ANSWER KEY 7 GAME THEORY, ECON 395
|
|
- Amy Higgins
- 5 years ago
- Views:
Transcription
1 ANSWER KEY 7 GAME THEORY, ECON 95 PROFESSOR A. JOSEPH GUSE 1 Gbbos.1 Recall the statc Bertrad duopoly wth homogeeous products: the frms ame prces smultaeously; demad for frm s product s a p f p < p j, s 0 f p > p j ad s a p / f p = p j. Margal costs are c < a. Show that the frms ca use trgger strateges that swtch forever to the stage-game Nash equlbrum after ay devato to susta the moopoly prce level a SPNE f ad oly f δ < 1. ANSWER. Let VNE represet the value of playg the stage game NE every perod for player. Sce the proft of each frm s zero every stage where they play the stage game NE, we have VNE 1 = V NE. Suppose that a gve stage, t, the two frms collude to maxmze total proft whch they splt. The maxmzato problem s max p a pp c where a p s the total demad for both frms output at prce p ad p c s the per ut proft. fd the frst order codto o the proft-maxmzg level of prce, p M, by takg the dervatve of proft ad settg t equal to zero: p M = [ a+c 5 a+c p ] Now use ths to calculate the total proft the frms ear whe they each set prce at p M. Call ths level of proft π M : π M = a p Mp M c = a a+c a+c = a c 4 c If both frms were to cooperatvely set ther prce to, p M, they would splt the demad half by assumpto the set-up ad therefore splt ths proft gag a c 8 each. Now cosder the most a frm could ga a sgle perod by devatg from the cooperatve prce level. If the other frm set ther prce equal to p M = a+c, the other frm could 1
2 PROFESSOR A. JOSEPH GUSE just udercut t, chargg p M ǫ ad ga the etre proft. Makg ǫ as small as possble, words, a devatg frm could ear as much as a c 4. Suppose that both frms adopt the followg grm trgger strategy: p t = { p M f p j t 1 = p M AND p t 1 = p M p B otherwse I words, play the cooperatve total proft maxmzg prce each perod as log as both players set that same prce the prevous perod. Otherwse set prce equal to the oe-shot Bertrad prce, p B, whch we kow from earler to be equal to c, the margal cost. To whether both frm adoptg ths strategy s a NE we eed to ask whether ether frm should devate. By cooperatg each frm wll ear π m / = a c 8 every perod. By devatg a frm wll ear a c 4 the perod they devate followed by 0 ever after. Hece devato from the grm trgger stragegy profle s worthwhle f PV dev PV coop a c 4 + δ0 1 δ a c 81 δ a c a c 1 δ 1 δ 1 1 δ Gbbos.15 Suppose there are frms a Courot olgopoly. Iverse demad s gve by PQ = a Q, where Q = =1 = q. Cosder the ftely repeated game based o ths stage game. a Whatsthelowestvalueofδ suchthatthefrmscausetrggerstrategestosustathe moopoly output level a subgame-perfect Nash equlbrum? How does the aswer vary wth, ad why? ANSWER We wll be terested the followg three strateges quattes ad assocated proft levels.
3 ANSWER KEY 7 GAME THEORY, ECON 95 Strategy Profle Descrpto Quatty Idvdual Proft All Frms Cooperate to Maxmze Proft q M = a c All Frms Play Courot Eqm stage game q C = a c Oe frm devates π M = a c 4 +1 π C = a c +1 q D = +1a c 4 π D = +1 a c 4 1 We eed to ask whether for a gve value of δ whether all frm playg grm trgger s a Nash Equlbrum the repeated game. I ths case grm trgger would mea each frm producg q M every perod as log as all frms dd ths the prevous perod ad producg q C ay perod where t was observed the prevous perod that someoe dd somethg other tha q M. Ths wll be a NE f PV colluso > PV devato π M 1 δ π D + δπ C 1 δ where colluso here meas playg the strategy ecouraged by the grm trgger strategy ad devatato meas takg advatage of the other players by maxmzg proft some perod. The preset value of colluto s the preset value of the fte stream of dvdual frm profts from playg the collusve strategy whe all other frms play t π M. The preset value of devato s the oe-tme proft of devatg π D the stage game plus the fte stream of subsequet profts from playg the ocooperatve courot equlbrum, π C. Pluggg the values we have for π M, π C ad π D we get 1 Dervg qm: Maxmze total proft, a Q cq w.r.t to Q. F.O.C. s Q = a c. So dustry total quatty to maxmze dustry total proft s Q M = a c. qm s just oe frm s share of QM. Dervg q C: See AK 1 Gbbos 1.4 Dervg q D: Suppose 1 frms all but frm cooperate by each producg q M; q D s the output level that wll maxmze s proft a sgle stage game. Hece t solves max q a c a c q q. F.O.C s a c a c q D.
4 4 PROFESSOR A. JOSEPH GUSE PV colluso > PV devato a c 41 δ δ +1 + δ δ a c + δ a c δ δ δ+1 4 +δ16 1 δ δ δ δ δ Note that s always egatve for all 1 sce the deomator s always egatve ad the umerator s always postve. Note also that the largest power the umeratve s O whle the the deomator we have O 4. Hece s a egatve umber approachg 0 as grows large. Whch meas that the mmum value of δ ecessary order to support colluso equlbrum s less tha 1 ad approaches 1 as grows large. b If δ s too small for the frms to use trgger strateges to susta the moopoly output, what s the most-proftable symmetrc subgame-perfect Nash equlbrum that ca be sustaed usg trgger strateges? OPTIONAL. Gbbos. Cosder a Courot duopoly operatg a market wth verse demad PQ = a Q, where Q = q 1 +q s the aggregate quatty o the market. Both frms have total costs c q = cq, but demad s ucerta: t s hgh a = a H wth probablty θ ad low a = a L wth probablty 1 θ. Furthermore, formato s asymetrc: frm 1 kows whether demad s hgh or lwo, but frm does ot. All of ths s commo kowledge. The two frms smulataeously chooose quattes. a What are the strategy spaces for the two frms. ANSWER. Frm 1: q 1 : {a L,a H } R Frm : q R + Sce Frm 1 observes the demad parameter Frms 1 s strategy s a fucto, mappg from the set of possble demad parameters, {a L,a H } to the postve real umbers feasble output levels. Sce there are oly two possble demad types, ths bols
5 ANSWER KEY 7 GAME THEORY, ECON 95 5 dow to Frm 1 s strategy beg a par or postve umbers - oe to be played f a = a L ad the other to be played f a = a H. Sce Frm observes ether the demad parameter or Frm 1 s acto, there s othg to make ts choce of cotget o; ts strategy s smply a choce of output quatty, q. b Make assumptos o a H, a L, θ, ad c such that all equlbrum quattes are postve. What s the Bayesa Nash equlbrum of ths game? ANSWER. If frm 1 s to playg a best respose agat a strategy of q by frm, the frm 1 should solve max q 1 a c q q 1 q 1 Frm 1 s best respose fucto ca be derved by wrtg dow the F.O.C. ad solvg for q 1 the usual way q 1q,a = a c q Frm s best respose fucto solves max q a H c q q 1 a H q θ H +a L c q 1 a L q q 1 θ H Note that the frst group of terms ths objectve fucto s the cotrbuto to F s expected proft whe demad s hgh ad F1 plays some strategy q 1 a H. Frm wo t kow whether demad s H or L, but wll kow that whe t s H that F1 wll plat q 1 a H. Hece ths frst group of terms s multpled by the probablty that demad s H, θ H. Dfferetatg w.r.t q gves us the followg F.O.C. a H q 1 a H θ H +a L q 1 a L 1 θ H c q To solve for the equlbrum we ca substtute the epresso we derved for F1 s BR futo to ths, whch yelds a H a H c q ah al θ H + θ H + a L a L c q 1 θ H c q 1 θ H c q a H θ H +a L 1 θ H c q q = a Hθ H +a L 1 θ H c q = Ea c
6 6 PROFESSOR A. JOSEPH GUSE Surprsgly?, ths meas that Frm s equlbrum strategy s to produce a output level as though t were a Courot Equlbrum ad demad s the expected demad. ote that Ea = a H θ H + a L 1 θ H. Sce Frm wll always play the average Courot quatty, Frm 1 wll wat to produce above ths quatty whe a = a H ad below ths quatty whe a = a L. Specfcally, q1a L = a L c a Hθ H +a L 1 θ H c q1a L = a L a H θ H a L 1 θ H c 6 q 1a L = a L Ea c 6 = a L Ea c Note that ths s less the F wll produce whe a+a L sce a L Ea c < Ea c. Smlarly, F1 s output whe t observes a = a H ca be calculated: q1a H = a H c a Hθ H +a L 1 θ H c q1a H = a H a H θ H a L 1 θ H c 6 q 1a H = a H Ea c 6 = a H Ea c Note that ths s less the F wll produce whe a+a L sce a H Ea c > Ea c. The aswer to the questo about what codtos the parameters have to meet order to guaratee that all quattes are postve s that we must have a L Ea c > 0 AND Ea c > 0 Notce that f ths codto hold the we see drectly that q 1 a L > 0. Also sce a H > a L, we kow that a L Ea c > 0 a H Ea c > 0 so q 1 a H > 0 as well. The secod codto guaratees that q > 0 Irocally, perhaps, Frm 1, who kows more tha Frm, wll ear less proft whe demad s low. Sce they wll face the same prce ad margal cost ad F wll produce more. 4 Gbbos.6 Cosder a frst-prce sealed-bd aucto whch the bdders valuatos are depedetly ad uformly dstrbuted o [0, 1]. Show that f there are bdders, the the strategy of bddg tmes oe s valuato s a symmetrc Bayesa ash equlbrum of ths aucto.
7 ANSWER KEY 7 GAME THEORY, ECON 95 7 ANSWER. Let v be the valuato of bder. To show that v s a symmetrc BNE, we eed to show that f all players except oe employ ths strategy the t s a best respose for the last oe. Suppose that all bdders j bd v j, bdder s problem s to maxmze expected utlty. maxeu b = Prw bv b+prlose b0 b Let s look more closely at Prw b Prw b = Prb > max {b j} j { } vj = Pr b > max j = Pr b > v 1 = Pr v 1 < b = j Pr &...&b > v 1 &b > v +1 &...&b > v &...&v 1 < b &b > v +1 < b &...&b > v < b v j < b Note the last step ca be take because each v j s a depedetly draw value whch meas the probablty of the jot statemet coceg each v j s equal to the product of each statemet. Usg the fact that each j s draw from the uform dstrbuto, we kow that, geeral Prv j < x = x, so we ca be eve more precse about Prw b: Prw b = j = b b Pluggg ths back to s objecve fucto ad dfferetatg w.r.t b, we get the followg frst order codto.
8 8 PROFESSOR A. JOSEPH GUSE b b v b b v b b b v b b v b b v b v b b b = v To be thorough, we should also check the secod-order codto SOC: d Eu b db d Eu b db / b b v b b v b [ [ b ] b ] b ] b b v b [ b v b [ v b b v b 1 ] Evaluatg ths last epressto at b = v we get
9 ANSWER KEY 7 GAME THEORY, ECON 95 9 d Eu b db v v v v v whch s true by our assumpto that v s draw o U0,1.
Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b
CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. August 2011
Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationMONOPOLISTIC COMPETITION MODEL
MONOPOLISTIC COMPETITION MODEL Key gredets Cosumer utlty: log (/ ) log (taste for varety of dfferetated goods) Produto of dfferetated produts: y (/ b) max[ f, ] (reasg returs/fxed osts) Assume that good,
More informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationChapter 3 Sampling For Proportions and Percentages
Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys
More informationLecture 3. Sampling, sampling distributions, and parameter estimation
Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called
More information1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.
Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationBayes (Naïve or not) Classifiers: Generative Approach
Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg
More informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationF. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties
進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem
More informationå 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018
Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of
More informationLogistic regression (continued)
STAT562 page 138 Logstc regresso (cotued) Suppose we ow cosder more complex models to descrbe the relatoshp betwee a categorcal respose varable (Y) that takes o two (2) possble outcomes ad a set of p explaatory
More information2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.
.5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely
More informationTHE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5
THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should
More informationThird 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 informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationarxiv:math/ v1 [math.gm] 8 Dec 2005
arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko
More informationRandom Variables and Probability Distributions
Radom Varables ad Probablty Dstrbutos * If X : S R s a dscrete radom varable wth rage {x, x, x 3,. } the r = P (X = xr ) = * Let X : S R be a dscrete radom varable wth rage {x, x, x 3,.}.If x r P(X = x
More information1 Solution to Problem 6.40
1 Soluto to Problem 6.40 (a We wll wrte T τ (X 1,...,X where the X s are..d. wth PDF f(x µ, σ 1 ( x µ σ g, σ where the locato parameter µ s ay real umber ad the scale parameter σ s > 0. Lettg Z X µ σ we
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More informationMean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationCHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and
CHAPTR 6 Secto 6.. a. We use the samle mea, to estmate the oulato mea µ. Σ 9.80 µ 8.407 7 ~ 7. b. We use the samle meda, 7 (the mddle observato whe arraged ascedg order. c. We use the samle stadard devato,
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationSampling 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 informationAlgorithms Theory, Solution for Assignment 2
Juor-Prof. Dr. Robert Elsässer, Marco Muñz, Phllp Hedegger WS 2009/200 Algorthms Theory, Soluto for Assgmet 2 http://lak.formatk.u-freburg.de/lak_teachg/ws09_0/algo090.php Exercse 2. - Fast Fourer Trasform
More informationTHE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE
THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationQualifying Exam Statistical Theory Problem Solutions August 2005
Qualfyg Exam Statstcal Theory Problem Solutos August 5. Let X, X,..., X be d uform U(,),
More information( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006
Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.
More information1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.
PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model
ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,
More informationChapter 14 Logistic Regression Models
Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as
More informationCIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights
CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationLecture 1 Review of Fundamental Statistical Concepts
Lecture Revew of Fudametal Statstcal Cocepts Measures of Cetral Tedecy ad Dsperso A word about otato for ths class: Idvduals a populato are desgated, where the dex rages from to N, ad N s the total umber
More information2SLS Estimates ECON In this case, begin with the assumption that E[ i
SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll
More informationBounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy
Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationCENTRE FOR ECONOMIC RESEARCH WORKING PAPER SERIES
CENTRE FOR ECONOMIC RESEARCH WORKING PAPER SERIES 2001 Mmum Wages for Roald McDoald Moopsoes: A Theory of Moopsostc Competto by V Bhaskar ad Ted To A Commet Frak Walsh, Uversty College Dubl WP01/18 September
More informationClass 13,14 June 17, 19, 2015
Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral
More informationLecture 02: Bounding tail distributions of a random variable
CSCI-B609: A Theorst s Toolkt, Fall 206 Aug 25 Lecture 02: Boudg tal dstrbutos of a radom varable Lecturer: Yua Zhou Scrbe: Yua Xe & Yua Zhou Let us cosder the ubased co flps aga. I.e. let the outcome
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationChapter 8. Inferences about More Than Two Population Central Values
Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha
More informationmeans the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.
9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,
More informationLecture Note to Rice Chapter 8
ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More information22 Nonparametric Methods.
22 oparametrc Methods. I parametrc models oe assumes apror that the dstrbutos have a specfc form wth oe or more ukow parameters ad oe tres to fd the best or atleast reasoably effcet procedures that aswer
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationExercises for Square-Congruence Modulo n ver 11
Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced
More informationMedian as a Weighted Arithmetic Mean of All Sample Observations
Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of
More informationInvestigation of Partially Conditional RP Model with Response Error. Ed Stanek
Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a
More informationLecture 2: Linear Least Squares Regression
Lecture : Lear Least Squares Regresso Dave Armstrog UW Mlwaukee February 8, 016 Is the Relatoshp Lear? lbrary(car) data(davs) d 150) Davs$weght[d]
More information{ }{ ( )} (, ) = ( ) ( ) ( ) 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 informationLecture 4 Sep 9, 2015
CS 388R: Radomzed Algorthms Fall 205 Prof. Erc Prce Lecture 4 Sep 9, 205 Scrbe: Xagru Huag & Chad Voegele Overvew I prevous lectures, we troduced some basc probablty, the Cheroff boud, the coupo collector
More information8.1 Hashing Algorithms
CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More information2. Independence and Bernoulli Trials
. Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationChapter 9 Jordan Block Matrices
Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationChapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance
Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss
More informationDescriptive Statistics
Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationMechanics of Materials CIVL 3322 / MECH 3322
Mechacs of Materals CVL / MECH Cetrods ad Momet of erta Calculatos Cetrods = A = = = A = = Cetrod ad Momet of erta Calculatos z= z A = = Parallel As Theorem f ou kow the momet of erta about a cetrodal
More informationLikewise, properties of the optimal policy for equipment replacement & maintenance problems can be used to reduce the computation.
Whe solvg a vetory repleshmet problem usg a MDP model, kowg that the optmal polcy s of the form (s,s) ca reduce the computatoal burde. That s, f t s optmal to replesh the vetory whe the vetory level s,
More information(b) By independence, the probability that the string 1011 is received correctly is
Soluto to Problem 1.31. (a) Let A be the evet that a 0 s trasmtted. Usg the total probablty theorem, the desred probablty s P(A)(1 ɛ ( 0)+ 1 P(A) ) (1 ɛ 1)=p(1 ɛ 0)+(1 p)(1 ɛ 1). (b) By depedece, the probablty
More informationd dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin
Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace
More informationHomework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015
Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationPART ONE. Solutions to Exercises
PART ONE Soutos to Exercses Chapter Revew of Probabty Soutos to Exercses 1. (a) Probabty dstrbuto fucto for Outcome (umber of heads) 0 1 probabty 0.5 0.50 0.5 Cumuatve probabty dstrbuto fucto for Outcome
More information1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.
CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More information6.867 Machine Learning
6.867 Mache Learg Problem set Due Frday, September 9, rectato Please address all questos ad commets about ths problem set to 6.867-staff@a.mt.edu. You do ot eed to use MATLAB for ths problem set though
More informationNATIONAL SENIOR CERTIFICATE GRADE 11
NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 007 MARKS: 50 TIME: 3 hours Ths questo paper cossts of 9 pages, a sheet of graph paper ad a -page formula sheet. Mathematcs/P INSTRUCTIONS AND INFORMATION
More information