Size: px
Start display at page:

Download ""

Transcription

1

2

3

4

5

6 W A W C 12

7 U A 7 8

8 U A U Y U A U Y

9 16

10

11 1 i p a=1 i 2 Y i = Yi a=1 p a=1 i 3 p a=1 i p U L L L A L U A L L A

12

13

14

15 ˆθ 0 θ 1 W ˆθ 0 ˆθ 1 W i [Y i θ 0 + θ 1 A i ] 2. [Y A = a] = θ 0 a A = a i i=1 Y iw i i=1 W i 95 θ 1 = 3.4 W A = 1/f(A L) SW A = f(a)/f(a L) 95

16 a E[Y a ] = β 0 + β 1 a. V E[Y a A, V ] = β 0 + β 1 a + β 2 V a + β 3 V. β 3 V A V L A B A B A B

17 W A,C = W A W C A, C L Y a=1,c=0 Π(A, C) L L SW A,C = SW A SW C SW C = P r[c = 0 A]/P r[c = 0 L, A] L C L L(and ) W A,C = 1/f(A, C = 0 L) A C f(a, C = 0 L) = f(a L) P r[c = 0 L, A] L f(a L) P r[c = 0 L, A] L L C A L E[Y a=1,c=0 ]

18 E[Y a=1,c=0 ] A = 1 A = 0 a E[Y A = a, C = 0, L = l] P r[l = l]. l L P r[l = l] P DF f L (l) Y E[Y A = a, C = 0, L = l]df L (l). A 9 L L l E[Y A = a, C = 0, L = l] A

19 l E[Y A = a, L = l] P r[l = l] A = 1 A = 0 A Y L L

20 l L L L A SW A (L) 1 L P r[a = 1 Y a=0, L] = P r[a = 1 L]

21 A logitp r[a = 1 Y a=0, L] = α 0 + α 1 Y a=0 + α 2 L α 2 L L p L 1,..., L p α 2 L = p j=1 α 2jL j α 1 Y a=0 A L E[Y a Y a=0 A = a, L] = β 1 a + β 2 al L β 0 β 3 L Y a=1 Y a=0

22 A Y A Y L Y a i Y a=0 i = ϕ 1 a + ϕ 2 al i ϕ 1 + ϕ 2 l L = l Y a=0 Y a=1 L E[Y a Y a=0 A = a, L] = β 1 a Yi a Yi a=0 = ϕ 1 a i ϕ 1 β i Y a Y a=0 = ϕ 1 a Y a=0 = Y a ϕ 1 a Y a=0 = Y ϕ 1 a ϕ 1 Y a=0 ϕ 1

23 ϕ 1 H(β 1 ) = Y a=1 H(β 1 ) Y a=0 L V V V E[Y a Y a=0 A = a, L] = β 1 a + β 2 av Y a i Y a=0 i = ϕ 1 a + ϕ 2 av logitp r[a = 1 H(ϕ ), L] = α 0 + α 1 H(ϕ ) + α 2 H(ϕ )V + α 3 L. A

24 L Y A E[Y a,c=0 L] = β 0 + β 1 a + β 2 al + β 3 L E[Y a,c=0 L = l] E[Y A = 1, C = 0, L = l] E[Y A = 1, C = 0, L = l] = α 0 + α 1 a + α 2 al + α 3 L L L p(l) Y a A L Y a A p(l) p(l) 1 0 L p(l) p(l)

25 E[Y A, C = 0, p(l)] s E[Y a=1,c=0 p(l) = s] E[Y a=0,c=0 p(l) = s] E[Y a=1,c=0 ] E[Y a=0,c=0 ] E[Y A, C = 0, p(l)] 13 L p(l) p(l) s p(l) s ± 0.05 A L L A L A A Y a L a

26 Z A Z Y A Z Y E[Y a=1 ] E[Y a=0 ] E[Y Z = 1] E[Y Z = 0] E[A Z = 1] E[A Z = 0] Z Cov(Y,Z) Cov(A,Z) Z Y Z A

27 E[A Z] = α 0 + α 1 Z Ê[A Z] E[Y Z] = β 0 + β 1 Ê[A Z] ˆβ 1 10 A Y Z E[Y a=1 Y a=0 Z = 1, A = a] = E[Y a=1 Y a=0 Z = 0, A = a] a = 0, 1 A z=1 = 1 A z=0 = 1 A z=1 = 0 A z=0 = 0 A z=1 = 1 A z=0 = 0 A z=1 = 0 A z=0 = 1

28 A z=1 A z=0 E[Y a=1 Y a=0 A z=1 = 1, A z=0 = 0]. Z Y Z Z Y Z A A Z Y Z U z U z A uz u z U z

29 Z A Z A A U A U Z A Z 95 A A Z Y Z Y V

30 Z Z Y

31 k P r[t > k] k k 1 P r[t > k] = P r[t k] t k t P r[t = k T > k 1] k k k k t k

32 D k k P r[d k = 0] = P r[t > k] k P r[d k = 1] = P r[t k] k P r[d k = 1 D k 1 = 0] k = 1 k = 0 k 0 k P r[d k = 0] = k P r[d m = 0 D m 1 = 0] m=1 k k P r[d k = 1 D k 1 = 0] k k 1 P r[d k = 0] k

33 k P r[d k+1 = 1 D k = 0] logitp r[d k+1 = 1 D k = 1, A] = θ 0,k + θ 1 A + θ 2 A k + θ 3 A k 2 θ 0,k = θ 0 + θ 4 k + θ 3 A k 2 P r[d k+1 = 0 A = a] P r[d k+1 = 0 D k = 0, A = a]

34

35 A = 1 E = 1 A = 1 E = A Y U A A U Y Y

36 n 95 n

37 $

38 P r[a = a L = l] = 0 P r[l = l] 0 E[Y A = a, L = l] E[Y A, L] α 1 = 0 L

39 α E[Y a,c=0 L] = β 0 + β 1 a + β 2 al + β 3 L, β 1 β 2 β 0 β Z A Y A

40 Z A Y Z A Y

41 P r[y = 0 E = e, A = a] P r[y = 0 E = e, A = a] = g(e)h(a) P r[y = 0 E = e, A = a]/p r[y = 0 E = e, A = 0] P r[y = 0 E = e, A = a] = g(e)h(a) P r[y = 1 E = e, A = a] = 1 g(e)h(a) Y = 0 Y = f( ) P DF U A U Y P DF P DF U A P DF U Y P DF 9.2 Y z=0,a = Y z=1,a

42 Y A L A L L 10.3

43 11.1 p i=0 θ ix i g{ } g{e[y X]} = p i=1 θ ix i log{e[y X]} = p θ i X i. i=1 E[Y X] p log{ 1 E[Y X] } = θ i X i. i=1 θ E[Y X] n i=1 E[Y X = x] x ω h(x X i )Y I i=1 nω h(x X i ω ) h (z) z = 0 0 z h ω p i=0 θ ix i p i=0 f i(x i )

44 f i ( ) k E[Y X = x] E[Y X = x] X x X X x E[Y X = x] E[Y X = x] X x E[Y X = x] X x h h = I(A = a)y ÊE[ ] f(a L) ÊE[ I(A=a)Y f(a L) ] ÊE[ I(A=a) f(a L) ]

45 12.2 g[a] f[a L] E[Y a ] E[ I(A = a) E[ ] = 1 f[a Y ] I(A = a)y E[ ] = E[Y a ] f[a Y ] E[ I(A=a)Y ] f[a Y ] E[ I(A=a) ] = E[Y a ] f[a Y ] E[ I(A=a)Y f[a Y ] E[ I(A=a) I(A = a)y f[a Y ] g(a)] f[a Y ] g(a)] = E[Y a ] g(a)] = E[Y a ]g(a) E[ I(A = a) g(a)] f[a Y ] A Y E[Y A = a, C = 0, L = l, D]

46 A = 1 A = 0 L 14.1 a V L E[Y a V ] = β 0 + β 1 a + β 2 av + β 3 V. E[Y a V ] = E[Y a=0 V ] + β 1 a + β 2 av E[Y a Y a=0 V ] = β 1 a + β 2 av 14.1 log( E[Y a A = a, L] E[Y a=0 A = a, L] ) = β 1a + β 2 L H(ϕ ) Y exp[ ϕ 1a ϕ 2L] (0, 1) Y = 1 L 1 Y logitp r[y a = 1 A = a, L] logitp t[y a=0 = 1 A = a, L] = β 1 a + β 2 L

47 14.2 β 1 H(ϕ ) H(ϕ ) A ϕ N i=1 I[C i = 0]W C i H i (ϕ )(A i E[A L i ]) = 0 H i (ϕ ) = Y i ϕ A i ϕ 1 = N i=1 I[C i = 0]W C i Y i (A i E[A L i ])/ N i=1 I[C i = 0]W C i A i (A i E[A L i ]) H i (ϕ ) H(ϕ ) H(ϕ ) E[H(ϕ ) L] ϕ E[H(ϕ ) L] E[A L] P r[c = 1 A, L] 15.1 b(l) L A L b(l) L b(l)

48 16.1 Z A Z Y Y a,z Z a, z Z 16.2 P r[y a=1 = 1] P r[y a=0 = 1] Z A Y A Z E[Y a=1 Y a=0 A = 1, Z] = β 0 + β 1 Z E[Y Y a=0 A, Z] = A(β 0 + β 1 Z) β 0 Z = 0

49 β 0 + β 1 Z = 1 Z β 1 = 0 β 0 E[Y a=0 Z = 1] = E[Y a=0 Z = 0] E[Y A(β 0 + β 1 ) Z = 1] = E[Y Aβ 0 Z = 0] β 1 = 0 β 0 = E[Y Z = 1] E[Y Z = 0] E[A Z = 1] E[A Z = 0] β 1 = 0 β 0 = E[Y a=1 Y a=0 A = 1, Z = z] = E[Y a=1 Y a=0 A = 1] z E[Y a=1 ] E[Y a=0 ] β 0 = E[Y a=1 ] E[Y a=0 ] E[Y a=1 ] E[Y a=0 ] 16.4 A Z E[Y a=1 A = 1, Z] E[Y a=0 A = 1, Z] = exp(β 0 + β 1 Z), E[Y A, Z] = E[Y a=0 A, Z]exp[A(β 0 + β 1 Z), exp(β 0 Z = 0 exp(β 0 + β 1 ) Z = 1

50 β 1 = 0 E[Y a=1 ]/E[Y a=0 ] = exp(β 0 ) E[Y a=1 ] E[Y a=0 ] = E[Y A = 0](1 E[A])[exp(β 0 ) 1] + E[Y A = 1]E[A][1 exp(β 0 )] Z E[Y (1)] E[Y (0)] Z 16.5 E[Y Y a=0 Z, A, V ] = γ(z, A, V, ψ ) E[Y a=0 Z = 1, V ] = E[Y a=0 Z = 0, V ]. E[Y Z, A, V ] = E[Y 0 Z, A, V ]exp[γ(z, A, V, ψ )] 16.6 E[Y a=1 Y a=0 A z=1 A z=0 = 1]

51 E[Y z=1 Y z=0 ] = E[Y z=1 Y z=0 A z=1 = 1, A z=0 = 1]P r[a z=1 = 1, A z=0 = 1] +E[Y z=1 Y z=0 A z=1 = 0, A z=0 = 0]P r[a z=1 = 0, A z=0 = 0] +E[Y z=1 Y z=0 A z=1 = 1, A z=0 = 0]P r[a z=1 = 1, A z=0 = 0] +E[Y z=1 Y z=0 A z=1 = 0, A z=0 = 1]P r[a z=1 = 0, A z=0 = 1]. E[Y z=1 Y z=0 ] = E[Y z=1 Y z=0 A z=1 = 1, A z=0 = 0]P r[a z=1 = 1, A z=0 = 0]. E[Y z=1 Y z=0 A z=1 = 1, A z=0 = 0] = E[Y a=1 Y a=0 A z=1 = 1, A z=0 = 0]. E[Y a=1 Y a=0 A z=1 = 1, A z=0 = 0] = E[Y z=1 Y z=0 ] P r[a z=1 = 1, A z=0 = 0]. Z E[Y z=1 Y z=0 ] = E[Y Z = 1] E[Y Z = 0]. P r[a z=1 A z=0 = 1] = P r[a = 1 Z = 1] P r[a = 1 Z = 0]. P r[a z=0 = 1] = P r[a = 1 Z = 0], P r[a z=1 = 0] = P r[a = 0 Z = 1]. P r[a z=1 A z=0 = 1] = 1 P r[a = 1 Z = 0] P r[a = 0 Z = 1] = 1 P r[a = 1 Z = 0] (1 P r[a = 1 Z = 1]) = P r[a = 1 Z = 1] P r[a = 1 Z = 0] Z Z U z U z

52 Z A Y U z U z U z Z U z 10.2

53

54 ... L p +E[Y z=1 Y z=0 A z=1 = 1, A z=0 = 0]P r[a z=1 = 1, A z=0 = 0]

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

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

More information

Topic 7 - Matrix Approach to Simple Linear Regression. Outline. Matrix. Matrix. Review of Matrices. Regression model in matrix form

Topic 7 - Matrix Approach to Simple Linear Regression. Outline. Matrix. Matrix. Review of Matrices. Regression model in matrix form Topic 7 - Matrix Approach to Simple Linear Regression Review of Matrices Outline Regression model in matrix form - Fall 03 Calculations using matrices Topic 7 Matrix Collection of elements arranged in

More information

Scripture quotations marked cev are from the Contemporary English Version, Copyright 1991, 1992, 1995 by American Bible Society. Used by permission.

Scripture quotations marked cev are from the Contemporary English Version, Copyright 1991, 1992, 1995 by American Bible Society. Used by permission. N Ra: E K B Da a a B a a, a-a- a aa, a a. T, a a. 2009 Ba P, I. ISBN 978-1-60260-296-0. N a a a a a, a,. C a a a Ba P, a 500 a a aa a. W, : F K B Da, Ba P, I. U. S a a a a K Ja V B. S a a a a N K Ja V.

More information

Likelihood Inference for Lattice Spatial Processes

Likelihood Inference for Lattice Spatial Processes Likelihood Inference for Lattice Spatial Processes Donghoh Kim November 30, 2004 Donghoh Kim 1/24 Go to 1234567891011121314151617 FULL Lattice Processes Model : The Ising Model (1925), The Potts Model

More information

/ / MET Day 000 NC1^ INRTL MNVR I E E PRE SLEEP K PRE SLEEP R E

/ / MET Day 000 NC1^ INRTL MNVR I E E PRE SLEEP K PRE SLEEP R E 05//0 5:26:04 09/6/0 (259) 6 7 8 9 20 2 22 2 09/7 0 02 0 000/00 0 02 0 04 05 06 07 08 09 0 2 ay 000 ^ 0 X Y / / / / ( %/ ) 2 /0 2 ( ) ^ 4 / Y/ 2 4 5 6 7 8 9 2 X ^ X % 2 // 09/7/0 (260) ay 000 02 05//0

More information

Infinitely Imbalanced Logistic Regression

Infinitely Imbalanced Logistic Regression p. 1/1 Infinitely Imbalanced Logistic Regression Art B. Owen Journal of Machine Learning Research, April 2007 Presenter: Ivo D. Shterev p. 2/1 Outline Motivation Introduction Numerical Examples Notation

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

22s:152 Applied Linear Regression. Example: Study on lead levels in children. Ch. 14 (sec. 1) and Ch. 15 (sec. 1 & 4): Logistic Regression

22s:152 Applied Linear Regression. Example: Study on lead levels in children. Ch. 14 (sec. 1) and Ch. 15 (sec. 1 & 4): Logistic Regression 22s:52 Applied Linear Regression Ch. 4 (sec. and Ch. 5 (sec. & 4: Logistic Regression Logistic Regression When the response variable is a binary variable, such as 0 or live or die fail or succeed then

More information

Generalized Linear Models. Kurt Hornik

Generalized Linear Models. Kurt Hornik Generalized Linear Models Kurt Hornik Motivation Assuming normality, the linear model y = Xβ + e has y = β + ε, ε N(0, σ 2 ) such that y N(μ, σ 2 ), E(y ) = μ = β. Various generalizations, including general

More information

Final Examination. Tuesday December 15, :30 am 12:30 pm. particles that are in the same spin state 1 2, + 1 2

Final Examination. Tuesday December 15, :30 am 12:30 pm. particles that are in the same spin state 1 2, + 1 2 Department of Physics Quantum Mechanics I, Physics 57 Temple University Instructor: Z.-E. Meziani Final Examination Tuesday December 5, 5 :3 am :3 pm Problem. pts) Consider a system of three non interacting,

More information

Link lecture - Lagrange Multipliers

Link lecture - Lagrange Multipliers Link lecture - Lagrange Multipliers Lagrange multipliers provide a method for finding a stationary point of a function, say f(x, y) when the variables are subject to constraints, say of the form g(x, y)

More information

ECE531 Homework Assignment Number 7 Solution

ECE531 Homework Assignment Number 7 Solution ECE53 Homework Assignment Number 7 Solution Due by 8:50pm on Wednesday 30-Mar-20 Make sure your reasoning and work are clear to receive full credit for each problem.. 4 points. Kay I: 2.6. Solution: I

More information

An Efficient Estimation Method for Longitudinal Surveys with Monotone Missing Data

An Efficient Estimation Method for Longitudinal Surveys with Monotone Missing Data An Efficient Estimation Method for Longitudinal Surveys with Monotone Missing Data Jae-Kwang Kim 1 Iowa State University June 28, 2012 1 Joint work with Dr. Ming Zhou (when he was a PhD student at ISU)

More information

Fun and Fascinating Bible Reference for Kids Ages 8 to 12. starts on page 3! starts on page 163!

Fun and Fascinating Bible Reference for Kids Ages 8 to 12. starts on page 3! starts on page 163! F a Faa R K 8 12 a a 3! a a 163! 2013 a P, I. ISN 978-1-62416-216-9. N a a a a a, a,. C a a a a P, a 500 a a aa a. W, : F G: K Fa a Q &, a P, I. U. L aa a a a Fa a Q & a. C a 2 (M) Ta H P M (K) Wa P a

More information

i=1 k i=1 g i (Y )] = k

i=1 k i=1 g i (Y )] = k Math 483 EXAM 2 covers 2.4, 2.5, 2.7, 2.8, 3.1, 3.2, 3.3, 3.4, 3.8, 3.9, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.9, 5.1, 5.2, and 5.3. The exam is on Thursday, Oct. 13. You are allowed THREE SHEETS OF NOTES and

More information

MLE for a logit model

MLE for a logit model IGIDR, Bombay September 4, 2008 Goals The link between workforce participation and education Analysing a two-variable data-set: bivariate distributions Conditional probability Expected mean from conditional

More information

S U E K E AY S S H A R O N T IM B E R W IN D M A R T Z -PA U L L IN. Carlisle Franklin Springboro. Clearcreek TWP. Middletown. Turtlecreek TWP.

S U E K E AY S S H A R O N T IM B E R W IN D M A R T Z -PA U L L IN. Carlisle Franklin Springboro. Clearcreek TWP. Middletown. Turtlecreek TWP. F R A N K L IN M A D IS O N S U E R O B E R T LE IC H T Y A LY C E C H A M B E R L A IN T W IN C R E E K M A R T Z -PA U L L IN C O R A O W E N M E A D O W L A R K W R E N N LA N T IS R E D R O B IN F

More information

(a) To show f(z) is analytic, explicitly evaluate partials,, etc. and show. = 0. To find v, integrate u = v to get v = dy u =

(a) To show f(z) is analytic, explicitly evaluate partials,, etc. and show. = 0. To find v, integrate u = v to get v = dy u = Homework -5 Solutions Problems (a) z = + 0i, (b) z = 7 + 24i 2 f(z) = u(x, y) + iv(x, y) with u(x, y) = e 2y cos(2x) and v(x, y) = e 2y sin(2x) u (a) To show f(z) is analytic, explicitly evaluate partials,,

More information

Central Limit Theorem ( 5.3)

Central Limit Theorem ( 5.3) Central Limit Theorem ( 5.3) Let X 1, X 2,... be a sequence of independent random variables, each having n mean µ and variance σ 2. Then the distribution of the partial sum S n = X i i=1 becomes approximately

More information

Chapter 5 Matrix Approach to Simple Linear Regression

Chapter 5 Matrix Approach to Simple Linear Regression STAT 525 SPRING 2018 Chapter 5 Matrix Approach to Simple Linear Regression Professor Min Zhang Matrix Collection of elements arranged in rows and columns Elements will be numbers or symbols For example:

More information

CMO 2007 Solutions. 3. Suppose that f is a real-valued function for which. f(xy) + f(y x) f(y + x) for all real numbers x and y.

CMO 2007 Solutions. 3. Suppose that f is a real-valued function for which. f(xy) + f(y x) f(y + x) for all real numbers x and y. CMO 2007 Solutions 1 What is the maximum number of non-overlapping 2 1 dominoes that can be placed on an 8 9 checkerboard if six of them are placed as shown? Each domino must be placed horizontally or

More information

Math 3330: Solution to midterm Exam

Math 3330: Solution to midterm Exam Math 3330: Solution to midterm Exam Question 1: (14 marks) Suppose the regression model is y i = β 0 + β 1 x i + ε i, i = 1,, n, where ε i are iid Normal distribution N(0, σ 2 ). a. (2 marks) Compute the

More information

Logistic regression. 11 Nov Logistic regression (EPFL) Applied Statistics 11 Nov / 20

Logistic regression. 11 Nov Logistic regression (EPFL) Applied Statistics 11 Nov / 20 Logistic regression 11 Nov 2010 Logistic regression (EPFL) Applied Statistics 11 Nov 2010 1 / 20 Modeling overview Want to capture important features of the relationship between a (set of) variable(s)

More information

Chapter 17 The bilinear covariant fields of the Dirac electron. from my book: Understanding Relativistic Quantum Field Theory.

Chapter 17 The bilinear covariant fields of the Dirac electron. from my book: Understanding Relativistic Quantum Field Theory. Chapter 17 The bilinear covariant fields of the Dirac electron from my book: Understanding Relativistic Quantum Field Theory Hans de Vries November 10, 008 Chapter Contents 17 The bilinear covariant fields

More information

MATH 644: Regression Analysis Methods

MATH 644: Regression Analysis Methods MATH 644: Regression Analysis Methods FINAL EXAM Fall, 2012 INSTRUCTIONS TO STUDENTS: 1. This test contains SIX questions. It comprises ELEVEN printed pages. 2. Answer ALL questions for a total of 100

More information

Ph.D. Qualifying Exam Friday Saturday, January 6 7, 2017

Ph.D. Qualifying Exam Friday Saturday, January 6 7, 2017 Ph.D. Qualifying Exam Friday Saturday, January 6 7, 2017 Put your solution to each problem on a separate sheet of paper. Problem 1. (5106) Let X 1, X 2,, X n be a sequence of i.i.d. observations from a

More information

VERITAS L1 trigger Constant Fraction Discriminator. Vladimir Vassiliev Jeremy Smith David Kieda

VERITAS L1 trigger Constant Fraction Discriminator. Vladimir Vassiliev Jeremy Smith David Kieda VERITAS L trigger Constant Fraction Discriminator Vladimir Vassiliev Jeremy Smith David Kieda Content Night Sky Background Noise Traditional Threshold Discriminator Constant Fraction Discriminator CFD:

More information

Controllability of linear PDEs (I): The wave equation

Controllability of linear PDEs (I): The wave equation Controllability of linear PDEs (I): The wave equation M. González-Burgos IMUS, Universidad de Sevilla Doc Course, Course 2, Sevilla, 2018 Contents 1 Introduction. Statement of the problem 2 Distributed

More information

Inference After Variable Selection

Inference After Variable Selection Department of Mathematics, SIU Carbondale Inference After Variable Selection Lasanthi Pelawa Watagoda lasanthi@siu.edu June 12, 2017 Outline 1 Introduction 2 Inference For Ridge and Lasso 3 Variable Selection

More information

Gauge Plots. Gauge Plots JAPANESE BEETLE DATA MAXIMUM LIKELIHOOD FOR SPATIALLY CORRELATED DISCRETE DATA JAPANESE BEETLE DATA

Gauge Plots. Gauge Plots JAPANESE BEETLE DATA MAXIMUM LIKELIHOOD FOR SPATIALLY CORRELATED DISCRETE DATA JAPANESE BEETLE DATA JAPANESE BEETLE DATA 6 MAXIMUM LIKELIHOOD FOR SPATIALLY CORRELATED DISCRETE DATA Gauge Plots TuscaroraLisa Central Madsen Fairways, 996 January 9, 7 Grubs Adult Activity Grub Counts 6 8 Organic Matter

More information

Approximation of the Fisher information and design in nonlinear mixed effects models

Approximation of the Fisher information and design in nonlinear mixed effects models of the Fisher information and design in nonlinear mixed effects models Tobias Mielke Optimum for Mixed Effects Non-Linear and Generalized Linear PODE - 2011 Outline 1 2 3 Mixed Effects Similar functions

More information

ME 509, Spring 2016, Final Exam, Solutions

ME 509, Spring 2016, Final Exam, Solutions ME 509, Spring 2016, Final Exam, Solutions 05/03/2016 DON T BEGIN UNTIL YOU RE TOLD TO! Instructions: This exam is to be done independently in 120 minutes. You may use 2 pieces of letter-sized (8.5 11

More information

x 3y 2z = 6 1.2) 2x 4y 3z = 8 3x + 6y + 8z = 5 x + 3y 2z + 5t = 4 1.5) 2x + 8y z + 9t = 9 3x + 5y 12z + 17t = 7

x 3y 2z = 6 1.2) 2x 4y 3z = 8 3x + 6y + 8z = 5 x + 3y 2z + 5t = 4 1.5) 2x + 8y z + 9t = 9 3x + 5y 12z + 17t = 7 Linear Algebra and its Applications-Lab 1 1) Use Gaussian elimination to solve the following systems x 1 + x 2 2x 3 + 4x 4 = 5 1.1) 2x 1 + 2x 2 3x 3 + x 4 = 3 3x 1 + 3x 2 4x 3 2x 4 = 1 x + y + 2z = 4 1.4)

More information

Exercise 9: Model of a Submarine

Exercise 9: Model of a Submarine Fluid Mechanics, SG4, HT3 October 4, 3 Eample : Submarine Eercise 9: Model of a Submarine The flow around a submarine moving at a velocity V can be described by the flow caused by a source and a sink with

More information

Single-level Models for Binary Responses

Single-level Models for Binary Responses Single-level Models for Binary Responses Distribution of Binary Data y i response for individual i (i = 1,..., n), coded 0 or 1 Denote by r the number in the sample with y = 1 Mean and variance E(y) =

More information

Structure of Generalized Parton Distributions

Structure of Generalized Parton Distributions =Hybrids Generalized Parton Distributions A.V. Radyushkin June 2, 201 Hadrons in Terms of Quarks and Gluons =Hybrids Situation in hadronic physics: All relevant particles established QCD Lagrangian is

More information

Last Lecture - Key Questions. Biostatistics Statistical Inference Lecture 03. Minimal Sufficient Statistics

Last Lecture - Key Questions. Biostatistics Statistical Inference Lecture 03. Minimal Sufficient Statistics Last Lecture - Key Questions Biostatistics 602 - Statistical Inference Lecture 03 Hyun Min Kang January 17th, 2013 1 How do we show that a statistic is sufficient for θ? 2 What is a necessary and sufficient

More information

Solutions to Assignment #8 Math 501 1, Spring 2006 University of Utah

Solutions to Assignment #8 Math 501 1, Spring 2006 University of Utah Solutions to Assignment #8 Math 5, Spring 26 University of Utah Problems:. A man and a woman agree to meet at a certain location at about 2:3 p.m. If the man arrives at a time uniformly distributed between

More information

EE5120 Linear Algebra: Tutorial 7, July-Dec Covers sec 5.3 (only powers of a matrix part), 5.5,5.6 of GS

EE5120 Linear Algebra: Tutorial 7, July-Dec Covers sec 5.3 (only powers of a matrix part), 5.5,5.6 of GS EE5 Linear Algebra: Tutorial 7, July-Dec 7-8 Covers sec 5. (only powers of a matrix part), 5.5,5. of GS. Prove that the eigenvectors corresponding to different eigenvalues are orthonormal for unitary matrices.

More information

[ zd z zdz dψ + 2i. 2 e i ψ 2 dz. (σ 2 ) 2 +(σ 3 ) 2 = (1+ z 2 ) 2

[ zd z zdz dψ + 2i. 2 e i ψ 2 dz. (σ 2 ) 2 +(σ 3 ) 2 = (1+ z 2 ) 2 2 S 2 2 2 2 2 M M 4 S 2 S 2 z, w : C S 2 z = 1/w e iψ S 1 S 2 σ 1 = 1 ( ) [ zd z zdz dψ + 2i 2 1 + z 2, σ 2 = Re 2 e i ψ 2 dz 1 + z 2 ], σ 3 = Im [ 2 e i ψ 2 dz 1 + z 2 σ 2 σ 3 (σ 2 ) 2 (σ 3 ) 2 σ 2 σ

More information

Lecture 2: Martingale theory for univariate survival analysis

Lecture 2: Martingale theory for univariate survival analysis Lecture 2: Martingale theory for univariate survival analysis In this lecture T is assumed to be a continuous failure time. A core question in this lecture is how to develop asymptotic properties when

More information

2.7 Estimation with linear Restriction

2.7 Estimation with linear Restriction Proof (Method 1: show that that a C(W T ), which implies that the GLSE is an estimable function under the old model is also an estimable function under the new model; secnd show that E[a T ˆβ G ] = a T

More information

8 Nonlinear Regression

8 Nonlinear Regression 8 Nonlinear Regression Nonlinear regression relates to models, where the mean response is not linear in the parameters of the model. A MLRM Y = β 0 + β 1 x 1 + β 2 x 2 + + β k x k + ε, ε N (0, σ 2 ) has

More information

The Schroedinger equation

The Schroedinger equation The Schroedinger equation After Planck, Einstein, Bohr, de Broglie, and many others (but before Born), the time was ripe for a complete theory that could be applied to any problem involving nano-scale

More information

I. Relationship with previous work

I. Relationship with previous work x x i t j J t = {0, 1,...J t } j t (p jt, x jt, ξ jt ) p jt R + x jt R k k ξ jt R ξ t T j = 0 t (z i, ζ i, G i ), ζ i z i R m G i G i (p j, x j ) i j U(z i, ζ i, x j, p j, ξ j ; G i ) = u(ζ i, x j,

More information

LIST OF FORMULAS FOR STK1100 AND STK1110

LIST OF FORMULAS FOR STK1100 AND STK1110 LIST OF FORMULAS FOR STK1100 AND STK1110 (Version of 11. November 2015) 1. Probability Let A, B, A 1, A 2,..., B 1, B 2,... be events, that is, subsets of a sample space Ω. a) Axioms: A probability function

More information

Theoretical Tutorial Session 2

Theoretical Tutorial Session 2 1 / 36 Theoretical Tutorial Session 2 Xiaoming Song Department of Mathematics Drexel University July 27, 216 Outline 2 / 36 Itô s formula Martingale representation theorem Stochastic differential equations

More information

Parts Manual. EPIC II Critical Care Bed REF 2031

Parts Manual. EPIC II Critical Care Bed REF 2031 EPIC II Critical Care Bed REF 2031 Parts Manual For parts or technical assistance call: USA: 1-800-327-0770 2013/05 B.0 2031-109-006 REV B www.stryker.com Table of Contents English Product Labels... 4

More information

FORMULAE FOR G-FUNCTION

FORMULAE FOR G-FUNCTION Revista de la Union Matematica Argentina Volumen 24, Numero 4, 1969. SOME FORMULAE FOR G-FUNCTION by B.L. Sharma 1. INTRODUCTION. In a recent paper {S} the author has defined the generalized function of

More information

3.6. Let s write out the sample space for this random experiment:

3.6. Let s write out the sample space for this random experiment: STAT 5 3 Let s write out the sample space for this ranom experiment: S = {(, 2), (, 3), (, 4), (, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5), (4, 5)} This sample space assumes the orering of the balls

More information

APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS

APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS LECTURE 10 APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS Ordinary Differential Equations Initial Value Problems For Initial Value problems (IVP s), conditions are specified

More information

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c L i f e t i m e M a n a g e m e n t o f F l a-b s ah s e d S S D s U s i n g R e c o v e r-a y w a r e D y n a m i c T h r o t t l i n g S u n g j i n L e, e T a e j i n K i m, K y u n g h o, Kainmd J

More information

At point G V = = = = = = RB B B. IN RB f

At point G V = = = = = = RB B B. IN RB f Common Emitter At point G CE RC 0. 4 12 0. 4 116. I C RC 116. R 1k C 116. ma I IC 116. ma β 100 F 116µ A I R ( 116µ A)( 20kΩ) 2. 3 R + 2. 3 + 0. 7 30. IN R f Gain in Constant Current Region I I I C F

More information

Solutions to PS 2 Physics 201

Solutions to PS 2 Physics 201 Solutions to PS Physics 1 1. ke dq E = i (1) r = i = i k eλ = i k eλ = i k eλ k e λ xdx () (x x) (x x )dx (x x ) + x dx () (x x ) x ln + x x + x x (4) x + x ln + x (5) x + x To find the field for x, we

More information

Chemistry 532 Problem Set 7 Spring 2012 Solutions

Chemistry 532 Problem Set 7 Spring 2012 Solutions Chemistry 53 Problem Set 7 Spring 01 Solutions 1. The study of the time-independent Schrödinger equation for a one-dimensional particle subject to the potential function leads to the differential equation

More information

Econometrics II - EXAM Answer each question in separate sheets in three hours

Econometrics II - EXAM Answer each question in separate sheets in three hours Econometrics II - EXAM Answer each question in separate sheets in three hours. Let u and u be jointly Gaussian and independent of z in all the equations. a Investigate the identification of the following

More information

H U G H T H O M A S, C E O, S K Y H I G H W A Y S

H U G H T H O M A S, C E O, S K Y H I G H W A Y S FARE COLLECTION SMARTPHONE APPLICAT ION FOR PUBLIC T R ANSIT U SERS H U G H T H O M A S, C E O, S K Y H I G H W A Y S APTA EXPO CONFERENCE OCTOBER 2011 1 MOBILE SOFTWARE FOR PEOPLE ON THE MOVE S K Y H

More information

Econometrics A. Simple linear model (2) Keio University, Faculty of Economics. Simon Clinet (Keio University) Econometrics A October 16, / 11

Econometrics A. Simple linear model (2) Keio University, Faculty of Economics. Simon Clinet (Keio University) Econometrics A October 16, / 11 Econometrics A Keio University, Faculty of Economics Simple linear model (2) Simon Clinet (Keio University) Econometrics A October 16, 2018 1 / 11 Estimation of the noise variance σ 2 In practice σ 2 too

More information

Fuzzy Reasoning and Optimization Based on a Generalized Bayesian Network

Fuzzy Reasoning and Optimization Based on a Generalized Bayesian Network Fuy R O B G By Nw H-Y K D M Du M Hu Cu Uvy 48 Hu Cu R Hu 300 Tw. @w.u.u.w A By w v wy u w w uy. Hwv u uy u By w y u v w uu By w w w u vu vv y. T uy v By w w uy v v uy. B By w uy. T uy v uy. T w w w- uy.

More information

Data Mining Stat 588

Data Mining Stat 588 Data Mining Stat 588 Lecture 9: Basis Expansions Department of Statistics & Biostatistics Rutgers University Nov 01, 2011 Regression and Classification Linear Regression. E(Y X) = f(x) We want to learn

More information

Solutions to Exercises 6.1

Solutions to Exercises 6.1 34 Chapter 6 Conformal Mappings Solutions to Exercises 6.. An analytic function fz is conformal where f z. If fz = z + e z, then f z =e z z + z. We have f z = z z += z =. Thus f is conformal at all z.

More information

Estimating the Marginal Odds Ratio in Observational Studies

Estimating the Marginal Odds Ratio in Observational Studies Estimating the Marginal Odds Ratio in Observational Studies Travis Loux Christiana Drake Department of Statistics University of California, Davis June 20, 2011 Outline The Counterfactual Model Odds Ratios

More information

Qualification Exam: Mathematical Methods

Qualification Exam: Mathematical Methods Qualification Exam: Mathematical Methods Name:, QEID#41534189: August, 218 Qualification Exam QEID#41534189 2 1 Mathematical Methods I Problem 1. ID:MM-1-2 Solve the differential equation dy + y = sin

More information

Econometric modelling and forecasting of intraday electricity prices

Econometric modelling and forecasting of intraday electricity prices E y y Xv:1812.09081v1 [q-.st] 21 D 2018 M Nw Uvy Duu-E F Z Uvy Duu-E D 24, 2018 A I w w y ID 3 -P G Iy Cuu Ey M u. A uv u uy qu-uy u y. W u qu u-- vy - uy. T w u. F u v w G Iy Cuu Ey M y ID 3 -P vu. T

More information

Lecture 04. Curl and Divergence

Lecture 04. Curl and Divergence Lecture 04 Curl and Divergence UCF Curl of Vector Field (1) F c d l F C Curl (or rotor) of a vector field a n curlf F d l lim c s s 0 F s a n C a n : normal direction of s follow right-hand rule UCF Curl

More information

IMPORTANCE SAMPLING Importance Sampling Background: let x = (x 1,..., x n ), θ = E[h(X)] = h(x)f(x)dx 1 N. h(x i ) = Θ,

IMPORTANCE SAMPLING Importance Sampling Background: let x = (x 1,..., x n ), θ = E[h(X)] = h(x)f(x)dx 1 N. h(x i ) = Θ, 1 IMPORTANCE SAMPLING Importance Sampling Background: let x = (x 1,..., x n ), θ = E[h(X)] = h(x)f(x)dx 1 N N h(x i ) = Θ, if X i F (X), and F (x) is cdf for f(x). For many problems, F (x) is difficult

More information

Combining Non-probability and Probability Survey Samples Through Mass Imputation

Combining Non-probability and Probability Survey Samples Through Mass Imputation Combining Non-probability and Probability Survey Samples Through Mass Imputation Jae-Kwang Kim 1 Iowa State University & KAIST October 27, 2018 1 Joint work with Seho Park, Yilin Chen, and Changbao Wu

More information

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9 OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at

More information

Differential forms. Proposition 3 Let X be a Riemann surface, a X and (U, z = x + iy) a coordinate neighborhood of a.

Differential forms. Proposition 3 Let X be a Riemann surface, a X and (U, z = x + iy) a coordinate neighborhood of a. Differential forms Proposition 3 Let X be a Riemann surface, a X and (U, z = x + iy) a coordinate neighborhood of a. 1. The elements d a x and d a y form a basis of the cotangent space T (1) a. 2. If f

More information

p y (1 p) 1 y, y = 0, 1 p Y (y p) = 0, otherwise.

p y (1 p) 1 y, y = 0, 1 p Y (y p) = 0, otherwise. 1. Suppose Y 1, Y 2,..., Y n is an iid sample from a Bernoulli(p) population distribution, where 0 < p < 1 is unknown. The population pmf is p y (1 p) 1 y, y = 0, 1 p Y (y p) = (a) Prove that Y is the

More information

Economics 620, Lecture 5: exp

Economics 620, Lecture 5: exp 1 Economics 620, Lecture 5: The K-Variable Linear Model II Third assumption (Normality): y; q(x; 2 I N ) 1 ) p(y) = (2 2 ) exp (N=2) 1 2 2(y X)0 (y X) where N is the sample size. The log likelihood function

More information

Nonparametric estimation of extreme risks from heavy-tailed distributions

Nonparametric estimation of extreme risks from heavy-tailed distributions Nonparametric estimation of extreme risks from heavy-tailed distributions Laurent GARDES joint work with Jonathan EL METHNI & Stéphane GIRARD December 2013 1 Introduction to risk measures 2 Introduction

More information

Gibbs models estimation of galaxy point processes with the ABC Shadow algorithm

Gibbs models estimation of galaxy point processes with the ABC Shadow algorithm Gibbs models estimation of galaxy point processes with the ABC Shadow algorithm Lluı s Hurtado Gil Universidad CEU San Pablo (lluis.hurtadogil@ceu.es) Radu Stoica Universite de Lorraine, IECL (radu-stefan.stoica@univlorraine.fr)

More information

Solutions to exam : 1FA352 Quantum Mechanics 10 hp 1

Solutions to exam : 1FA352 Quantum Mechanics 10 hp 1 Solutions to exam 6--6: FA35 Quantum Mechanics hp Problem (4 p): (a) Define the concept of unitary operator and show that the operator e ipa/ is unitary (p is the momentum operator in one dimension) (b)

More information

Regression Analysis Chapter 2 Simple Linear Regression

Regression Analysis Chapter 2 Simple Linear Regression Regression Analysis Chapter 2 Simple Linear Regression Dr. Bisher Mamoun Iqelan biqelan@iugaza.edu.ps Department of Mathematics The Islamic University of Gaza 2010-2011, Semester 2 Dr. Bisher M. Iqelan

More information

Chapter 5: Models used in conjunction with sampling. J. Kim, W. Fuller (ISU) Chapter 5: Models used in conjunction with sampling 1 / 70

Chapter 5: Models used in conjunction with sampling. J. Kim, W. Fuller (ISU) Chapter 5: Models used in conjunction with sampling 1 / 70 Chapter 5: Models used in conjunction with sampling J. Kim, W. Fuller (ISU) Chapter 5: Models used in conjunction with sampling 1 / 70 Nonresponse Unit Nonresponse: weight adjustment Item Nonresponse:

More information

Mathematics for 3D Graphics

Mathematics for 3D Graphics math 1 Topics Mathematics for 3D Graphics math 1 Points, Vectors, Vertices, Coordinates Dot Products, Cross Products Lines, Planes, Intercepts References Many texts cover the linear algebra used for 3D

More information

Gifted Education Program Plan

Gifted Education Program Plan G E Pg P G E Cv Pg P 1 4-Y Cv P 2017-2021 E P 2, H - 21020 Ey A A THE FOLLOWING SECTION IS REQUIRED IF THE ADMINISTRATIVE UNIT PERMITS EARLY ACCESS TO KINDERGARTEN OR FIRST GRADE. R ECEA 12.08 bg v q y.

More information

Conditional Distributions

Conditional Distributions Conditional Distributions The goal is to provide a general definition of the conditional distribution of Y given X, when (X, Y ) are jointly distributed. Let F be a distribution function on R. Let G(,

More information

F & B Approaches to a simple model

F & B Approaches to a simple model A6523 Signal Modeling, Statistical Inference and Data Mining in Astrophysics Spring 215 http://www.astro.cornell.edu/~cordes/a6523 Lecture 11 Applications: Model comparison Challenges in large-scale surveys

More information

Quantitative Methods in Economics Panel data and generalized least squares

Quantitative Methods in Economics Panel data and generalized least squares Quantitative Methods in Economics Panel data and generalized least squares Maximilian Kasy Harvard University, fall 2016 1 / 22 Roadmap, Part I 1. Linear predictors and least squares regression 2. Conditional

More information

BIOS 2083 Linear Models c Abdus S. Wahed

BIOS 2083 Linear Models c Abdus S. Wahed Chapter 5 206 Chapter 6 General Linear Model: Statistical Inference 6.1 Introduction So far we have discussed formulation of linear models (Chapter 1), estimability of parameters in a linear model (Chapter

More information

A = (a + 1) 2 = a 2 + 2a + 1

A = (a + 1) 2 = a 2 + 2a + 1 A = (a + 1) 2 = a 2 + 2a + 1 1 A = ( (a + b) + 1 ) 2 = (a + b) 2 + 2(a + b) + 1 = a 2 + 2ab + b 2 + 2a + 2b + 1 A = ( (a + b) + 1 ) 2 = (a + b) 2 + 2(a + b) + 1 = a 2 + 2ab + b 2 + 2a + 2b + 1 3 A = (

More information

c. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f

c. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f Essential Skills Chapter f ( x + h) f ( x ). Simplifying the difference quotient Section. h f ( x + h) f ( x ) Example: For f ( x) = 4x 4 x, find and simplify completely. h Answer: 4 8x 4 h. Finding the

More information

EE/Stat 376B Handout #5 Network Information Theory October, 14, Homework Set #2 Solutions

EE/Stat 376B Handout #5 Network Information Theory October, 14, Homework Set #2 Solutions EE/Stat 376B Handout #5 Network Information Theory October, 14, 014 1. Problem.4 parts (b) and (c). Homework Set # Solutions (b) Consider h(x + Y ) h(x + Y Y ) = h(x Y ) = h(x). (c) Let ay = Y 1 + Y, where

More information

University of Oxford. Statistical Methods Autocorrelation. Identification and Estimation

University of Oxford. Statistical Methods Autocorrelation. Identification and Estimation University of Oxford Statistical Methods Autocorrelation Identification and Estimation Dr. Órlaith Burke Michaelmas Term, 2011 Department of Statistics, 1 South Parks Road, Oxford OX1 3TG Contents 1 Model

More information

p r * < & *'& ' 6 y S & S f \ ) <» d «~ * c t U * p c ^ 6 *

p r * < & *'& ' 6 y S & S f \ ) <» d «~ * c t U * p c ^ 6 * B. - - F -.. * i r > --------------------------------------------------------------------------- ^ l y ^ & * s ^ C i$ j4 A m A ^ v < ^ 4 ^ - 'C < ^y^-~ r% ^, n y ^, / f/rf O iy r0 ^ C ) - j V L^-**s *-y

More information

Contents. MATH 32B-2 (18W) (L) G. Liu / (TA) A. Zhou Calculus of Several Variables. 1 Multiple Integrals 3. 2 Vector Fields 9

Contents. MATH 32B-2 (18W) (L) G. Liu / (TA) A. Zhou Calculus of Several Variables. 1 Multiple Integrals 3. 2 Vector Fields 9 MATH 32B-2 (8W) (L) G. Liu / (TA) A. Zhou Calculus of Several Variables Contents Multiple Integrals 3 2 Vector Fields 9 3 Line and Surface Integrals 5 4 The Classical Integral Theorems 9 MATH 32B-2 (8W)

More information

Jesper Møller ) and Kateřina Helisová )

Jesper Møller ) and Kateřina Helisová ) Jesper Møller ) and ) ) Aalborg University (Denmark) ) Czech Technical University/Charles University in Prague 5 th May 2008 Outline 1. Describing model 2. Simulation 3. Power tessellation of a union of

More information

SGN Advanced Signal Processing: Lecture 8 Parameter estimation for AR and MA models. Model order selection

SGN Advanced Signal Processing: Lecture 8 Parameter estimation for AR and MA models. Model order selection SG 21006 Advanced Signal Processing: Lecture 8 Parameter estimation for AR and MA models. Model order selection Ioan Tabus Department of Signal Processing Tampere University of Technology Finland 1 / 28

More information

El X Cs. Allen Percival. a I i I ( INSTRUCTOR(S) Training Roster TRAINING TOPIC DATE/TIME OFTRAINING. PRINT NAME (Legibly)

El X Cs. Allen Percival. a I i I ( INSTRUCTOR(S) Training Roster TRAINING TOPIC DATE/TIME OFTRAINING. PRINT NAME (Legibly) WestVuginiaUniwisiry Allen Percival Page of DATE/TIME OFTRAINING i 'I a I i I ( seed' i t e. A N, 1,..,....e...e..., -.1.6-!;:or...., 3. El X Cs 4. 8. 10. \VestVirprvAUrvvetsny CAC Page of v..,ltd 1 5

More information

Fall 2017 STAT 532 Homework Peter Hoff. 1. Let P be a probability measure on a collection of sets A.

Fall 2017 STAT 532 Homework Peter Hoff. 1. Let P be a probability measure on a collection of sets A. 1. Let P be a probability measure on a collection of sets A. (a) For each n N, let H n be a set in A such that H n H n+1. Show that P (H n ) monotonically converges to P ( k=1 H k) as n. (b) For each n

More information

Bayesian Interpretations of Regularization

Bayesian Interpretations of Regularization Bayesian Interpretations of Regularization Charlie Frogner 9.50 Class 15 April 1, 009 The Plan Regularized least squares maps {(x i, y i )} n i=1 to a function that minimizes the regularized loss: f S

More information

Econ 5150: Applied Econometrics Dynamic Demand Model Model Selection. Sung Y. Park CUHK

Econ 5150: Applied Econometrics Dynamic Demand Model Model Selection. Sung Y. Park CUHK Econ 5150: Applied Econometrics Dynamic Demand Model Model Selection Sung Y. Park CUHK Simple dynamic models A typical simple model: y t = α 0 + α 1 y t 1 + α 2 y t 2 + x tβ 0 x t 1β 1 + u t, where y t

More information

Boltzmann Equation and Hydrodynamics beyond Navier-Stokes

Boltzmann Equation and Hydrodynamics beyond Navier-Stokes Boltzmann Equation and Hydrodynamics beyond Navier-Stokes Alexander Bobylev Keldysh Institute for Applied Mathematics, RAS, Moscow Chapman-Enskog method and Burnett equations Notation: f (x, v, t) - distribution

More information

IP WEIGHTING AND MARGINAL STRUCTURAL MODELS (CHAPTER 12) BIOS IPW and MSM

IP WEIGHTING AND MARGINAL STRUCTURAL MODELS (CHAPTER 12) BIOS IPW and MSM IP WEIGHTING AND MARGINAL STRUCTURAL MODELS (CHAPTER 12) BIOS 776 1 12 IPW and MSM IP weighting and marginal structural models ( 12) Outline 12.1 The causal question 12.2 Estimating IP weights via modeling

More information

(308 ) EXAMPLES. 1. FIND the quotient and remainder when. II. 1. Find a root of the equation x* = +J Find a root of the equation x 6 = ^ - 1.

(308 ) EXAMPLES. 1. FIND the quotient and remainder when. II. 1. Find a root of the equation x* = +J Find a root of the equation x 6 = ^ - 1. (308 ) EXAMPLES. N 1. FIND the quotient and remainder when is divided by x 4. I. x 5 + 7x* + 3a; 3 + 17a 2 + 10* - 14 2. Expand (a + bx) n in powers of x, and then obtain the first derived function of

More information

Generalized linear models

Generalized linear models Generalized linear models Søren Højsgaard Department of Mathematical Sciences Aalborg University, Denmark October 29, 202 Contents Densities for generalized linear models. Mean and variance...............................

More information

G-ESTIMATION OF STRUCTURAL NESTED MODELS (CHAPTER 14) BIOS G-Estimation

G-ESTIMATION OF STRUCTURAL NESTED MODELS (CHAPTER 14) BIOS G-Estimation G-ESTIMATION OF STRUCTURAL NESTED MODELS (CHAPTER 14) BIOS 776 1 14 G-Estimation ( G-Estimation of Structural Nested Models 14) Outline 14.1 The causal question revisited 14.2 Exchangeability revisited

More information

Physics 7A Lecture 2 Fall 2014 Final Solutions. December 22, 2014

Physics 7A Lecture 2 Fall 2014 Final Solutions. December 22, 2014 Physics 7A Lecture Fall 04 Final Solutions December, 04 PROBLEM The string is oscillating in a transverse manner. The wave velocity of the string is thus T s v = µ, where T s is tension and µ is the linear

More information