LET G = (V (G), E(G)) be a graph with vertex
|
|
- Amy Ramsey
- 5 years ago
- Views:
Transcription
1 O Maximal Icidece Eergy of Graphs with Give Coectivity Qu Liu a,b Abstract Let G be a graph of order. The icidece eergy IE(G) of graph G, IE for short, is defied as the sum of all sigular values of its icidece matrix. I this paper, we determie the maximal icidece eergy IE(G) amog all coected graphs with the coectivity κ ad edge coectivity κ ad K κ (K 1 K κ 1) has the maximal icidece eergy. Idex Terms Sigless Laplacia matrix, Icidece eergy, Coectivity, Edge coectivity I. INTRODUCTION LET G = (V (G), E(G)) be a graph with vertex set V (G) = {v 1, v,..., v } ad edge set E(G) = {e 1, e,..., e m }, the adjacecy matrix A(G) = (a ij ) of G is a (vertex-vertex) symmetric matrix with a ij = 1 if v i ad v j are adjacet, ad a ij = 0 otherwise. Deote the degree of vertex v i by d(v i ), the sigless Laplacia matrix Q(G) of G is defied as Q(G) = D(G) + A(G), where D(G) = diag(d(v 1 )), d(v ),..., d(v )) is the diagoal matrix of degree of G. Sice Q(G) are symmetric matrix, their eigevalues are real umbers. So, we ca assume that q 1 (G) q (G)... q (G) are the sigless Laplacia eigevalues of G. Let I(G) be the (vertex-edge) icidece matrix of the graph G, the (i, j)-etry of I(G) is 0 if v i is ot icidet with e j ad 1 if v i is icidet with e j. Jooyadeh et al. [1] itroduced the icidece eergy IE of G, which is defied as the sum of the sigular values of the icidece matrix of G. Gutma et al. [] showed that IE = IE(G) = qi (G). Some basic properties of IE may be foud i [1 3]. Let G be a coected graph with vertices ad m edges. Let S(G) be the subdivisio graph of G, that is, S(G) is obtaied from G by isertig a ew vertex i each edge. Clearly, S(G) is a bipartite graph with + m vertices ad m edges. We deote σ(g, x) the characteristic polyomial det(xi A(G)) of G. It is well kow [4] that if G is a bipartite graph, the σ(g, x) = det(xi A(G)) = / ( 1) t b(g, t)x t, t=0 Mauscript received December 10, 017; revised September 3, 018. This work was supported by the Natioal Natural Sciece Foudatio of Chia (Nos , ), the Research Foudatio of the Higher Educatio Istitutios of Gasu Provice, Chia (018A-093) ad the Sciece ad Techology Pla of Gasu Provice(18JR3RG06). Q. Liu is with (a) School of Computer Sciece, Fuda Uiversity, Shaghai 00433, Chia ad (b) School of Mathematics ad Statistics, Hexi Uiversity, Gasu, Zhagye, , P.R. Chia. liuqu@fuda.edu.c. (1) where b(g, 0) = 1 ad b(g, t) 0 for all t = 1,,..., /. This expressio for σ(g, x) iduce a quasi-order relatio (i.e. reflexive ad trasitive relatio) o the set of all bipartite graphs with vertices: If G 1 ad G are bipartite graphs with characteristic polyomials i the form G 1 G b(g 1, t) b(g, t) for all t = 0, 1,,..., /. If G 1 G ad there exist t such that b(g 1, t) > b(g, t), the we write G 1 G. Gutma [5] itroduced this quasi-order relatio i order to compare the eergies of a pair of graphs. It is kow [5, 6] that for the bipartite graph S(G), E(S(G)) ca be also expressed as the Coulso itegral formula E(S(G)) = π + 0 Thus for m, we have [7] IE(G) = 1 π + where p i (G) = ( 1) i b i (S(G)). 0 (+m)/ 1 x I[1 + b i (S(G))x i ]dx. () 1 x I[1 + ( 1) i p i (G)x i ]dx, (3) From Eq.() ad Eq.(3) we kow that for two bipartite graphs S(G 1 ) ad S(G ), S(G 1 ) S(G ) IE(G 1 ) IE(G ), S(G 1 ) S(G ) IE(G 1 ) < IE(G ). For two oadjacet vertices v i ad v j, we use G + e to deote the graph obtaied by isertig a ew edge e = v i v j i G. For two vertex disjoit graph G 1 ad G, we deote by G 1 G the graph which cosists of two coected compoets G 1 ad G. The joi of G 1 ad G, deoted by G 1 G, is the graph with vertex set V (G 1 ) V (G ) ad edge set E(G 1 ) E(G ) {u i u j : u i V (G 1 ), u j V (G )}. The coectivity κ(g) of a graph G is the miimum umber of vertices whose removal from G yields a discoected graph or a trivial graph. The edge-coectivity κ is defied aalogously. It is both iterestig ad sigificat to determie the graph with extremal eergies amog a give class of graphs. Numerous results o this subject have bee put forward, for details see [8-13]. Zhou ad Triajstić [9] determied the extremal Kirchhoff idex of graphs with respect to give matchig umber. I [10], Xu characterized the extremal Laplacia-eergy-like with give matchig umber. Rojo [8] obtaied the extremal icidece eergy with respect to (Advace olie publicatio: 7 November 018)
2 the coectivity. I [14], Hu et al. determied the maximal eergy amog all subdivisios of graphs with vertices ad chromatic umber k. Zhag [18] characterize the graphs with the maximum icidece eergies amog all graphs with give chromatic umber ad give pedet vertex umber. Ispired by those works, i this paper, we determie the maximal icidece eergy amog all graphs with vertices ad the coectivity κ ad edge-coectivity κ. ( i + s x) ( s j,i j ( i + s x))]. Proof Sice the sum of all etries o every row of sigless Laplacia matrix of K is ( 1), (4) implies that T Q (x) =, usig Theorem.1, we ca get it immediately. x 1) III. RESULTS II. THE SIGNLESS LAPLACIAN CHARACTERISTIC POLYNOMIAL OF G 1 (G G 3 ) FOR REGULAR GRAPH I this sectio, we determie the sigless Laplacia characteristic polyomials of G = G 1 (G G 3 ) with the help of the coroal of a matrix. The M-coroal T M (x) of a matrix M is defied [15, 16] to be the sum of the etries of the matrix (xi M) 1, that is T M (x) = j T (xi M) 1 j, where j deotes the colum vector of dimesio with all the etries equal oe. It is well kow [15, P ropositio] that, if M is a matrix with each row sum equal to a costat t, the T M (x) = x t. (4) Theorem.1 Let G i (i = 1,, 3) be three graphs o i vertices. Also let T Qi (λ)(i = 1,, 3) be the Q i -coroal of G i. The the sigless Laplacia characteristic polyomial of the matrix Q(G 1 (G G 3 )) is P Q (G 1 (G G 3 )) = P Q (G 1, x 3 )P Q (G, x 1 )P Q (G 3, x 1 ) (1 T Q(G3)(x 1 )T Q(G1)(x 3 ) T Q(G)(x 1 )T Q(G1)(x 3 )). Proof With a proper labelig of vertices, the sigless Laplacia characteristic polyomial of Q(G) = Q(G 1 (G G 3 )) is give by Let M = xi 1 Q(G 1 ) + 3 )I 1, N = xi Q(G ) 1 I, T = xi 3 Q(G 3 ) 1 I 3, the P Q (G) where B = ( = det M j 1 j 1 3 j 1 N 0 3 j T = det(xi 3 Q(G 3 ) 1 I 3 )det(b) = P Q(G3)(x 1 )det(b), ( ) ( ) M j 1 j1 3 j 1 N 0 3 ((x 1 )I 3 Q(G 3 )) ( ) 1 j is the Schur complemet of λi 3 Q(G 3 ) 1 I 3. Thus the result follows. Corollary. Let G = K s (K 1 K ), where K s, K i (i = 1, ) deote the complete graph o s ad i vertices, respectively. The the characteristic polyomial of the sigless Laplacia matrix of G is P Q (G, x) = (x + ) s 1 (x s i + ) i 1 [( + s x) ) First, we defie the relatio (,, ) as follows. Defiitio 3.1. ([13]) We say p is partial larger tha q if p > q, deoted by p q. Similarly, we have p q, p q, p q. Defiitio 3.. ([13]) Let p(x) = i=0 p ix i ad q(x) = i=0 q ix i. If p i q i (resp. p i q i )for each 0 i, the we call p(x) q(x) (resp. p(x) q(x)). If p(x) q(x)(resp. p(x) q(x)), ad there exist a j {0, 1,,..., } such that p i q i (resp. p j q j ), we call p(x) q(x)(resp. p(x) q(x)). By the defiitio above, the followig result is immediate. Lemma 3.3 ([14]) Suppose a i b i 0 for i = 1,,...,. The (x a i ) (x b i ), furthermore, if there exist a j {1,,..., } such that a j > b j, the (x a i ) (x b i ). Theorem 3.4 Let, a ad k be three positive itegers ad a k. The (x +3) k (x k a+) a 1 (x +a+) k a 1. Proof Note that a k ad 1 > a a+k. By Lemma 3.3, (x +3) k (x k a+) a 1 (x +a+) k a 1. The result follows. Theorem 3.5 Let, a i ad b i be positive itegers ad a 1 a, where s = a 1 + a. If b 1 = a 1, b = a +, the (+s x) (b i +s x ) sb j ( t,i j (+s x) (a i +s x ) sa j ( Proof Note that (b i +s x ))),i j (a 1 + s x)(a + s x) = x (a 1 + a + s )x + 4a 1 a + s(a 1 + a ) 4(a 1 + a ) + (s ). (a i +s x ))). (Advace olie publicatio: 7 November 018)
3 ad So (b 1 + s x)(b + s x) = = x (b 1 + b + s )x + 4b 1 b + s(b 1 + b ) 4(b 1 + b ) + (s ). (b 1 +s x)(b +s x)a 1 +s x)(a +s x) Agai, ad Thus = 8s(a 1 a ). (5) ( sa 1 )(a + s x ) + ( sa )(a 1 + s x ) = s(a 1 + a )x 4sa 1 a + s(a 1 + a ) s (a 1 + a ) ( sb 1 )(b + s x ) + ( sb )(b 1 + s x ) = s(b 1 + b )x 4sb 1 b + s(b 1 + b ) s (b 1 + b ). [( sb 1 )(b + s x ) + ( sb )(b 1 + s x )] [( sa 1 )(a + s x ) + ( sa )(a 1 + s x )] Hece by (5) ad (6) ( + s x) + s x) = 8s(a 1 a ). (6) (b i + s x ) (a i + s x ) = ( + s x)[ 8s(a 1 a )] (7) + (sb j (sa j Hece, by (7) ad (8),,i j,i j (b i + s x)) (a i + s x)) = [ 8s(a 1 a )]. (8) [( + s x) sb j (,i j [( + s x) sa j ( t,i j (b i + s x ) (b i + s x )))] (a i + s x ) (a i + s x )))] = ( + s 1 x + a i )[ 8s(a 1 a )]. Thus by Defiitio 3. ( + s x) sb j (,i j ( + s x) sa j (,i j (b i + s x ) (b i + s x ))) (a i + s x ) (a i + s x ))). Hece we have fiished the proof of the Theorem. Lemma 3.6([16]) Let G be a o-complete coected graph of order ad e E(G). The σ(q(g + e), x) = det(xi Q(G + e)) det(xi Q(G)) = σ(q(g), x), where G deote the complemet of a graph G. Lemma 3.7([17]) Let G be a graph with vertices ad m edges. The σ(s(g), x) = x m σ(q(g), x ) = x m det(x I Q(G)). Now we preset our mai result. Theorem 3.8 Let G be a coected graph with vertices ad coectivity κ. The σ(q(g), x) (x + ) κ 1 (x + 3) κ [( + κ x)(κ x)( 4 κ x) κ( κ 4 x) κ( κ 1)(κ x)]. The equality holds if ad oly if G = K κ (K 1 K κ 1 ). Proof Let G 0 be a graph havig the maximal coefficiets of the sigless Laplacia characteristic polyomial amog all coected graphs of order with coectivity κ. The there is a vertex subset X 0 V (G 0 ) ad V 0 = κ such that G 0 V 0 = G 1 G... G t, where G 1, G,..., G t (t ) coected compoets of G 0 V 0. By Lemma 3.6, t =, G 1 ad G ad G[U] are complete, ad each vertex of G 1 ad G is adjacet to each vertex x i V 0. Let i = G i for i = 1,. The G 0 = K κ (K 1 K ) ad 1 + = κ. Assume that 1. By Corollary., P Q (G 0, x) = (x + ) κ 1 [( + κ x) κ j,i j (x κ i + ) i 1 ( i + κ x) ( i + κ x))]. If 1 κ, the by Lemma 3.4, (x +3) κ (x κ a+) a 1 (x +a+) κ a 1. (Advace olie publicatio: 7 November 018)
4 By Theorem 3.5 ( + κ x)(κ x)( 4 κ x) κ( κ 4 x) κ( κ 1)(κ x)] ( + κ x) (a i + κ x ) κa j (,i j (a i + κ x ))), where b 1 = 1, b = = κ 1. Hece, we fid the coefficiets of P Q (G 0, x) with 1 + = κ ad 1 is maximum if ad oly if 1 = 1, = κ 1. It follows that G 0 = K κ (K 1 K κ 1 ), the σ(q(g 0 ), x) (x + ) κ 1 (x + 3) κ [( + κ x)(κ x)( 4 κ x) κ( κ 4 x) κ( κ 1)(κ x)]. This completes the proof of Theorem. Lemma 3.9([14]) Let G 1 ad G be two bipartite graphs with 1 ad vertices, respectively. For ay two positive itegers p 1 ad p satisfyig 1 + p 1 = + p, the x p1 σ(g 1, x) x p σ(g, x) E(G 1 ) E(G ); x p1 σ(g 1, x) x p σ(g, x) E(G 1 ) E(G ). By Lemma 3.7 ad Theorem 3.8 ad Lemma3.9, the followig result is obvious. Theorem 3.10 Let G be a simple graph of order whose coectivity is κ. The IE(G) IE(K κ (K 1 K κ 1 )), with equality holds if ad oly if G = K κ (K 1 K κ 1 ). Theorem 3.11 Let G be a coected graph with vertices ad edge coectivity κ. The σ(q(g), x) (x + ) κ 1 (x + 3) κ [( + κ x)(κ x)( 4 κ x) κ ( κ 4 x) κ ( κ 1)(κ x)]. The equality holds if ad oly if G = K κ (K 1 K κ 1). Proof Let G be a coected graph of order with coectivity κ ad edge-coectivity κ, the κ κ. Note that (x + ) κ κ (x + 3) κ κ ad [( + κ x)(κ x)( 4 κ x) κ( κ 4 x) κ( κ 1)(κ x)] [( + κ x)(κ x)( 4 κ x) κ ( κ 4 x) κ ( κ 1)(κ x)]. By Theorem 3.8, σ(q(g), x) (x +) κ 1 (x +3) κ [(+κ x) (κ x)( 4 κ x) κ( κ 4 x) κ( κ 1)(κ x)] (x +) κ 1 (x +3) κ [(+κ x)(κ x) ( 4 κ x) κ ( κ 4 x) κ ( κ 1)(κ x)]. Ad the equality holds if ad oly if G = K κ (K 1 K κ 1 ) ad κ = κ, that is G = K κ (K 1 K κ 1). The result follows. Similarly Theorem 3.10, we get the followig result. Theorem 3.1 Let G be a simple graph of order whose edge coectivity is κ. The IE(G) IE(K κ (K 1 K κ 1), with equality holds if ad oly if G = K κ (K 1 K κ 1). IV. CONCLUSION I this paper, we use quasi-order relatio to compare the icidece eergy of two graphs with respect to the coectivity ad edge coectivity ad further obtai the maximal icidece eergy amog all coected graphs with give coectivity ad edge coectivity. ACKNOWLEDGMENT The author is very grateful to the aoymous referees for their carefully readig the paper ad for costructive commets ad suggestios which have improved this paper. REFERENCES [1] M. Jooyadeh, D. Kiai, ad M. Mirzakhah, Icidece eergy of a graph, MATCH. Commu. Math. Comput. Chem., vol. 6, o. 3, pp , 009. [] I. Gutma, D. Kiai, ad M. Mirzakhah, O icidece eergy of graphs, MATCH. Commu. Math. Comput. Chem., vol. 6, o. 3, pp , 009. [3] I. Gutma, D. Kiai, M.Mirzakhah ad B.Zhou, O icidece eergy of a graph, Liear Algebra Appl., vol. 431, o. 8, pp , 009. [4] D.M. Cvetkovic, M. Doob, H. Sachs, Spectra of Graphs, Academic Press, New York, [5] I. Gutma, Acyclic systems with extremal Huckel π-electro eergy, Theoret. Chim. Acta (Berli), vol. 45, o., pp , [6] I. Gutma, O. E. Polasky, Mathematicl Cocepts i Orgaic Chemistry, Spriger, Berli, [7] N. Biggs, Algebraic Graph Theory, d ed, Cambridge, Cambridge Uiversity Press, [8] O. Rojo, E. Lees, A sharp upper boud o the icidece eergy of graphs i terms of coectivity, Liear Algebra Appl., vol. 438, o. 3, pp , 013. [9] B. Zhou ad N. Triajstić, The Kirchhoff idex ad the matchig umber, It. J. of Quatum Chem., vol. 109, o. 13, pp , 009. [10] K.X. Xu, K.C. Das, Extremal Laplacia-eergy-like ivariat of graphs with give matchig umber, Electro Joural of Liear Alg. Appl., vol. 6, o. 1, pp , 013. [11] G. Zhog, M.X. Yi, ad J. Huag, Numerical method for solvig fractioal covectio diffusio equatios with time-space variable coefficiets, IAENG Iteratioal Joural of Applied Mathematics, vol. 48, o. 1, pp. 6-66, 018. [1] M. Asgari, Numerical solutio for solvig a system of fractioal itegro-differetial equatios, IAENG Iteratioal Joural of Applied Mathematics, vol. 45, o., pp , 015. [13] W. Qiu, W.G. Ya, Coefficiets of Laplacia characteristi polyomials of graphs, Liear Algebra Appl., 436 (01), vol. 38, o. 4, pp , 015. [14] M.L. Hu, W.G. Ya, W. Qiu, Maximal eergy of subdivisio of graphs with a fixed chromatic umber, Bull. Malay. Math. Sci. Soc., vol. 38, o. 4, pp , 015. (Advace olie publicatio: 7 November 018)
5 [15] S.Y. Cui, G.X. Tia, The spectrum ad the sigless Laplacia spectrum of coroae, Liear Algebra Appl., vol. 437, o. 7, pp , 01. [16] C. McLema, E. McNicholas, Spectra of coroae, Liear Algebra Appl., vol. 435, o. 5, pp , 011. [17] S. Shioda, O the characteristic polyomial of the adjacecy matrix of the subdivisio graph of a graph, Discrete Appl. Math., vol., o. 4, pp , [18] J.B. Zhag, H.B. Zhag ad X.D. Liu, Graphs with extremal icidece eergy, Filomat, vol. 9, o. 6, pp , 015. (Advace olie publicatio: 7 November 018)
Research Article The Average Lower Connectivity of Graphs
Applied Mathematics, Article ID 807834, 4 pages http://dx.doi.org/10.1155/2014/807834 Research Article The Average Lower Coectivity of Graphs Ersi Asla Turgutlu Vocatioal Traiig School, Celal Bayar Uiversity,
More informationA New Constructive Proof of Graham's Theorem and More New Classes of Functionally Complete Functions
A New Costructive Proof of Graham's Theorem ad More New Classes of Fuctioally Complete Fuctios Azhou Yag, Ph.D. Zhu-qi Lu, Ph.D. Abstract A -valued two-variable truth fuctio is called fuctioally complete,
More informationApplied Mathematics Letters
Applied Mathematics Letters 23 (2010) 286 290 Contents lists available at ScienceDirect Applied Mathematics Letters journal homepage: wwwelseviercom/locate/aml The number of spanning trees of a graph Jianxi
More informationAverage Distance and Vertex-Connectivity
Average Distace ad Vertex-Coectivity Peter Dakelma, Simo Mukwembi, Heda C. Swart School of Mathematical Scieces Uiversity of KwaZulu-Natal, Durba, 4041 South Africa March 17, 013 Abstract The average distace
More informationThe Limit of a Sequence (Brief Summary) 1
The Limit of a Sequece (Brief Summary). Defiitio. A real umber L is a it of a sequece of real umbers if every ope iterval cotaiig L cotais all but a fiite umber of terms of the sequece. 2. Claim. A sequece
More informationEXERCISE - BINOMIAL THEOREM
BINOMIAL THOEREM / EXERCISE - BINOMIAL THEOREM LEVEL I SUBJECTIVE QUESTIONS. Expad the followig expressios ad fid the umber of term i the expasio of the expressios. (a) (x + y) 99 (b) ( + a) 9 + ( a) 9
More informationCombining imperfect data, and an introduction to data assimilation Ross Bannister, NCEO, September 2010
Combiig imperfect data, ad a itroductio to data assimilatio Ross Baister, NCEO, September 00 rbaister@readigacuk The probability desity fuctio (PDF prob that x lies betwee x ad x + dx p (x restrictio o
More informationON DIFFERENTIATION AND HARMONIC NUMBERS
ON DIFFERENTIATION AND HARMONIC NUMBERS ERIC MORTENSON Abstract. I a paper of Adrews ad Uchimura [AU, it is show how differetiatio applied to hypergeometric idetities produces formulas for harmoic ad q-harmoic
More informationLecture 9: The law of large numbers and central limit theorem
Lecture 9: The law of large umbers ad cetral limit theorem Theorem.4 Let X,X 2,... be idepedet radom variables with fiite expectatios. (i) (The SLLN). If there is a costat p [,2] such that E X i p i i=
More informationNORMALIZATION OF BEURLING GENERALIZED PRIMES WITH RIEMANN HYPOTHESIS
Aales Uiv. Sci. Budapest., Sect. Comp. 39 2013) 459 469 NORMALIZATION OF BEURLING GENERALIZED PRIMES WITH RIEMANN HYPOTHESIS We-Bi Zhag Chug Ma Pig) Guagzhou, People s Republic of Chia) Dedicated to Professor
More informationSequences and Series
Sequeces ad Series Matt Rosezweig Cotets Sequeces ad Series. Sequeces.................................................. Series....................................................3 Rudi Chapter 3 Exercises........................................
More informationMA Lesson 11 Section 1.3. Solving Applied Problems with Linear Equations of one Variable
MA 15200 Lesso 11 Sectio 1. I Solvig Applied Problems with Liear Equatios of oe Variable 1. After readig the problem, let a variable represet the ukow (or oe of the ukows). Represet ay other ukow usig
More informationNeighboring Optimal Solution for Fuzzy Travelling Salesman Problem
Iteratioal Joural of Egieerig Research ad Geeral Sciece Volume 2, Issue 4, Jue-July, 2014 Neighborig Optimal Solutio for Fuzzy Travellig Salesma Problem D. Stephe Digar 1, K. Thiripura Sudari 2 1 Research
More informationMath 312, Intro. to Real Analysis: Homework #4 Solutions
Math 3, Itro. to Real Aalysis: Homework #4 Solutios Stephe G. Simpso Moday, March, 009 The assigmet cosists of Exercises 0.6, 0.8, 0.0,.,.3,.6,.0,.,. i the Ross textbook. Each problem couts 0 poits. 0.6.
More informationBayes Estimator for Coefficient of Variation and Inverse Coefficient of Variation for the Normal Distribution
Iteratioal Joural of Statistics ad Systems ISSN 0973-675 Volume, Number 4 (07, pp. 7-73 Research Idia Publicatios http://www.ripublicatio.com Bayes Estimator for Coefficiet of Variatio ad Iverse Coefficiet
More informationA New Approach to Obtain an Optimal Solution for the Assignment Problem
Iteratioal Joural of Sciece ad Research (IJSR) ISSN (Olie): 231-7064 Idex Copericus Value (2013): 6.14 Impact Factor (2015): 6.31 A New Approach to Obtai a Optimal Solutio for the Assigmet Problem A. Seethalakshmy
More information5. Best Unbiased Estimators
Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai
More informationRafa l Kulik and Marc Raimondo. University of Ottawa and University of Sydney. Supplementary material
Statistica Siica 009: Supplemet 1 L p -WAVELET REGRESSION WITH CORRELATED ERRORS AND INVERSE PROBLEMS Rafa l Kulik ad Marc Raimodo Uiversity of Ottawa ad Uiversity of Sydey Supplemetary material This ote
More informationCAUCHY'S FORMULA AND EIGENVAULES (PRINCIPAL STRESSES) IN 3-D
GG303 Lecture 19 11/5/0 1 CAUCHY'S FRMULA AN EIGENVAULES (PRINCIPAL STRESSES) IN 3- I II Mai Topics A Cauchy s formula Pricipal stresses (eigevectors ad eigevalues) Cauchy's formula A Relates tractio vector
More informationDepartment of Mathematics, S.R.K.R. Engineering College, Bhimavaram, A.P., India 2
Skewess Corrected Cotrol charts for two Iverted Models R. Subba Rao* 1, Pushpa Latha Mamidi 2, M.S. Ravi Kumar 3 1 Departmet of Mathematics, S.R.K.R. Egieerig College, Bhimavaram, A.P., Idia 2 Departmet
More informationSection Mathematical Induction and Section Strong Induction and Well-Ordering
Sectio 4.1 - Mathematical Iductio ad Sectio 4. - Strog Iductio ad Well-Orderig A very special rule of iferece! Defiitio: A set S is well ordered if every subset has a least elemet. Note: [0, 1] is ot well
More informationAUTOMATIC GENERATION OF FUZZY PAYOFF MATRIX IN GAME THEORY
AUTOMATIC GENERATION OF FUZZY PAYOFF MATRIX IN GAME THEORY Dr. Farha I. D. Al Ai * ad Dr. Muhaed Alfarras ** * College of Egieerig ** College of Coputer Egieerig ad scieces Gulf Uiversity * Dr.farha@gulfuiversity.et;
More informationRandom Sequences Using the Divisor Pairs Function
Radom Sequeces Usig the Divisor Pairs Fuctio Subhash Kak Abstract. This paper ivestigates the radomess properties of a fuctio of the divisor pairs of a atural umber. This fuctio, the atecedets of which
More information1 Random Variables and Key Statistics
Review of Statistics 1 Radom Variables ad Key Statistics Radom Variable: A radom variable is a variable that takes o differet umerical values from a sample space determied by chace (probability distributio,
More informationMaximum Empirical Likelihood Estimation (MELE)
Maximum Empirical Likelihood Estimatio (MELE Natha Smooha Abstract Estimatio of Stadard Liear Model - Maximum Empirical Likelihood Estimator: Combiatio of the idea of imum likelihood method of momets,
More informationA New Second-Order Corrector Interior-Point Algorithm for P (κ)-lcp
Filomat 3:0 07, 6379 639 https://doi.org/0.9/fil70379k Published by Faculty of Scieces ad Mathematics, Uiversity of Niš, Serbia Available at: http://www.pmf.i.ac.rs/filomat A New Secod-Order Corrector
More informationChapter 8: Estimation of Mean & Proportion. Introduction
Chapter 8: Estimatio of Mea & Proportio 8.1 Estimatio, Poit Estimate, ad Iterval Estimate 8.2 Estimatio of a Populatio Mea: σ Kow 8.3 Estimatio of a Populatio Mea: σ Not Kow 8.4 Estimatio of a Populatio
More informationGame Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India July 2012
Game Theory Lecture Notes By Y. Narahari Departmet of Computer Sciece ad Automatio Idia Istitute of Sciece Bagalore, Idia July 01 Chapter 4: Domiat Strategy Equilibria Note: This is a oly a draft versio,
More informationNew Distance and Similarity Measures of Interval Neutrosophic Sets
New Distace ad Similarity Measures of Iterval Neutrosophic Sets Said Broumi Abstract: I this paper we proposed a ew distace ad several similarity measures betwee iterval eutrosophic sets. Keywords: Neutrosophic
More informationx satisfying all regularity conditions. Then
AMS570.01 Practice Midterm Exam Sprig, 018 Name: ID: Sigature: Istructio: This is a close book exam. You are allowed oe-page 8x11 formula sheet (-sided). No cellphoe or calculator or computer is allowed.
More informationSummary. Recap. Last Lecture. .1 If you know MLE of θ, can you also know MLE of τ(θ) for any function τ?
Last Lecture Biostatistics 60 - Statistical Iferece Lecture Cramer-Rao Theorem Hyu Mi Kag February 9th, 03 If you kow MLE of, ca you also kow MLE of τ() for ay fuctio τ? What are plausible ways to compare
More informationSubject CT5 Contingencies Core Technical. Syllabus. for the 2011 Examinations. The Faculty of Actuaries and Institute of Actuaries.
Subject CT5 Cotigecies Core Techical Syllabus for the 2011 Examiatios 1 Jue 2010 The Faculty of Actuaries ad Istitute of Actuaries Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical
More informationResearch Article The Probability That a Measurement Falls within a Range of n Standard Deviations from an Estimate of the Mean
Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 70806, 8 pages doi:0.540/0/70806 Research Article The Probability That a Measuremet Falls withi a Rage of Stadard Deviatios
More information11.7 (TAYLOR SERIES) NAME: SOLUTIONS 31 July 2018
.7 (TAYLOR SERIES NAME: SOLUTIONS 3 July 08 TAYLOR SERIES ( The power series T(x f ( (c (x c is called the Taylor Series for f(x cetered at x c. If c 0, this is called a Maclauri series. ( The N-th partial
More informationA Note About Maximum Likelihood Estimator in Hypergeometric Distribution
Comuicacioes e Estadística Juio 2009, Vol. 2, No. 1 A Note About Maximum Likelihood Estimator i Hypergeometric Distributio Ua ota sobre los estimadores de máxima verosimilitud e la distribució hipergeométrica
More informationINTERVAL GAMES. and player 2 selects 1, then player 2 would give player 1 a payoff of, 1) = 0.
INTERVAL GAMES ANTHONY MENDES Let I ad I 2 be itervals of real umbers. A iterval game is played i this way: player secretly selects x I ad player 2 secretly ad idepedetly selects y I 2. After x ad y are
More informationSTAT 135 Solutions to Homework 3: 30 points
STAT 35 Solutios to Homework 3: 30 poits Sprig 205 The objective of this Problem Set is to study the Stei Pheomeo 955. Suppose that θ θ, θ 2,..., θ cosists of ukow parameters, with 3. We wish to estimate
More informationNotes on Expected Revenue from Auctions
Notes o Epected Reveue from Auctios Professor Bergstrom These otes spell out some of the mathematical details about first ad secod price sealed bid auctios that were discussed i Thursday s lecture You
More information2.6 Rational Functions and Their Graphs
.6 Ratioal Fuctios ad Their Graphs Sectio.6 Notes Page Ratioal Fuctio: a fuctio with a variable i the deoiator. To fid the y-itercept for a ratioal fuctio, put i a zero for. To fid the -itercept for a
More information43. A 000 par value 5-year bod with 8.0% semiaual coupos was bought to yield 7.5% covertible semiaually. Determie the amout of premium amortized i the 6 th coupo paymet. (A).00 (B).08 (C).5 (D).5 (E).34
More informationEstimation of Population Variance Utilizing Auxiliary Information
Iteratioal Joural of Statistics ad Systems ISSN 0973-675 Volume 1, Number (017), pp. 303-309 Research Idia Publicatios http://www.ripublicatio.com Estimatio of Populatio Variace Utilizig Auxiliary Iformatio
More informationON THE MAXIMUM AND MINIMUM SIZES OF A GRAPH
Discussiones Mathematicae Graph Theory 37 (2017) 623 632 doi:10.7151/dmgt.1941 ON THE MAXIMUM AND MINIMUM SIZES OF A GRAPH WITH GIVEN k-connectivity Yuefang Sun Department of Mathematics Shaoxing University
More informationSELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION
1 SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION Hyue-Ju Kim 1,, Bibig Yu 2, ad Eric J. Feuer 3 1 Syracuse Uiversity, 2 Natioal Istitute of Agig, ad 3 Natioal Cacer Istitute Supplemetary
More information1 The Power of Compounding
1 The Power of Compoudig 1.1 Simple vs Compoud Iterest You deposit $1,000 i a bak that pays 5% iterest each year. At the ed of the year you will have eared $50. The bak seds you a check for $50 dollars.
More informationSUPPLEMENTAL MATERIAL
A SULEMENTAL MATERIAL Theorem (Expert pseudo-regret upper boud. Let us cosider a istace of the I-SG problem ad apply the FL algorithm, where each possible profile A is a expert ad receives, at roud, a
More informationThe material in this chapter is motivated by Experiment 9.
Chapter 5 Optimal Auctios The material i this chapter is motivated by Experimet 9. We wish to aalyze the decisio of a seller who sets a reserve price whe auctioig off a item to a group of bidders. We begi
More informationSolution to Tutorial 6
Solutio to Tutorial 6 2012/2013 Semester I MA4264 Game Theory Tutor: Xiag Su October 12, 2012 1 Review Static game of icomplete iformatio The ormal-form represetatio of a -player static Bayesia game: {A
More informationHow the Default Probability is Defined by the CreditRisk+Model?
Iteratioal Joural of Global Eergy Marets ad Fiace, 28, Vol, No, 2-25 vailable olie at http://pubssciepubcom/igefm///4 Sciece ad Educatio Publishig DOI:269/igefm---4 How the Default Probability is Defied
More informationStatistics for Economics & Business
Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie
More informationPositivity Preserving Schemes for Black-Scholes Equation
Research Joural of Fiace ad Accoutig IN -97 (Paper) IN -7 (Olie) Vol., No.7, 5 Positivity Preservig chemes for Black-choles Equatio Mohammad Mehdizadeh Khalsaraei (Correspodig author) Faculty of Mathematical
More informationMixed and Implicit Schemes Implicit Schemes. Exercise: Verify that ρ is unimodular: ρ = 1.
Mixed ad Implicit Schemes 3..4 The leapfrog scheme is stable for the oscillatio equatio ad ustable for the frictio equatio. The Euler forward scheme is stable for the frictio equatio but ustable for the
More informationChpt 5. Discrete Probability Distributions. 5-3 Mean, Variance, Standard Deviation, and Expectation
Chpt 5 Discrete Probability Distributios 5-3 Mea, Variace, Stadard Deviatio, ad Expectatio 1/23 Homework p252 Applyig the Cocepts Exercises p253 1-19 2/23 Objective Fid the mea, variace, stadard deviatio,
More informationMore On λ κ closed sets in generalized topological spaces
Journal of Algorithms and Computation journal homepage: http://jac.ut.ac.ir More On λ κ closed sets in generalized topological spaces R. Jamunarani, 1, P. Jeyanthi 2 and M. Velrajan 3 1,2 Research Center,
More information18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013
18.S096 Problem Set 5 Fall 2013 Volatility Modelig Due Date: 10/29/2013 1. Sample Estimators of Diffusio Process Volatility ad Drift Let {X t } be the price of a fiacial security that follows a geometric
More informationEVEN NUMBERED EXERCISES IN CHAPTER 4
Joh Riley 7 July EVEN NUMBERED EXERCISES IN CHAPTER 4 SECTION 4 Exercise 4-: Cost Fuctio of a Cobb-Douglas firm What is the cost fuctio of a firm with a Cobb-Douglas productio fuctio? Rather tha miimie
More informationMonopoly vs. Competition in Light of Extraction Norms. Abstract
Moopoly vs. Competitio i Light of Extractio Norms By Arkadi Koziashvili, Shmuel Nitza ad Yossef Tobol Abstract This ote demostrates that whether the market is competitive or moopolistic eed ot be the result
More informationPolicy Improvement for Repeated Zero-Sum Games with Asymmetric Information
Policy Improvemet for Repeated Zero-Sum Games with Asymmetric Iformatio Malachi Joes ad Jeff S. Shamma Abstract I a repeated zero-sum game, two players repeatedly play the same zero-sum game over several
More information14.30 Introduction to Statistical Methods in Economics Spring 2009
MIT OpeCourseWare http://ocwmitedu 430 Itroductio to Statistical Methods i Ecoomics Sprig 009 For iformatio about citig these materials or our Terms of Use, visit: http://ocwmitedu/terms 430 Itroductio
More informationInstitute of Actuaries of India Subject CT5 General Insurance, Life and Health Contingencies
Istitute of Actuaries of Idia Subject CT5 Geeral Isurace, Life ad Health Cotigecies For 2017 Examiatios Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical techiques which
More informationWhen you click on Unit V in your course, you will see a TO DO LIST to assist you in starting your course.
UNIT V STUDY GUIDE Percet Notatio Course Learig Outcomes for Uit V Upo completio of this uit, studets should be able to: 1. Write three kids of otatio for a percet. 2. Covert betwee percet otatio ad decimal
More informationChapter 5: Sequences and Series
Chapter 5: Sequeces ad Series 1. Sequeces 2. Arithmetic ad Geometric Sequeces 3. Summatio Notatio 4. Arithmetic Series 5. Geometric Series 6. Mortgage Paymets LESSON 1 SEQUENCES I Commo Core Algebra I,
More informationMoving frame and integrable system of the discrete centroaffine curves in R 3
Movig frame ad itegrable system of the discrete cetroaffie curves i R 3 Yu Yag, Yahua Yu Departmet of Mathematics, Northeaster Uiversity, Sheyag 0004, P R Chia arxiv:6006530v2 [mathdg] 27 Nov 206 Abstract
More informationSolutions to Problem Sheet 1
Solutios to Problem Sheet ) Use Theorem.4 to prove that p log for all real x 3. This is a versio of Theorem.4 with the iteger N replaced by the real x. Hit Give x 3 let N = [x], the largest iteger x. The,
More informationMethods of Assess the Impact of Technological Variables Complex Spatial-Distributed Systems on Costs
Iteratioal Joural of Advaces i Applied Scieces (IJAAS) Vol. 5, No., March 206, pp. 45~49 ISSN: 2252-884 45 Methods of Assess the Ipact of echological Variables Cople Spatial-Distributed Systes o Costs
More informationSecant Varieties, Symbolic Powers, Statistical Models
Secant Varieties, Symbolic Powers, Statistical Models Seth Sullivant North Carolina State University November 19, 2012 Seth Sullivant (NCSU) Secant Varieties, etc. November 19, 2012 1 / 27 Joins and Secant
More informationCHAPTER 2 PRICING OF BONDS
CHAPTER 2 PRICING OF BONDS CHAPTER SUARY This chapter will focus o the time value of moey ad how to calculate the price of a bod. Whe pricig a bod it is ecessary to estimate the expected cash flows ad
More informationpoint estimator a random variable (like P or X) whose values are used to estimate a population parameter
Estimatio We have oted that the pollig problem which attempts to estimate the proportio p of Successes i some populatio ad the measuremet problem which attempts to estimate the mea value µ of some quatity
More informationHopscotch and Explicit difference method for solving Black-Scholes PDE
Mälardale iversity Fiacial Egieerig Program Aalytical Fiace Semiar Report Hopscotch ad Explicit differece method for solvig Blac-Scholes PDE Istructor: Ja Röma Team members: A Gog HaiLog Zhao Hog Cui 0
More informationTopic 14: Maximum Likelihood Estimation
Toic 4: November, 009 As before, we begi with a samle X = (X,, X of radom variables chose accordig to oe of a family of robabilities P θ I additio, f(x θ, x = (x,, x will be used to deote the desity fuctio
More informationDiener and Diener and Walsh follow as special cases. In addition, by making. smooth, as numerically observed by Tian. Moreover, we propose the center
Smooth Covergece i the Biomial Model Lo-Bi Chag ad Ke Palmer Departmet of Mathematics, Natioal Taiwa Uiversity Abstract Various authors have studied the covergece of the biomial optio price to the Black-Scholes
More informationAsymptotics: Consistency and Delta Method
ad Delta Method MIT 18.655 Dr. Kempthore Sprig 2016 1 MIT 18.655 ad Delta Method Outlie Asymptotics 1 Asymptotics 2 MIT 18.655 ad Delta Method Cosistecy Asymptotics Statistical Estimatio Problem X 1,...,
More informationFourier Transform in L p (R) Spaces, p 1
Ge. Math. Notes, Vol. 3, No., March 20, pp.4-25 ISSN 229-784; Copyright c ICSS Publicatio, 200 www.i-csrs.org Available free olie at http://www.gema.i Fourier Trasform i L p () Spaces, p Devedra Kumar
More informationA DOUBLE INCREMENTAL AGGREGATED GRADIENT METHOD WITH LINEAR CONVERGENCE RATE FOR LARGE-SCALE OPTIMIZATION
A DOUBLE INCREMENTAL AGGREGATED GRADIENT METHOD WITH LINEAR CONVERGENCE RATE FOR LARGE-SCALE OPTIMIZATION Arya Mokhtari, Mert Gürbüzbalaba, ad Alejadro Ribeiro Departmet of Electrical ad Systems Egieerig,
More informationUnbiased estimators Estimators
19 Ubiased estimators I Chapter 17 we saw that a dataset ca be modeled as a realizatio of a radom sample from a probability distributio ad that quatities of iterest correspod to features of the model distributio.
More informationAn Empirical Study of the Behaviour of the Sample Kurtosis in Samples from Symmetric Stable Distributions
A Empirical Study of the Behaviour of the Sample Kurtosis i Samples from Symmetric Stable Distributios J. Marti va Zyl Departmet of Actuarial Sciece ad Mathematical Statistics, Uiversity of the Free State,
More informationConfidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.
Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).
More informationBinomial Theorem. Combinations with repetition.
Biomial.. Permutatios ad combiatios Give a set with elemets. The umber of permutatios of the elemets the set: P() =! = ( 1) ( 2)... 1 The umber of r-permutatios of the set: P(, r) =! = ( 1) ( 2)... ( r
More informationMinhyun Yoo, Darae Jeong, Seungsuk Seo, and Junseok Kim
Hoam Mathematical J. 37 (15), No. 4, pp. 441 455 http://dx.doi.org/1.5831/hmj.15.37.4.441 A COMPARISON STUDY OF EXPLICIT AND IMPLICIT NUMERICAL METHODS FOR THE EQUITY-LINKED SECURITIES Mihyu Yoo, Darae
More informationThese characteristics are expressed in terms of statistical properties which are estimated from the sample data.
0. Key Statistical Measures of Data Four pricipal features which characterize a set of observatios o a radom variable are: (i) the cetral tedecy or the value aroud which all other values are buched, (ii)
More informationZeros of Jacobi-Sobolev orthogonal polynomials following non-coherent pair of measures
Volume 29, N. 3, pp. 423 445, 2010 Copyright 2010 SBMAC ISSN 0101-8205 www.scielo.br/cam Zeros of Jacobi-Sobolev orthogoal polyomials followig o-coheret pair of measures ELIANA X.L. DE ANDRADE 1, CLEONICE
More informationDecision Science Letters
Decisio Sciece Letters 3 (214) 35 318 Cotets lists available at GrowigSciece Decisio Sciece Letters homepage: www.growigsciece.com/dsl Possibility theory for multiobective fuzzy radom portfolio optimizatio
More informationMark to Market Procedures (06, 2017)
Mark to Market Procedures (06, 207) Risk Maagemet Baco Sumitomo Mitsui Brasileiro S.A CONTENTS SCOPE 4 2 GUIDELINES 4 3 ORGANIZATION 5 4 QUOTES 5 4. Closig Quotes 5 4.2 Opeig Quotes 5 5 MARKET DATA 6 5.
More informationAn Empirical Study on the Contribution of Foreign Trade to the Economic Growth of Jiangxi Province, China
usiess, 21, 2, 183-187 doi:1.4236/ib.21.2222 Published Olie Jue 21 (http://www.scirp.org/joural/ib) 183 A Empirical Study o the Cotributio of Foreig Trade to the Ecoomic Growth of Jiagxi Provice, Chia
More informationMarking Estimation of Petri Nets based on Partial Observation
Markig Estimatio of Petri Nets based o Partial Observatio Alessadro Giua ( ), Jorge Júlvez ( ) 1, Carla Seatzu ( ) ( ) Dip. di Igegeria Elettrica ed Elettroica, Uiversità di Cagliari, Italy {giua,seatzu}@diee.uica.it
More informationStrong Subgraph k-connectivity of Digraphs
Strong Subgraph k-connectivity of Digraphs Yuefang Sun joint work with Gregory Gutin, Anders Yeo, Xiaoyan Zhang yuefangsun2013@163.com Department of Mathematics Shaoxing University, China July 2018, Zhuhai
More informationST 305: Exam 2 Fall 2014
ST 305: Exam Fall 014 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad
More informationFolia Oeconomica Stetinensia DOI: /foli NOTE TO
olia Oecoomica Stetiesia OI: 10.1515/foli-2016-0038 NOTE TO ATES O ETUN ON OPEN-EN EBT INVESTMENT UNS AN BANK EPOSITS IN POLAN IN THE YEAS 1995 2015 A COMPAATIVE ANALYSIS OLIA OECONOMICA STETINENSIA 16
More informationFurther Pure 1 Revision Topic 5: Sums of Series
The OCR syllabus says that cadidates should: Further Pure Revisio Topic 5: Sums of Series Cadidates should be able to: (a) use the stadard results for Σr, Σr, Σr to fid related sums; (b) use the method
More informationr i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i
The iformatio required by the mea-variace approach is substatial whe the umber of assets is large; there are mea values, variaces, ad )/2 covariaces - a total of 2 + )/2 parameters. Sigle-factor model:
More informationA point estimate is the value of a statistic that estimates the value of a parameter.
Chapter 9 Estimatig the Value of a Parameter Chapter 9.1 Estimatig a Populatio Proportio Objective A : Poit Estimate A poit estimate is the value of a statistic that estimates the value of a parameter.
More informationBinomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge
Biomial Model Stock Price Dyamics The value of a optio at maturity depeds o the price of the uderlyig stock at maturity. The value of the optio today depeds o the expected value of the optio at maturity
More informationA random variable is a variable whose value is a numerical outcome of a random phenomenon.
The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss
More informationNPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE)
NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) READ THE INSTRUCTIONS VERY CAREFULLY 1) Time duratio is 2 hours
More information(Electronic) Heat Current
ECE 656: Electroic rasport i Semicoductors Fall 2017 (Electroic) Heat Curret Mark Ludstrom Electrical ad Computer Egieerig Purdue Uiversity West Lafayette, IN USA 10/26/17 Seebeck effect voltage differece
More informationIntroduction to Financial Derivatives
550.444 Itroductio to Fiacial Derivatives Determiig Prices for Forwards ad Futures Week of October 1, 01 Where we are Last week: Itroductio to Iterest Rates, Future Value, Preset Value ad FRAs (Chapter
More informationIntroduction to Probability and Statistics Chapter 7
Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based
More informationFOUNDATION ACTED COURSE (FAC)
FOUNDATION ACTED COURSE (FAC) What is the Foudatio ActEd Course (FAC)? FAC is desiged to help studets improve their mathematical skills i preparatio for the Core Techical subjects. It is a referece documet
More informationECON 5350 Class Notes Maximum Likelihood Estimation
ECON 5350 Class Notes Maximum Likelihood Estimatio 1 Maximum Likelihood Estimatio Example #1. Cosider the radom sample {X 1 = 0.5, X 2 = 2.0, X 3 = 10.0, X 4 = 1.5, X 5 = 7.0} geerated from a expoetial
More informationChapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1
Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for
More informationInternational Journal of Management (IJM), ISSN (Print), ISSN (Online) Volume 1, Number 2, July - Aug (2010), IAEME
Iteratioal Joural of Maagemet (IJM), ISSN 0976 6502(Prit), ISSN 0976 6510(Olie) Volume 1, Number 2, July - Aug (2010), pp. 09-13 IAEME, http://www.iaeme.com/ijm.html IJM I A E M E AN ANALYSIS OF STABILITY
More information