nfnte, o that amplng an effetvely be regarded a "amplng wth replaement".. The frt-order nluon probablte of partle may vary between lae, but do not vary for partle wthn a la. v. The eond-order nluon probablte may depend on the lae of both partle, but there no varaton n eond-order nluon probablte of dfferent partle par but wth eah member belongng to the ame la a the orrepondng member n the other partle par.
3 84 B. GEELHOED v. Varaton n ample ma reman mall, o that the ample onentraton an effetvely be lnearzed a a funton of the ample ma. Under thee ondton, the equaton for the varane of the fundamental amplng error equvalent to the followng equaton for the varane of the onentraton n the ample, V( ample ): V( ample ) Nm ( ) C NN mm ( )( ) (Eq. ) Where the parameter m and denote repetvely the ma of and onentraton n a partle belongng to the -th partle la, ', N' and ' are repetvely the expeted value of the ample ma, the expeted value of the number of partle belongng to the -th la n the ample and the expeted value of the onentraton of the property of nteret n the ample. In () and ubequent part of th artle, C repreent the parameter for the dependent eleton of partle of a par ontng of a partle belongng to the -th la and a partle belongng to the -th la. The frt term on the rght-hand de of () orrepond to the equaton derved n the theory of Gy (Geelhoed, 005A), where the varane nverely proportonal to the expeted value of the ample ma when the expeted value N' are proportonal to the expeted value of the ample ma. It an thu be een that Gy method to derve a model equaton for the fundamental amplng error rele on the mplt aumpton that frt-order nluon probablte play a domnant role n explanng the ample-to-ample varaton and that the hgher eond-order nluon probablte do not gnfantly nfluene the reult,.e. the eond term, lnear n C, neglgble. The generalzaton therefore offer the potental of a more aurate derpton of the varane of the fundamental amplng error. The eond term on the rght-hand de an thu be ondered to be a orreton term, derbng the effet of non-zero eond-order nluon probablte. It noted that th orreton term may alo depend on the expeted value of the ample ma. Therefore, an eay determnaton of the orreton term by onderng a ere of ample wth large ample mae, uh that the frt term on the rght-hand de would beome neglgble ompared to the orreton term, not generally poble. The obervaton that the varane of the amplng error vare nverely proportonal wth the expeted value of the ample ma hould not be een a an ndaton that the orreton term (and hene the effet of non-zero eond-order nluon probablte on the amplng varane) doe not gnfantly nfluene the amplng varane. Under ertan aumpton about the amplng proe, Lyman (998) derved an expreon for the amplng varane for amplng from a bath of partle n a totally egregated tate and aumed that th value repreent a maxmum poble value for the amplng varane. It would be nteretng to nvetgate whether () an gve a theoretal ba for th aumpton. Further duon of th ubet however outde the ope of th artle. If the expeted value ', N' and ' are not known (a generally the ae n prate), the varane may be etmated by replang thee expeted value n () by the orrepondng value n a ample. The reultng varane etmator then denoted a Var( ample ) (ntead of V( ample )) and the orrepondng ample value of ', N' and ' are repetvely denoted a, N and. The thu obtaned equaton :
4 VARIABLE SECOND-ORDER INCLUSION PROBABILITIES 85 Var( ample ) N m ( ) C N N m m ( )( (Eq. 3) ) where, N and repreent repetvely the ma of the ample, the number of partle belongng to the -th la n the ample and the onentraton n the ample. Another way of etmatng the varane baed on the ue of the Horvtz-Thompon etmator (ee e.g. Särndal et al, 99), whh lead to the followng varane etmator (Geelhoed, 006), denoted a V HT ( ample ): N m C N N m m V HT (ample) (Eq. 4) - C - C A dervaton preented n the Appendx. The equaton baed on the addtonal aumpton that the amplng proe ued to draw the ample lead to ample of a ontant ma and that the frtorder nluon probablty of the partle gven by the rato of the ample ma and the ma of the bath from whh the ample wa drawn (.e. amplng orret). (4) therefore not applable when C =0 for all poble value of and (Gy model), beaue n that ae the ample ma ould not be ontant, but would vary between zero and the ma of the bath from whh the ample wa drawn. If the ample value, N and are not known, thee may be replaed by ther expeted value ', N' and ' repetvely, f avalable, n order to arrve at an etmated value for V HT. It hould be noted that the ue of (4) an lead to dfferent etmate for the varane than the ue of (3), o future pratal ue of thee equaton need to take nto aount the trength and weaknee of both opton. A full duon of th however outde the ope of th paper. However, a bref and prelmnary duon wll be gven. A general advantage of V HT ( ample ) that, beaue t baed on the Horvtz-Thompon etmator, whh unbaed, t expeted that the ba n V HT ( ample ) generally maller than the ba n Var( ample ). Another advantage of the ue of V HT ( ample ) that, n the mple rumtane where there only one partle la k ontng of partle wth a non-zero value for the onentraton k, whle all other lae ( k) have =0, the equaton mplfed o that t doe not expltly depend on the mae of the other partle: V HT ( ample ) = (/ )( ( N k C kk ) k m k /( C kk )). If a relable empral etmate for the amplng varane, denoted here a V e avalable, e.g. determned ung the analy of a ere of ample of ontant ma, th etmate an be ubttuted nto the above equaton, from whh the parameter C kk an then be olved: C kk =(V e k m k / )/(V e N k k m k / ). Hene, C kk zero when V e = k m k /, whh wll here be denoted a V GY, beaue n Gy amplng model C kk would be zero by defnton. Table how the thu obtaned etmate for C kk for everal value of the rato of V e and V GY and the number N k of partle wth non-zero onentraton n the ample (or t expeted value N' k ).
5 86 B. GEELHOED Table - Value of the etmated value of C kk for ome value of the rato of the empral varane etmate V e and the varane etmate V GY and (the expeted value of) the number of partle wth non-zero onentraton n the ample for the mple ae of only one partle la (la k) wth non-zero onentraton. N k, N k V e /V GY A general drawbak of the ue of V HT ( ample ) that t trtly only applable when the ample ma ontant. Whle t tehnally poble to draw ample of approxmately ontant ma, remanng varaton n ample ma ould lead to a ba n the etmate alulated ung V HT ( ample ). Therefore, further tudy requred nto pratally aeptable value for the ba n varane etmate and aeptable level of varaton n ample ma for whh V HT ( ample) an tll be ued wthout ba orreton. Three general advantage of the ue of Var( ample ) wll be dued. The frt advantage of the ue of Var( ample ) that t an ealy be een that the value of Var( ample ) wll be zero when all onentraton and are equal. Th derable, beaue f all partle have the ame onentraton, the varane of the ample onentraton wll be zero. A eond advantage that the value of Var( ample ) not nfluened by ontant ytemat error n the determnaton of the parameter and. Fnally, the thrd advantage that Var( ample ) depend n a lnear way on the parameter C, makng t le entve to error n the determnaton of C than V HT ( ample ), for value of C loe to one. A further duon of the dfferene between the etmator expreed n (3) and (4) and the potental pratal onequene outde the ope of th artle. A prerequte of applaton of the equaton of the generalzed theory knowledge of the parameter for the dependent eleton of partle, C. It therefore ueful to dentfy the patal dtrbuton of partle n the bath before the ample drawn a a domnant underlyng oure of dependent eleton of partle (Geelhoed, 005B). Two type of partle that tend to be more n the vnty of eah other than would be expeted on the ba of ompletely ndependently randomly patal dtrbuton of partle wll have an nreaed eond-order nluon probablty and wll therefore have a negatve value of C. On the other hand, two type of partle that tend to be further away from eah other than would be expeted on the ba of a ompletely random and ndependent patal dtrbuton of partle wll have a dereaed eond-order nluon probablty and wll therefore have a potve value of C. In vew of the above remark, fator ontrbutng to a potental dereae of the value of C are dentfed a: luterng of partle aued by attratve nter-partle fore, uh a attratve eletrotat fore or moture that make partle wet and tk to eah other. Fator leadng to a potental nreae n the value of C are dentfed a: denty dfferene ombned wth the nfluene of gravty durng tranport or repulve nter-partle fore. Wth all of the effet mentoned, the ze and hape of the partle alo nfluene the magntude of the effet. Sze alo n telf a fator leadng to a potental nreae of the value of C. Th an be demontrated ung a mple example of two pheral partle wth dameter D and D. The dtane between (the entre of mae of) the partle annot be maller than D / + D /. Hene, larger partle wll on average be further away from eah other than maller partle, leadng to a potental nreae of C.
6 VARIABLE SECOND-ORDER INCLUSION PROBABILITIES 87 RESULTS A generalzaton of the theory of Gy wa revewed, whh an potentally lead to more aurate etmate for the varane of the fundamental amplng error. Beaue a prerequte of applaton of the equaton of the generalzed theory knowledge of the parameter for the dependent eleton of partle, an mportant reult the dentfaton of the patal dtrbuton of partle n the bath before the ample drawn a a domnant underlyng oure of dependent eleton of partle. Th then reult n the dentfaton of luterng of partle, dfferene n dente or ze of the partle and repulve nter-partle fore a fator nfluenng the magntude of the parameter for the dependent eleton of partle. DISCUSSION Beaue the generalzaton ha the potental of provdng more aurate predton for the varane of the fundamental error, poble approahe to evaluate the eental new nput parameter for the generalzed theory, the parameter for the dependent eleton of partle, are dued below. A frt approah would be to fnd a model equaton for predtng the value of C (and ultmately alo of the varane) baed on the phyal properte of the partle uh a ze, denty, hape and eletrotat properte. Th a dffult and omplex tak, whh, f poble, may be ueful for ertan applaton, but wll not be further dued here. A eond approah would be the applaton of mage analy of ample, whh allow obervng dretly the patal dtrbuton of partle. Although t generally eay to obtan an mage, there are many potental way of extratng nformaton about the value of the parameter C from the mage. Below, one poble way wll be dued. A poble way of evaluatng the parameter for the dependent eleton of partle would be to ue lne-nterept amplng (alo known a lne-tranet amplng ) of the mage, a method of amplng partle n a regon whereby, roughly, a partle ampled f a hoen lne egment, alled a tranet, nteret the partle (Kaer, 983). Th produe a one-dmenonal han of partle from whh nformaton about the patal dtrbuton, pefally C an be derved. Th done by frt ountng the number of tranton between the dfferent partle type. The number of tranton gong from a partle of type to a partle of type denoted a N. Intutvely t lear that there wll be a relatonhp between C and N : a large negatve value of C wll lead to a hgh value of N, beaue partle type that have a hgh eond-order nluon probablty tend to have more tranton between eah other. arkov Chan modellng (ee e.g. Freedman, 97) ould be appled to quantfy th relatonhp. However, th wll not be further developed here. A potental problem wth the above-derbed method that the lne-nterept ample ould be baed toward larger partle, beaue of the nreaed probablty of nteretng a larger partle. Therefore, further tudy requred nto the effet of th potental ba on the etmate for C and t effet on the varane etmate. Alo further tudy nto way of overomng th ba requred for ae where the effet of th ba on the varane etmate non-neglgble. Some general obervaton onernng the eond approah an be made baed on the fat that th approah rele on an mage of the ample. Beaue two-dmenonal mage an be obtaned ung a varety of routne tehnque rangng from dgtal photography to Sannng Eletron roopy, further tudy nto the feablty of the eond approah requred for a range of materal and tehnque to obtan two-dmenonal mage. A ommon problem wth thee D tehnque,
7 88 B. GEELHOED however, that a urfae ample may be unrepreentatve for the whole ample. Th problem an be overome by ung a 3D magng tehnque (f avalable of oure!) or ung multple twodmenonal ro eton of the ame ample. However, for fat and heap reult, ung a dgtal mage of the top urfae would be deal. Therefore, further reearh alo propoed nto th applaton. A fnal pont of duon onernng the generalzed theory a a whole that although t ha the potental of provdng more aurate varane etmate than the theory of Gy, pratally relevant partle ytem mut be dentfed for whh t an be demontrated that th a gnfant mprovement. Further expermental work therefore requred before the new approah an beome a routnely appled and generally aepted method. CONCLUSIONS At the moment t too early for a routne applaton of the generalzed theory. However, the generalzaton ha the potental of provdng more aurate varane etmate than now poble n the theory of Gy. In vew of th, two approahe for evaluatng an eental nput parameter of the generalzed model are dued: a modellng approah baed on the partle properte and an approah baed on mage analy of an atual ample. Only one method, a method baed on lnenterept amplng and arkov Chan modellng, n the eond approah brefly dued n th artle. There are many other potental method of determnng the parameter for the dependent eleton of partle. Beaue the generalzed theory an potentally lead to more aurate etmate for the varane of the fundamental amplng error, further reearh nto the development of new method n both approahe propoed. APPENDIX A π-expanded etmator (ee e.g. Särndal et al, 99) for the onentraton n the bath gven by: N m π (Eq. 5) bath π bath where < bath > π the π-expanded etmator for the onentraton n the bath, N the number of partle n the ample belongng to the -th partle la, m and are repetvely the ma of and the onentraton n a partle belongng to the -th la, bath the ma of the bath and π the frt-order nluon probablty of a partle belongng to the -th la. A dervaton of the above equaton an be found n Geelhoed (004). If the ample ma ontant and the frt-order nluon probablty equal to the rato of the ample ma ( ) and the bath ma ( bath ), the π- expanded etmator beome equal to the ample onentraton,. Under thee aumpton (and ondton (), () and (v) tated n the man text of th artle), Geelhoed (004) derved the followng equaton for the varane of the ample onentraton, baed on the general Horvtz- Thompon etmator for the varane of the π-expanded etmator: mm m V HT (ample) N N ( ) N ( ) (Eq. 6) π π π π π bath bath
8 VARIABLE SECOND-ORDER INCLUSION PROBABILITIES 89 n whh π the eond-order nluon probablty of a partle par n whh the frt-partle belong to the -th la and the eond to the -th la. Subttuton of () for the eond-order nluon probablty and π = / bath, reult n: m m V HT (ample) N N ( ) ( )N (Eq. 7) C C bath Aumng that the bath from whh the ample wa drawn muh larger than the ample,.e. bath >> ample o that /( C ) / bath /( C ), the above reult an be rearranged to yeld (4) n the man text. m REFERENCES Geelhoed, B, 005A. A generalaton of Gy model for the fundamental amplng error. Seond World Conferene on Samplng and Blendng. The Autralaan Inttute of nng and etallurgy, Autrala, (ISBN ). pp 9-5. Geelhoed, B, 005B. A amplng tudy of ndutral mxture of partle, Appled Statt program and abtrat. Slovena, (ISBN ). pp Geelhoed, B, 006. Varable eond-order nluon probablte durng the amplng of ndutral mxture of partle, Appled Stohat odel n Bune and Indutry. Vol., pp Gy, P, 979 and 983. Samplng of partulate materal, Theory and prate. Elever: Amterdam. 43 p. Lyman, G, 998. The nfluene of egregaton of partulate on amplng varane. the queton of dtrbutonal heterogenety. Internatonal Journal of neral Proeng. pp 95-. Kaer, L, 983. Unbaed Etmaton n Lne-Interept Samplng, Bometr 39. pp Freedman, D, 97. arkov Chan. San Frano: Holden-day. ISBN/ISSN/CN p. Särndal, C E, Swenon, B, and Wretman, J. 99. odel Ated Survey Samplng. New York: Sprnger-Verlag.
Additional File 1 - Detailed explanation of the expression level CPD
Addtonal Fle - Detaled explanaton of the expreon level CPD A mentoned n the man text, the man CPD for the uterng model cont of two ndvdual factor: P( level gen P( level gen P ( level gen 2 (.).. CPD factor
More informationSpecification -- Assumptions of the Simple Classical Linear Regression Model (CLRM) 1. Introduction
ECONOMICS 35* -- NOTE ECON 35* -- NOTE Specfcaton -- Aumpton of the Smple Clacal Lnear Regreon Model (CLRM). Introducton CLRM tand for the Clacal Lnear Regreon Model. The CLRM alo known a the tandard lnear
More informationEstimation of Finite Population Total under PPS Sampling in Presence of Extra Auxiliary Information
Internatonal Journal of Stattc and Analy. ISSN 2248-9959 Volume 6, Number 1 (2016), pp. 9-16 Reearch Inda Publcaton http://www.rpublcaton.com Etmaton of Fnte Populaton Total under PPS Samplng n Preence
More informationTeam. Outline. Statistics and Art: Sampling, Response Error, Mixed Models, Missing Data, and Inference
Team Stattc and Art: Samplng, Repone Error, Mxed Model, Mng Data, and nference Ed Stanek Unverty of Maachuett- Amhert, USA 9/5/8 9/5/8 Outlne. Example: Doe-repone Model n Toxcology. ow to Predct Realzed
More informationJSM Survey Research Methods Section. Is it MAR or NMAR? Michail Sverchkov
JSM 2013 - Survey Researh Methods Seton Is t MAR or NMAR? Mhal Sverhkov Bureau of Labor Statsts 2 Massahusetts Avenue, NE, Sute 1950, Washngton, DC. 20212, Sverhkov.Mhael@bls.gov Abstrat Most methods that
More informationarxiv: v1 [cond-mat.stat-mech] 8 Jan 2019
Quantum Phae Tranton n Fully-Conneted Quantum Wajnflaz Pk Model Yuya Sek 1, Shu Tanaka 2,3, Shro Kawabata 1 1 Nanoeletron Reearh Inttute, Natonal Inttute of Advaned Indutral Sene and Tehnology (AIST),
More informationChapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder
S-. The Method of Steepet cent Chapter. Supplemental Text Materal The method of teepet acent can be derved a follow. Suppoe that we have ft a frtorder model y = β + β x and we wh to ue th model to determne
More informationWave Particle Dualism for Both Matter and Wave and Non-Einsteinian View of Relativity
Talukder and Aad: Wave Partle Dual for Bot Matter and Wave and Non-Entenan Vew of Relatvty (8-91) Wave Partle Dual for Bot Matter and Wave and Non-Entenan Vew of Relatvty M.O.G. Talukder 1, Mufq Aad 1
More informationMULTIPLE REGRESSION ANALYSIS For the Case of Two Regressors
MULTIPLE REGRESSION ANALYSIS For the Cae of Two Regreor In the followng note, leat-quare etmaton developed for multple regreon problem wth two eplanator varable, here called regreor (uch a n the Fat Food
More informationHarmonic oscillator approximation
armonc ocllator approxmaton armonc ocllator approxmaton Euaton to be olved We are fndng a mnmum of the functon under the retrcton where W P, P,..., P, Q, Q,..., Q P, P,..., P, Q, Q,..., Q lnwgner functon
More informationOptimal Design of Multi-loop PI Controllers for Enhanced Disturbance Rejection in Multivariable Processes
Proeedng of the 3rd WSEAS/IASME Internatonal Conferene on Dynamal Sytem and Control, Arahon, Frane, Otober 3-5, 2007 72 Optmal Degn of Mult-loop PI Controller for Enhaned Dturbane Rejeton n Multvarable
More informationTwo commodity perishable inventory system with negative and. impatient customers, postponed demands and a finite population
Int Journal of ppled ene Engneerng Reearh ol Iue 4 wwwaerom 4 by the author Lenee IJER- Under Creatve Common Lene edtoral@aerom Reearh artle I 77 944 Two ommodty perhable nventory ytem wth negatve mpatent
More informationScattering of two identical particles in the center-of. of-mass frame. (b)
Lecture # November 5 Scatterng of two dentcal partcle Relatvtc Quantum Mechanc: The Klen-Gordon equaton Interpretaton of the Klen-Gordon equaton The Drac equaton Drac repreentaton for the matrce α and
More informationChapter 6 The Effect of the GPS Systematic Errors on Deformation Parameters
Chapter 6 The Effect of the GPS Sytematc Error on Deformaton Parameter 6.. General Beutler et al., (988) dd the frt comprehenve tudy on the GPS ytematc error. Baed on a geometrc approach and aumng a unform
More informationSmall signal analysis
Small gnal analy. ntroducton Let u conder the crcut hown n Fg., where the nonlnear retor decrbed by the equaton g v havng graphcal repreentaton hown n Fg.. ( G (t G v(t v Fg. Fg. a D current ource wherea
More informationRoot Locus Techniques
Root Locu Technque ELEC 32 Cloed-Loop Control The control nput u t ynthezed baed on the a pror knowledge of the ytem plant, the reference nput r t, and the error gnal, e t The control ytem meaure the output,
More informationModelling for Cruise Two-Dimensional Online Revenue Management System
Modellng for Crue Two-Dmenonal Onlne Revenue Management Sytem Bngzhou L Modellng for Crue Two-Dmenonal Onlne Revenue Management Sytem Bngzhou L Shool of Management, Xamen Unverty, Chna, lbngzhou260@63.om
More informationIntroduction to Interfacial Segregation. Xiaozhe Zhang 10/02/2015
Introducton to Interfacal Segregaton Xaozhe Zhang 10/02/2015 Interfacal egregaton Segregaton n materal refer to the enrchment of a materal conttuent at a free urface or an nternal nterface of a materal.
More informationStatistical Properties of the OLS Coefficient Estimators. 1. Introduction
ECOOMICS 35* -- OTE 4 ECO 35* -- OTE 4 Stattcal Properte of the OLS Coeffcent Etmator Introducton We derved n ote the OLS (Ordnary Leat Square etmator ˆβ j (j, of the regreon coeffcent βj (j, n the mple
More informationThe multivariate Gaussian probability density function for random vector X (X 1,,X ) T. diagonal term of, denoted
Appendx Proof of heorem he multvarate Gauan probablty denty functon for random vector X (X,,X ) px exp / / x x mean and varance equal to the th dagonal term of, denoted he margnal dtrbuton of X Gauan wth
More informationMoving Source Localization in Near-field by a Stationary Passive Synthetic Aperture Array
APSIPA ASC X an Movng Soure Loalzaton n Near-eld by a Statonary Pave Synthet Aperture Array Zhwe Wang * Feng Tan Yxn Yang Lngj Xu * College o Underwater Aout Engneerng, Harbn Engneerng Unverty, Harbn Naton
More informationVoltammetry. Bulk electrolysis: relatively large electrodes (on the order of cm 2 ) Voltammetry:
Voltammetry varety of eletroanalytal methods rely on the applaton of a potental funton to an eletrode wth the measurement of the resultng urrent n the ell. In ontrast wth bul eletrolyss methods, the objetve
More informationSection2: Index Models for Binary Response: Probit and Logit
Drete Repone Model In th hapter we etend GMM to non-lnear etmaton problem Seton: he Lnear robablt Model for Bnar Repone E ( + +... + k k ( + +... + k k Var --- (5. ( ( --- (5. (5. - Equaton (5. mple that
More informationMore Ramsey Pricing. (firm willing to produce) Notation: 1 of 6
EP 7426, Problem Set 3 Len abrera More Ramey Prng regulated frm rodue two rodut, and, and ell them dretly to fnal utomer. onumer demand for thee erve known erfetly, a are the frm' roduton ot. Produt rodued
More informationAn Information-Theoretic Study for Noisy Multiple Measurement Vectors with Different Sensing Matrices
> Aepted for publaton n IEEE ranaton on Informaton heory An Informaton-heoret tudy for oy Multple Meaurement Vetor wth Dfferent enng Matre angjun Park, am Yul Yu and Heung-o Lee*, enor Member, IEEE Abtrat
More informationConstraints of Compound Systems: Prerequisites for Thermodynamic Modeling Based on Shannon Entropy
Entropy 2014, 16, 2990-3008; do:10.3390/e16062990 OPEN ACCESS entropy ISSN 1099-4300 www.mdp.om/ournal/entropy Artle Contrant of Compound Sytem: Prerequte for Thermodynam Modelng Baed on Shannon Entropy
More informationThe corresponding link function is the complementary log-log link The logistic model is comparable with the probit model if
SK300 and SK400 Lnk funtons for bnomal GLMs Autumn 08 We motvate the dsusson by the beetle eample GLMs for bnomal and multnomal data Covers the followng materal from hapters 5 and 6: Seton 5.6., 5.6.3,
More informationNo! Yes! Only if reactions occur! Yes! Ideal Gas, change in temperature or pressure. Survey Results. Class 15. Is the following possible?
Survey Reult Chapter 5-6 (where we are gong) % of Student 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Hour Spent on ChE 273 1-2 3-4 5-6 7-8 9-10 11+ Hour/Week 2008 2009 2010 2011 2012 2013 2014 2015 2017 F17
More informationComplement of an Extended Fuzzy Set
Internatonal Journal of Computer pplatons (0975 8887) Complement of an Extended Fuzzy Set Trdv Jyot Neog Researh Sholar epartment of Mathemats CMJ Unversty, Shllong, Meghalaya usmanta Kumar Sut ssstant
More informationNot at Steady State! Yes! Only if reactions occur! Yes! Ideal Gas, change in temperature or pressure. Yes! Class 15. Is the following possible?
Chapter 5-6 (where we are gong) Ideal gae and lqud (today) Dente Partal preure Non-deal gae (next tme) Eqn. of tate Reduced preure and temperature Compreblty chart (z) Vapor-lqud ytem (Ch. 6) Vapor preure
More information2.3 Least-Square regressions
.3 Leat-Square regreon Eample.10 How do chldren grow? The pattern of growth vare from chld to chld, o we can bet undertandng the general pattern b followng the average heght of a number of chldren. Here
More informationSTK4900/ Lecture 4 Program. Counterfactuals and causal effects. Example (cf. practical exercise 10)
STK4900/9900 - Leture 4 Program 1. Counterfatuals and ausal effets 2. Confoundng 3. Interaton 4. More on ANOVA Setons 4.1, 4.4, 4.6 Supplementary materal on ANOVA Example (f. pratal exerse 10) How does
More informationTHE SOLAR SYSTEM. We begin with an inertial system and locate the planet and the sun with respect to it. Then. F m. Then
THE SOLAR SYSTEM We now want to apply what we have learned to the olar ytem. Hitorially thi wa the great teting ground for mehani and provided ome of it greatet triumph, uh a the diovery of the outer planet.
More informationFriction parameters identification and compensation of LuGre model base on genetic algorithms
Internatonal Sympoum on Computer & Informat (ISCI 015) Frton parameter dentfaton and ompenaton of LuGre model bae on genet algorthm Yuqn Wen a, Mng Chu b and Hanxu Sun Shool of Automaton, Bejng Unverty
More informationAP Statistics Ch 3 Examining Relationships
Introducton To tud relatonhp between varable, we mut meaure the varable on the ame group of ndvdual. If we thnk a varable ma eplan or even caue change n another varable, then the eplanator varable and
More informationAn Information Theoretic Study for Noisy Compressed Sensing With Joint Sparsity Model 2
> ubmtted to IEEE ranaton on Informaton heory An Informaton heoret tudy for oy Compreed enng Wth Jont party Model angjun Park, am Yul Yu and Heung-o Lee*, enor Member, IEEE Abtrat In th paper, we tudy
More informationPythagorean triples. Leen Noordzij.
Pythagorean trple. Leen Noordz Dr.l.noordz@leennoordz.nl www.leennoordz.me Content A Roadmap for generatng Pythagorean Trple.... Pythagorean Trple.... 3 Dcuon Concluon.... 5 A Roadmap for generatng Pythagorean
More informationMultiple Lorentz Groups A Toy Model for Superluminal Muon Neutrinos
Journal of Modern Phy 01 3 1398-107 http://dx.do.org/10.36/jmp.01.310177 Publhed Onlne Otober 01 (http://www.srp.org/journal/jmp) Multple Lorentz Group A Toy Model for Superlumnal Muon Neutrno Maro Shrek
More informationPopulation element: 1 2 N. 1.1 Sampling with Replacement: Hansen-Hurwitz Estimator(HH)
Chapter 1 Samplng wth Unequal Probabltes Notaton: Populaton element: 1 2 N varable of nterest Y : y1 y2 y N Let s be a sample of elements drawn by a gven samplng method. In other words, s s a subset of
More informationImage Registration for a Series of Chest Radiograph Images
Proceedng of the 5th WE Internatonal Conference on gnal Proceng, Itanbul, Turkey, May 7-9, 006 (pp179-184) Image Regtraton for a ere of Chet Radograph Image Omar Mohd. Rjal*, Norlza Mohd. Noor, hee Lee
More informationImprovements on Waring s Problem
Improvement on Warng Problem L An-Png Bejng, PR Chna apl@nacom Abtract By a new recurve algorthm for the auxlary equaton, n th paper, we wll gve ome mprovement for Warng problem Keyword: Warng Problem,
More informationPHYSICS 212 MIDTERM II 19 February 2003
PHYSICS 1 MIDERM II 19 Feruary 003 Exam s losed ook, losed notes. Use only your formula sheet. Wrte all work and answers n exam ooklets. he aks of pages wll not e graded unless you so request on the front
More informationChapter 4. Simulations. 4.1 Introduction
Chapter 4 Simulation 4.1 Introdution In the previou hapter, a methodology ha been developed that will be ued to perform the ontrol needed for atuator haraterization. A tudy uing thi methodology allowed
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationPHYSICALLY-BASED CONSTITUTIVE EQUATIONS FOR IRRADIATED RPV AND INTERNALS
Tranng Shool, 3-7 September 208 Polytehn Unverty of Valena (Span) PHYSICALLY-BASED CONSTITUTIVE EQUATIONS FOR IRRADIATED RPV AND INTERNALS Ghath Monnet, EDF R&D, MMC Ludov Vnent, CEA DEN, SRMA Chu Ma,
More informationTwo Approaches to Proving. Goldbach s Conjecture
Two Approache to Provng Goldbach Conecture By Bernard Farley Adved By Charle Parry May 3 rd 5 A Bref Introducton to Goldbach Conecture In 74 Goldbach made h mot famou contrbuton n mathematc wth the conecture
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More informationVerification of Selected Precision Parameters of the Trimble S8 DR Plus Robotic Total Station
81 Verfcaton of Selected Precon Parameter of the Trmble S8 DR Plu Robotc Total Staton Sokol, Š., Bajtala, M. and Ježko, J. Slovak Unverty of Technology, Faculty of Cvl Engneerng, Radlnkého 11, 81368 Bratlava,
More informationChapter.4 MAGNETIC CIRCUIT OF A D.C. MACHINE
Chapter.4 MAGNETIC CIRCUIT OF A D.C. MACHINE The dfferent part of the dc machne manetc crcut / pole are yoke, pole, ar ap, armature teeth and armature core. Therefore, the ampere-turn /pole to etablh the
More informationMachine Learning: and 15781, 2003 Assignment 4
ahne Learnng: 070 and 578, 003 Assgnment 4. VC Dmenson 30 onts Consder the spae of nstane X orrespondng to all ponts n the D x, plane. Gve the VC dmenson of the followng hpothess spaes. No explanaton requred.
More informationSEMI-ACTIVE MR DAMPERS FOR SEISMIC CONTROL OF STRUCTURES
157 SEMI-ACIVE MR DAMPERS FOR SEISMIC CONROL OF SRUCURES Jagadh G. Kor 1 and R.S. Jangd 2 SUMMARY Magnotorheologal (MR) damper have been demontrated to be more effetve n redung the trutural repone due
More informationAmelioration of Verdegay s Approach for Fuzzy Linear Programs with Stochastic Parameters
` Iranan Journal of Manageent Stude (IJMS) http://.ut.a.r/ Vol. 11, No. 1, Wnter 2018 Prnt ISSN: 2008-7055 pp. 71-89 Onlne ISSN: 235-375 DOI: 10.22059/.2018.23617.672722 Aeloraton of Verdegay Approah for
More informationOpportunities in Analytical Approaches to Spray Drying of Solid Dosage Forms
Opportuntes n Analytal Approahes to Spray Dryng of Sold Dosage Forms Dr. Renhard Vehrng Assoate Professor and George Ford Char n Materals Engneerng Unversty of Alberta, Department of Mehanal Engneerng
More informationGEL 446: Applied Environmental Geology
GE 446: ppled Envronmental Geology Watershed Delneaton and Geomorphology Watershed Geomorphology Watersheds are fundamental geospatal unts that provde a physal and oneptual framewor wdely used by sentsts,
More informationSupporting Information. Hydroxyl Radical Production by H 2 O 2 -Mediated. Conditions
Supportng Informaton Hydroxyl Radcal Producton by H 2 O 2 -Medated Oxdaton of Fe(II) Complexed by Suwannee Rver Fulvc Acd Under Crcumneutral Frehwater Condton Chrtopher J. Mller, Andrew L. Roe, T. Davd
More informationTwo-Layered Model of Blood Flow through Composite Stenosed Artery
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 93-9466 Vol. 4, Iue (December 9), pp. 343 354 (Prevouly, Vol. 4, No.) Applcaton Appled Mathematc: An Internatonal Journal (AAM) Two-ayered Model
More informationAnalysis of Variance and Design of Experiments-II
Anly of Vrne Degn of Experment-II MODULE VI LECTURE - 8 SPLIT-PLOT AND STRIP-PLOT DESIGNS Dr Shlbh Deprtment of Mthemt & Sttt Indn Inttute of Tehnology Knpur Tretment ontrt: Mn effet The uefulne of hvng
More informationSampling Theory MODULE V LECTURE - 17 RATIO AND PRODUCT METHODS OF ESTIMATION
Samplng Theory MODULE V LECTURE - 7 RATIO AND PRODUCT METHODS OF ESTIMATION DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPUR Propertes of separate rato estmator:
More informationtechnische universiteit eindhoven Analysis of one product /one location inventory control models prof.dr. A.G. de Kok 1
TU/e tehnshe unverstet endhoven Analyss of one produt /one loaton nventory ontrol models prof.dr. A.G. de Kok Aknowledgements: I would lke to thank Leonard Fortun for translatng ths ourse materal nto Englsh
More informationFirst Year Examination Department of Statistics, University of Florida
Frst Year Examnaton Department of Statstcs, Unversty of Florda May 7, 010, 8:00 am - 1:00 noon Instructons: 1. You have four hours to answer questons n ths examnaton.. You must show your work to receve
More informationLogistics and spatial planning. Case studies on optimizing the supply chain and reorganizing the urban freight distribution
Logt and patal plannng Cae tude on optmzng the upply han and reorganzng the urban reght dtrbuton Ir. Thoma Dubo Ghent Unverty Logt and patal plannng Cae tude on optmzng the upply han and reorganzng the
More informationFUZZY FINITE ELEMENT METHOD
FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments
More informationEQUILIBRIA IN SUBSURFACE FLUIDS WITH LINEAR INTERACTION BETWEEN DECAY AND SORPTION
JOUNAL O ENVONMENTAL HYDOLOGY The Eletron Journal o the nternatonal Aoaton or Envronmental Hydrology On the World Wde Web at http://www.hydroweb.om VOLUME 5 997 EQULBA N SUBSUACE LUDS WTH LNEA NTEACTON
More informationModeling the airside dynamic behavior of a heat exchanger under frosting conditions
Journal of Mehanal Sene and Tehnology 5 (10) (011) 719~78 www.prngerlnk.om/ontent/178-494x DOI 10.1007/106-011-0615-5 Modelng the arde dynam behavor of a heat exhanger under frotng ondton Teyu Gao and
More informationThe Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction
ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also
More informationDigital Signal Processing
Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over
More informationTo determine the biasing conditions needed to obtain a specific gain each stage must be considered.
PHYSIS 56 Experiment 9: ommon Emitter Amplifier A. Introdution A ommon-emitter oltage amplifier will be tudied in thi experiment. You will inetigate the fator that ontrol the midfrequeny gain and the low-and
More informationResource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis
Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques
More informationS-Domain Analysis. s-domain Circuit Analysis. EE695K VLSI Interconnect. Time domain (t domain) Complex frequency domain (s domain) Laplace Transform L
EE695K S nterconnect S-Doman naly -Doman rcut naly Tme doman t doman near rcut aplace Tranform omplex frequency doman doman Tranformed rcut Dfferental equaton lacal technque epone waveform aplace Tranform
More informationOptimal inference of sameness Supporting information
Optmal nference of amene Supportng nformaton Content Decon rule of the optmal oberver.... Unequal relablte.... Equal relablte... 5 Repone probablte of the optmal oberver... 6. Equal relablte... 6. Unequal
More informationModule 9. Lecture 6. Duality in Assignment Problems
Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept
More informationExpected Value and Variance
MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or
More informationComputation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models
Computaton of Hgher Order Moments from Two Multnomal Overdsperson Lkelhood Models BY J. T. NEWCOMER, N. K. NEERCHAL Department of Mathematcs and Statstcs, Unversty of Maryland, Baltmore County, Baltmore,
More informationFormulas for the Determinant
page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationThe influence of Stern layer conductance on the. dielectrophoretic behaviour of latex nanospheres
The nfluence of Stern layer conductance on the delectrophoretc behavour of latex nanophere Mchael Pycraft Hughe* Bomedcal Engneerng Group, Unverty of Surrey, Guldford, GU2 7XH, UK Ncola Gavn Green Boelectronc
More informationDesign of Recursive Digital Filters IIR
Degn of Recurve Dgtal Flter IIR The outut from a recurve dgtal flter deend on one or more revou outut value, a well a on nut t nvolve feedbac. A recurve flter ha an nfnte mule reone (IIR). The mulve reone
More informationExercise 10: Theory of mass transfer coefficient at boundary
Partle Tehnology Laboratory Prof. Sotrs E. Pratsns Sonneggstrasse, ML F, ETH Zentrum Tel.: +--6 5 http://www.ptl.ethz.h 5-97- U Stoffaustaush HS 7 Exerse : Theory of mass transfer oeffent at boundary Chapter,
More informationGravity Drainage Prior to Cake Filtration
1 Gravty Dranage Pror to ake Fltraton Sott A. Wells and Gregory K. Savage Department of vl Engneerng Portland State Unversty Portland, Oregon 97207-0751 Voe (503) 725-4276 Fax (503) 725-4298 ttp://www.e.pdx.edu/~wellss
More informationA Result on a Cyclic Polynomials
Gen. Math. Note, Vol. 6, No., Feruary 05, pp. 59-65 ISSN 9-78 Copyrght ICSRS Pulcaton, 05.-cr.org Avalale free onlne at http:.geman.n A Reult on a Cyclc Polynomal S.A. Wahd Department of Mathematc & Stattc
More informationStatistics II Final Exam 26/6/18
Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the
More informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationConfidence intervals for the difference and the ratio of Lognormal means with bounded parameters
Songklanakarn J. Sc. Technol. 37 () 3-40 Mar.-Apr. 05 http://www.jt.pu.ac.th Orgnal Artcle Confdence nterval for the dfference and the rato of Lognormal mean wth bounded parameter Sa-aat Nwtpong* Department
More informationPhysics 111. CQ1: springs. con t. Aristocrat at a fixed angle. Wednesday, 8-9 pm in NSC 118/119 Sunday, 6:30-8 pm in CCLIR 468.
c Announcement day, ober 8, 004 Ch 8: Ch 10: Work done by orce at an angle Power Rotatonal Knematc angular dplacement angular velocty angular acceleraton Wedneday, 8-9 pm n NSC 118/119 Sunday, 6:30-8 pm
More informationModelling Monotonic Behaviour of Unsaturated Compacted Soils in Constant Volume Direct Simple Shear
Modellng Monoton Behavour of Unaturated Compated Sol n Contant Volume Dret Smple Shear T. Ihgak, S.Omoto and N.Nemoto NIPPO Corporaton Reearh Inttute, Tokyo, Japan A. Izuka Kobe Unverty, Kobe, Hyogo, Japan
More informationGeneralized Linear Methods
Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set
More informationThe optimal delay of the second test is therefore approximately 210 hours earlier than =2.
THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple
More informationA Novel Approach for Testing Stability of 1-D Recursive Digital Filters Based on Lagrange Multipliers
Amercan Journal of Appled Scence 5 (5: 49-495, 8 ISSN 546-939 8 Scence Publcaton A Novel Approach for Tetng Stablty of -D Recurve Dgtal Flter Baed on Lagrange ultpler KRSanth, NGangatharan and Ponnavakko
More information8 Waves in Uniform Magnetized Media
8 Wave n Unform Magnetzed Meda 81 Suceptblte The frt order current can be wrtten j = j = q d 3 p v f 1 ( r, p, t) = ɛ 0 χ E For Maxwellan dtrbuton Y n (λ) = f 0 (v, v ) = 1 πvth exp (v V ) v th 1 πv th
More informationA Theorem of Mass Being Derived From Electrical Standing Waves (As Applied to Jean Louis Naudin's Test)
A Theorem of Mass Beng Derved From Eletral Standng Waves (As Appled to Jean Lous Naudn's Test) - by - Jerry E Bayles Aprl 4, 000 Ths paper formalzes a onept presented n my book, "Eletrogravtaton As A Unfed
More informationProofs of Number of Compressed Measurements Needed for Noisy Distributed Compressed Sensing
Proof of umber of Comreed eaurement eeded for oy Dtrbuted Comreed enng angjun Park Heung-o Lee hool of Informaton Communaton Gwangju Inttute of ene ehnology Gwangju Reubl of Korea {jark@gt.a.k heungno@gt.a.kr}
More informationPlastic Analysis and Design of Steel Plate Shear Walls
7 Platc Analy and Degn of Steel Plate Shear Wall Jeffrey Berman Department of Cvl, Structural & Envronmental Engneerng, Unverty at Buffalo Reearch Supervor: Mchel Bruneau, Profeor Summary A reved procedure
More informationCHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS
CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS 103 Phy 1 9.1 Lnear Momentum The prncple o energy conervaton can be ued to olve problem that are harder to olve jut ung Newton law. It ued to decrbe moton
More informationQuick Visit to Bernoulli Land
Although we have een the Bernoull equaton and een t derved before, th next note how t dervaton for an uncopreble & nvcd flow. The dervaton follow that of Kuethe &Chow ot cloely (I lke t better than Anderon).
More informationLeast Squares Algorithms for Time-of-Arrival Based Mobile Location
Leat Square Algorthm for me-of-arrval Baed oble Loaton K. W. Cheung H. C. So W.-K. a Y.. Chan Dept. of Computer Engneerng & Informaton ehnology Cty Unverty of Hong Kong at Chee Avenue Kowloon Hong Kong
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationA Theorem of Mass Being Derived From Electrical Standing Waves (As Applied to Jean Louis Naudin's Test)
A Theorem of Mass Beng Derved From Eletral Standng Waves (As Appled to Jean Lous Naudn's Test) - by - Jerry E Bayles Aprl 5, 000 Ths Analyss Proposes The Neessary Changes Requred For A Workng Test Ths
More informationFREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,
FREQUENCY DISTRIBUTIONS Page 1 of 6 I. Introducton 1. The dea of a frequency dstrbuton for sets of observatons wll be ntroduced, together wth some of the mechancs for constructng dstrbutons of data. Then
More informationSee Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)
Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes
More information