Lecture 17: Minimum Variance Unbiased (MVUB) Estimators

Μέγεθος: px
Εμφάνιση ξεκινά από τη σελίδα:

Download "Lecture 17: Minimum Variance Unbiased (MVUB) Estimators"

Transcript

1 ECE 830 Fall 2011 Statistical Sigal Processig istructor: R. Nowak, scribe: Iseok Heo Lecture 17: Miimum Variace Ubiased (MVUB Estimators Ultimately, we would like to be able to argue that a give estimator is(or is ot optimal i some sese. Usually this is very difficult, but i certai cases it is possible to make precise statemets about optimality. MVUB estimators are oe class where this is sometimes the case. First let s quickly review some key estimatio-theoretic cocepts. 1 Review of key estimatio cocepts Observatio model Loss Example 1 X p(x θ, x X, θ Θ l : Θ Θ R + Risk : expected loss of estimator ˆθ(x l 2 : l(θ 1, θ 2 θ 1 θ l 1 : l(θ 1, θ 2 θ 1 θ 2 1 log-likelihood : l(θ 1, θ 2 log p(x θ 2, where x p(x θ 1 R(θ, ˆθ E[l(θ, ˆθ MSE : the l 2 risk is usually called the mea square error : MSE(ˆθ E[ θ ˆθ 2 2 Recall that the MSE ca be decomposed ito the bias ad variace MSE(ˆθ E[ θ ˆθ 2 2 E[ θ Eˆθ + Eˆθ ˆθ 2 2 θ Eˆθ E[(θ Eˆθ T (Eˆθ ˆθ + E[ Eˆθ ˆθ 2 2 θ Eˆθ E[ ˆθ Eˆθ 2 2 bias 2 (ˆθ + var(ˆθ It is usually impossible to desig ˆθ to miimize the MSE because the bias depeds o θ, which is of course ukow. But suppose we restrict our attetio to ubiased estimators; i.e., ˆθ satisfyig Eˆθ θ. The MSE(ˆθ var(ˆθ ad var(ˆθ does ot deped o θ. So a realizable approach is to optimize the MSE with respect to the class of ubiased estimators. The Miimum Variace UBiased (MVUB estimator is defied as θ arg mi E[ ˆθ Eˆθ 2 2 ˆθ : E[ˆθθ 1

2 Lecture 17: Miimum Variace Ubiased (MVUB Estimators 2 Example 2 X 1, X 2,..., X iid N (θ, 1 Is this the MVUB estimator? ˆθ 1 xi E[ˆθ θ [ 1 2 MSE(ˆθ E xi Eˆθ var xi 2 Fidig the MVUB estimator 1 2 var(xi 1 Fidig the MVUB estimator ca be difficult, but sometimes it is easy to verify that a particular estimator is MVUB. Theorem 1 (Cramér-Rao Lower Boud (CRLB Let x deote a -dimesioal radom vector with desity p(x θ, θ R k Assume that the first ad secod derivatives of log p(x θ exist. Let ˆθ ˆθ(x be a ubiased estimator of θ. The the error covariace satisfies the matrix iequality E[(ˆθ Eˆθ(ˆθ Eˆθ T I 1 (θ where I(θ is the Fisher-Iformatio matrix with i,jth elemet [ I ij (θ 2 log p(x θ E i j Remark : The meaig of the iequality is that the eigevalues of the symmetric matrix are o-egative. As a cosequece C : E[(ˆθ Eˆθ(ˆθ Eˆθ T I 1 (θ C I 1 (θ var(ˆθ tr(e[(ˆθ Eˆθ(ˆθ Eˆθ T tr(c tr(i 1 (θ Proof : We will prove the scalar case (θ R. The geeral case follows i a similar fashio. The Fisher- Iformatio is scalar i this case : [ I(θ 2 log p(x θ E 2 Before proceedig we will show first that [( 2 log p(x θ I(θ E

3 Lecture 17: Miimum Variace Ubiased (MVUB Estimators 3 To this ed, first observe that 2 log p(x θ 2 ( log p(x θ 1 p 2 (x θ p(x θ p(x θ p(x θ p(x θ p(x θ p(x θ p(x θ p(x θ p(x θ p(x θ 2 Cosider the expectatio of the secod term : [ 1 2 p(x θ 1 2 p(x θ E p(x θ 2 p(x θ 2 p(x θdx 2 p(x θ 2 dx 2 2 p(x θdx 2 2 (1 0 Thus, [ 2 log p(x θ E 2 [( 2 1 p(x θ E p(x θ [( 2 log p(x θ E The gradiet of the log-likelihood is called the score fuctio. Let s deote it S(θ, x : log p(x θ Observe that the MLE satisfies S(ˆθ, x 0. Also ote that E[S(θ, x log p(x θ p(x θ dx p(x θdx 0 ad therefore the Fisher-Iformatio is the variace of the score fuctio [( 2 log p(x θ I(θ E So we see that the Fisher-Iformatio measures the variability of the score fuctio at θ θ. We will also show that E[S(θ, x(ˆθ θ 1 To verify this, ote that E[ˆθ θ (ˆθ θ p(x θ dx 0

4 Lecture 17: Miimum Variace Ubiased (MVUB Estimators 4 sice ˆθ is ubiased. Take the derivative 0 (ˆθ θp(x θdx ( p(x θdx + (ˆθ θ p(x θ dx 1 + (ˆθ θ log p(x θ p(x θ dx 1 + E[S(θ, x(ˆθ θ Now we apply the Cauchy-Schwarz iequality. (i.e., f(xg(xdx f 2 (xdx g 2 (xdx var(ˆθ Example 3 X 1, X 2,..., X iid N (θ, 1 E[S(θ, x(ˆθ θ 1 θθ 1 E[S(θ, x(ˆθ θ E[S 2 (θ, x E[(ˆθ θ 2 var(s(θ, x var(ˆθ 1 var(s(θ, x I 1 (θ log p(x θ log p(x i θ log p(x θ log p(x i θ log (x i θ [( 2 log p(x θ I(θ E But recall the ubiased estimator e (x i θ2 2 2π E[(x i θ 2 MVUB estimator variace 1 ˆθ 1 x i var( θ 2 1 ˆθ is the MVUB estimator!

5 Lecture 17: Miimum Variace Ubiased (MVUB Estimators 5 3 Efficiecy A ubiased estimator that achieved the CRLB is said to be efficiet. Efficiet estimators are MVUB, but ot all MVUB estimators are ecessarily efficiet. A estimator ˆθ is said to be asymptotically efficiet if it achieves the CRLB, as. Recall that uder mild regularity coditios, the MLE has a asymptotic distributio ˆθ asymp N (θ, 1 I(θ ad so ˆθ is asymptotically ubiased ad so it is asymptotically efficiet. var(ˆθ 1 I 1 (θ

Homework for 1/27 Due 2/5

Homework for 1/27 Due 2/5 Name: ID: Homework for /7 Due /5. [ 8-3] I Example D of Sectio 8.4, the pdf of the populatio distributio is + αx x f(x α) =, α, otherwise ad the method of momets estimate was foud to be ˆα = 3X (where

Διαβάστε περισσότερα

Solutions: Homework 3

Solutions: Homework 3 Solutios: Homework 3 Suppose that the radom variables Y,, Y satisfy Y i = βx i + ε i : i,, where x,, x R are fixed values ad ε,, ε Normal0, σ ) with σ R + kow Fid ˆβ = MLEβ) IND Solutio: Observe that Y

Διαβάστε περισσότερα

Last Lecture. Biostatistics Statistical Inference Lecture 19 Likelihood Ratio Test. Example of Hypothesis Testing.

Last Lecture. Biostatistics Statistical Inference Lecture 19 Likelihood Ratio Test. Example of Hypothesis Testing. Last Lecture Biostatistics 602 - Statistical Iferece Lecture 19 Likelihood Ratio Test Hyu Mi Kag March 26th, 2013 Describe the followig cocepts i your ow words Hypothesis Null Hypothesis Alterative Hypothesis

Διαβάστε περισσότερα

true value θ. Fisher information is meaningful for families of distribution which are regular: W (x) f(x θ)dx

true value θ. Fisher information is meaningful for families of distribution which are regular: W (x) f(x θ)dx Fisher Iformatio April 6, 26 Debdeep Pati Fisher Iformatio Assume X fx θ pdf or pmf with θ Θ R. Defie I X θ E θ [ θ log fx θ 2 ] where θ log fx θ is the derivative of the log-likelihood fuctio evaluated

Διαβάστε περισσότερα

Solution Series 9. i=1 x i and i=1 x i.

Solution Series 9. i=1 x i and i=1 x i. Lecturer: Prof. Dr. Mete SONER Coordinator: Yilin WANG Solution Series 9 Q1. Let α, β >, the p.d.f. of a beta distribution with parameters α and β is { Γ(α+β) Γ(α)Γ(β) f(x α, β) xα 1 (1 x) β 1 for < x

Διαβάστε περισσότερα

Other Test Constructions: Likelihood Ratio & Bayes Tests

Other Test Constructions: Likelihood Ratio & Bayes Tests Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :

Διαβάστε περισσότερα

SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES. Reading: QM course packet Ch 5 up to 5.6

SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES. Reading: QM course packet Ch 5 up to 5.6 SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES Readig: QM course packet Ch 5 up to 5. 1 ϕ (x) = E = π m( a) =1,,3,4,5 for xa (x) = πx si L L * = πx L si L.5 ϕ' -.5 z 1 (x) = L si

Διαβάστε περισσότερα

Lecture 3: Asymptotic Normality of M-estimators

Lecture 3: Asymptotic Normality of M-estimators Lecture 3: Asymptotic Istructor: Departmet of Ecoomics Staford Uiversity Prepared by Webo Zhou, Remi Uiversity Refereces Takeshi Amemiya, 1985, Advaced Ecoometrics, Harvard Uiversity Press Newey ad McFadde,

Διαβάστε περισσότερα

Statistical Inference I Locally most powerful tests

Statistical Inference I Locally most powerful tests Statistical Inference I Locally most powerful tests Shirsendu Mukherjee Department of Statistics, Asutosh College, Kolkata, India. shirsendu st@yahoo.co.in So far we have treated the testing of one-sided

Διαβάστε περισσότερα

1. For each of the following power series, find the interval of convergence and the radius of convergence:

1. For each of the following power series, find the interval of convergence and the radius of convergence: Math 6 Practice Problems Solutios Power Series ad Taylor Series 1. For each of the followig power series, fid the iterval of covergece ad the radius of covergece: (a ( 1 x Notice that = ( 1 +1 ( x +1.

Διαβάστε περισσότερα

Outline. Detection Theory. Background. Background (Cont.)

Outline. Detection Theory. Background. Background (Cont.) Outlie etectio heory Chapter7. etermiistic Sigals with Ukow Parameters afiseh S. Mazloum ov. 3th Backgroud Importace of sigal iformatio Ukow amplitude Ukow arrival time Siusoidal detectio Classical liear

Διαβάστε περισσότερα

ECE598: Information-theoretic methods in high-dimensional statistics Spring 2016

ECE598: Information-theoretic methods in high-dimensional statistics Spring 2016 ECE598: Information-theoretic methods in high-dimensional statistics Spring 06 Lecture 7: Information bound Lecturer: Yihong Wu Scribe: Shiyu Liang, Feb 6, 06 [Ed. Mar 9] Recall the Chi-squared divergence

Διαβάστε περισσότερα

Lecture 21: Properties and robustness of LSE

Lecture 21: Properties and robustness of LSE Lecture 21: Properties and robustness of LSE BLUE: Robustness of LSE against normality We now study properties of l τ β and σ 2 under assumption A2, i.e., without the normality assumption on ε. From Theorem

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max

Διαβάστε περισσότερα

6. MAXIMUM LIKELIHOOD ESTIMATION

6. MAXIMUM LIKELIHOOD ESTIMATION 6 MAXIMUM LIKELIHOOD ESIMAION [1] Maximum Likelihood Estimator (1) Cases in which θ (unknown parameter) is scalar Notational Clarification: From now on, we denote the true value of θ as θ o hen, view θ

Διαβάστε περισσότερα

The Equivalence Theorem in Optimal Design

The Equivalence Theorem in Optimal Design he Equivalece heorem i Optimal Desig Raier Schwabe & homas Schmelter, Otto vo Guericke Uiversity agdeburg Bayer Scherig Pharma, Berli rschwabe@ovgu.de PODE 007 ay 4, 007 Outlie Prologue: Simple eamples.

Διαβάστε περισσότερα

LAD Estimation for Time Series Models With Finite and Infinite Variance

LAD Estimation for Time Series Models With Finite and Infinite Variance LAD Estimatio for Time Series Moels With Fiite a Ifiite Variace Richar A. Davis Colorao State Uiversity William Dusmuir Uiversity of New South Wales 1 LAD Estimatio for ARMA Moels fiite variace ifiite

Διαβάστε περισσότερα

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8 questions or comments to Dan Fetter 1

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8  questions or comments to Dan Fetter 1 Eon : Fall 8 Suggested Solutions to Problem Set 8 Email questions or omments to Dan Fetter Problem. Let X be a salar with density f(x, θ) (θx + θ) [ x ] with θ. (a) Find the most powerful level α test

Διαβάστε περισσότερα

Bessel function for complex variable

Bessel function for complex variable Besse fuctio for compex variabe Kauhito Miuyama May 4, 7 Besse fuctio The Besse fuctio Z ν () is the fuctio wich satisfies + ) ( + ν Z ν () =. () Three kids of the soutios of this equatio are give by {

Διαβάστε περισσότερα

Lecture 12: Pseudo likelihood approach

Lecture 12: Pseudo likelihood approach Lecture 12: Pseudo likelihood approach Pseudo MLE Let X 1,...,X n be a random sample from a pdf in a family indexed by two parameters θ and π with likelihood l(θ,π). The method of pseudo MLE may be viewed

Διαβάστε περισσότερα

Second-order asymptotic comparison of the MLE and MCLE of a natural parameter for a truncated exponential family of distributions

Second-order asymptotic comparison of the MLE and MCLE of a natural parameter for a truncated exponential family of distributions A Ist Stat Math 06 68:469 490 DOI 0.007/s046-04-050-9 Secod-order asymptotic compariso of the MLE ad MCLE of a atural parameter for a trucated expoetial family of distributios Masafumi Akahira Received:

Διαβάστε περισσότερα

L.K.Gupta (Mathematic Classes) www.pioeermathematics.com MOBILE: 985577, 4677 + {JEE Mai 04} Sept 0 Name: Batch (Day) Phoe No. IT IS NOT ENOUGH TO HAVE A GOOD MIND, THE MAIN THING IS TO USE IT WELL Marks:

Διαβάστε περισσότερα

Three Classical Tests; Wald, LM(Score), and LR tests

Three Classical Tests; Wald, LM(Score), and LR tests Eco 60 Three Classical Tests; Wald, MScore, ad R tests Suppose that we have the desity l y; θ of a model with the ull hypothesis of the form H 0 ; θ θ 0. et θ be the lo-likelihood fuctio of the model ad

Διαβάστε περισσότερα

ST5224: Advanced Statistical Theory II

ST5224: Advanced Statistical Theory II ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known

Διαβάστε περισσότερα

A Note on Intuitionistic Fuzzy. Equivalence Relation

A Note on Intuitionistic Fuzzy. Equivalence Relation International Mathematical Forum, 5, 2010, no. 67, 3301-3307 A Note on Intuitionistic Fuzzy Equivalence Relation D. K. Basnet Dept. of Mathematics, Assam University Silchar-788011, Assam, India dkbasnet@rediffmail.com

Διαβάστε περισσότερα

Lecture 7: Overdispersion in Poisson regression

Lecture 7: Overdispersion in Poisson regression Lecture 7: Overdispersion in Poisson regression Claudia Czado TU München c (Claudia Czado, TU Munich) ZFS/IMS Göttingen 2004 0 Overview Introduction Modeling overdispersion through mixing Score test for

Διαβάστε περισσότερα

Bayesian statistics. DS GA 1002 Probability and Statistics for Data Science.

Bayesian statistics. DS GA 1002 Probability and Statistics for Data Science. Bayesian statistics DS GA 1002 Probability and Statistics for Data Science http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall17 Carlos Fernandez-Granda Frequentist vs Bayesian statistics In frequentist

Διαβάστε περισσότερα

Every set of first-order formulas is equivalent to an independent set

Every set of first-order formulas is equivalent to an independent set Every set of first-order formulas is equivalent to an independent set May 6, 2008 Abstract A set of first-order formulas, whatever the cardinality of the set of symbols, is equivalent to an independent

Διαβάστε περισσότερα

Example Sheet 3 Solutions

Example Sheet 3 Solutions Example Sheet 3 Solutions. i Regular Sturm-Liouville. ii Singular Sturm-Liouville mixed boundary conditions. iii Not Sturm-Liouville ODE is not in Sturm-Liouville form. iv Regular Sturm-Liouville note

Διαβάστε περισσότερα

Diane Hu LDA for Audio Music April 12, 2010

Diane Hu LDA for Audio Music April 12, 2010 Diae Hu LDA for Audio Music April, 00 Terms Model Terms (per sog: Variatioal Terms: p( α Γ( i α i i Γ(α i p( p(, β p(c, A j Σ i α i i i ( V / ep β (i j ij (3 q( γ Γ( i γ i i Γ(γ i q( φ q( ω { } (c A T

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr (T t N n) Pr (max (X 1,..., X N ) t N n) Pr (max

Διαβάστε περισσότερα

Theorem 8 Let φ be the most powerful size α test of H

Theorem 8 Let φ be the most powerful size α test of H Testing composite hypotheses Θ = Θ 0 Θ c 0 H 0 : θ Θ 0 H 1 : θ Θ c 0 Definition 16 A test φ is a uniformly most powerful (UMP) level α test for H 0 vs. H 1 if φ has level α and for any other level α test

Διαβάστε περισσότερα

Section 9.2 Polar Equations and Graphs

Section 9.2 Polar Equations and Graphs 180 Section 9. Polar Equations and Graphs In this section, we will be graphing polar equations on a polar grid. In the first few examples, we will write the polar equation in rectangular form to help identify

Διαβάστε περισσότερα

CHAPTER 103 EVEN AND ODD FUNCTIONS AND HALF-RANGE FOURIER SERIES

CHAPTER 103 EVEN AND ODD FUNCTIONS AND HALF-RANGE FOURIER SERIES CHAPTER 3 EVEN AND ODD FUNCTIONS AND HALF-RANGE FOURIER SERIES EXERCISE 364 Page 76. Determie the Fourier series for the fuctio defied by: f(x), x, x, x which is periodic outside of this rage of period.

Διαβάστε περισσότερα

4.6 Autoregressive Moving Average Model ARMA(1,1)

4.6 Autoregressive Moving Average Model ARMA(1,1) 84 CHAPTER 4. STATIONARY TS MODELS 4.6 Autoregressive Moving Average Model ARMA(,) This section is an introduction to a wide class of models ARMA(p,q) which we will consider in more detail later in this

Διαβάστε περισσότερα

Tridiagonal matrices. Gérard MEURANT. October, 2008

Tridiagonal matrices. Gérard MEURANT. October, 2008 Tridiagonal matrices Gérard MEURANT October, 2008 1 Similarity 2 Cholesy factorizations 3 Eigenvalues 4 Inverse Similarity Let α 1 ω 1 β 1 α 2 ω 2 T =......... β 2 α 1 ω 1 β 1 α and β i ω i, i = 1,...,

Διαβάστε περισσότερα

Στα επόμενα θεωρούμε ότι όλα συμβαίνουν σε ένα χώρο πιθανότητας ( Ω,,P) Modes of convergence: Οι τρόποι σύγκλισης μιας ακολουθίας τ.μ.

Στα επόμενα θεωρούμε ότι όλα συμβαίνουν σε ένα χώρο πιθανότητας ( Ω,,P) Modes of convergence: Οι τρόποι σύγκλισης μιας ακολουθίας τ.μ. Στα πόμνα θωρούμ ότι όλα συμβαίνουν σ ένα χώρο πιθανότητας ( Ω,,). Modes of covergece: Οι τρόποι σύγκλισης μιας ακολουθίας τ.μ. { } ίναι οι ξής: σ μια τ.μ.. Ισχυρή σύγκλιση strog covergece { } lim = =.

Διαβάστε περισσότερα

Introduction to the ML Estimation of ARMA processes

Introduction to the ML Estimation of ARMA processes Introduction to the ML Estimation of ARMA processes Eduardo Rossi University of Pavia October 2013 Rossi ARMA Estimation Financial Econometrics - 2013 1 / 1 We consider the AR(p) model: Y t = c + φ 1 Y

Διαβάστε περισσότερα

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required)

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required) Phys460.nb 81 ψ n (t) is still the (same) eigenstate of H But for tdependent H. The answer is NO. 5.5.5. Solution for the tdependent Schrodinger s equation If we assume that at time t 0, the electron starts

Διαβάστε περισσότερα

6.3 Forecasting ARMA processes

6.3 Forecasting ARMA processes 122 CHAPTER 6. ARMA MODELS 6.3 Forecasting ARMA processes The purpose of forecasting is to predict future values of a TS based on the data collected to the present. In this section we will discuss a linear

Διαβάστε περισσότερα

MATH 38061/MATH48061/MATH68061: MULTIVARIATE STATISTICS Solutions to Problems on Matrix Algebra

MATH 38061/MATH48061/MATH68061: MULTIVARIATE STATISTICS Solutions to Problems on Matrix Algebra MATH 38061/MATH48061/MATH68061: MULTIVARIATE STATISTICS Solutios to Poblems o Matix Algeba 1 Let A be a squae diagoal matix takig the fom a 11 0 0 0 a 22 0 A 0 0 a pp The ad So, log det A t log A t log

Διαβάστε περισσότερα

2 Composition. Invertible Mappings

2 Composition. Invertible Mappings Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Composition. Invertible Mappings In this section we discuss two procedures for creating new mappings from old ones, namely,

Διαβάστε περισσότερα

= λ 1 1 e. = λ 1 =12. has the properties e 1. e 3,V(Y

= λ 1 1 e. = λ 1 =12. has the properties e 1. e 3,V(Y Stat 50 Homework Solutions Spring 005. (a λ λ λ 44 (b trace( λ + λ + λ 0 (c V (e x e e λ e e λ e (λ e by definition, the eigenvector e has the properties e λ e and e e. (d λ e e + λ e e + λ e e 8 6 4 4

Διαβάστε περισσότερα

Estimation for ARMA Processes with Stable Noise. Matt Calder & Richard A. Davis Colorado State University

Estimation for ARMA Processes with Stable Noise. Matt Calder & Richard A. Davis Colorado State University Estimation for ARMA Processes with Stable Noise Matt Calder & Richard A. Davis Colorado State University rdavis@stat.colostate.edu 1 ARMA processes with stable noise Review of M-estimation Examples of

Διαβάστε περισσότερα

INTEGRATION OF THE NORMAL DISTRIBUTION CURVE

INTEGRATION OF THE NORMAL DISTRIBUTION CURVE INTEGRATION OF THE NORMAL DISTRIBUTION CURVE By Tom Irvie Email: tomirvie@aol.com March 3, 999 Itroductio May processes have a ormal probability distributio. Broadbad radom vibratio is a example. The purpose

Διαβάστε περισσότερα

Lecture 34 Bootstrap confidence intervals

Lecture 34 Bootstrap confidence intervals Lecture 34 Bootstrap confidence intervals Confidence Intervals θ: an unknown parameter of interest We want to find limits θ and θ such that Gt = P nˆθ θ t If G 1 1 α is known, then P θ θ = P θ θ = 1 α

Διαβάστε περισσότερα

IIT JEE (2013) (Trigonomtery 1) Solutions

IIT JEE (2013) (Trigonomtery 1) Solutions L.K. Gupta (Mathematic Classes) www.pioeermathematics.com MOBILE: 985577, 677 (+) PAPER B IIT JEE (0) (Trigoomtery ) Solutios TOWARDS IIT JEE IS NOT A JOURNEY, IT S A BATTLE, ONLY THE TOUGHEST WILL SURVIVE

Διαβάστε περισσότερα

Final: May 17 (W) 6:30 pm In WH 100E Notice the time changes! Introduction to Statistics (Math 502)

Final: May 17 (W) 6:30 pm In WH 100E Notice the time changes! Introduction to Statistics (Math 502) Fial: May 7 (W) 6:30 pm I WH 00E Notice the time chages! Itroductio to Statistics (Math 50) WH 00E MWF 8:30am-9:30am Office: WH 3 Office hours: M, T 3-4pm Textbook: Statistical Iferece (d ed.) by George

Διαβάστε περισσότερα

Ψηφιακή Επεξεργασία Εικόνας

Ψηφιακή Επεξεργασία Εικόνας ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ Ψηφιακή Επεξεργασία Εικόνας Φιλτράρισμα στο πεδίο των συχνοτήτων Διδάσκων : Αναπληρωτής Καθηγητής Νίκου Χριστόφορος Άδειες Χρήσης Το παρόν εκπαιδευτικό

Διαβάστε περισσότερα

Math221: HW# 1 solutions

Math221: HW# 1 solutions Math: HW# solutions Andy Royston October, 5 7.5.7, 3 rd Ed. We have a n = b n = a = fxdx = xdx =, x cos nxdx = x sin nx n sin nxdx n = cos nx n = n n, x sin nxdx = x cos nx n + cos nxdx n cos n = + sin

Διαβάστε περισσότερα

Problem Set 3: Solutions

Problem Set 3: Solutions CMPSCI 69GG Applied Information Theory Fall 006 Problem Set 3: Solutions. [Cover and Thomas 7.] a Define the following notation, C I p xx; Y max X; Y C I p xx; Ỹ max I X; Ỹ We would like to show that C

Διαβάστε περισσότερα

A study on generalized absolute summability factors for a triangular matrix

A study on generalized absolute summability factors for a triangular matrix Proceedigs of the Estoia Acadey of Scieces, 20, 60, 2, 5 20 doi: 0.376/proc.20.2.06 Available olie at www.eap.ee/proceedigs A study o geeralized absolute suability factors for a triagular atrix Ere Savaş

Διαβάστε περισσότερα

Chapter 6: Systems of Linear Differential. be continuous functions on the interval

Chapter 6: Systems of Linear Differential. be continuous functions on the interval Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations

Διαβάστε περισσότερα

C.S. 430 Assignment 6, Sample Solutions

C.S. 430 Assignment 6, Sample Solutions C.S. 430 Assignment 6, Sample Solutions Paul Liu November 15, 2007 Note that these are sample solutions only; in many cases there were many acceptable answers. 1 Reynolds Problem 10.1 1.1 Normal-order

Διαβάστε περισσότερα

Fractional Colorings and Zykov Products of graphs

Fractional Colorings and Zykov Products of graphs Fractional Colorings and Zykov Products of graphs Who? Nichole Schimanski When? July 27, 2011 Graphs A graph, G, consists of a vertex set, V (G), and an edge set, E(G). V (G) is any finite set E(G) is

Διαβάστε περισσότερα

Supplementary Materials: Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent

Supplementary Materials: Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Supplemetary Materials: Tradig Computatio for Commuicatio: istributed Stochastic ual Coordiate Ascet Tiabao Yag NEC Labs America, Cupertio, CA 954 tyag@ec-labs.com Proof of Theorem ad Theorem For the proof

Διαβάστε περισσότερα

Lecture 13 - Root Space Decomposition II

Lecture 13 - Root Space Decomposition II Lecture 13 - Root Space Decomposition II October 18, 2012 1 Review First let us recall the situation. Let g be a simple algebra, with maximal toral subalgebra h (which we are calling a CSA, or Cartan Subalgebra).

Διαβάστε περισσότερα

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch: HOMEWORK 4 Problem a For the fast loading case, we want to derive the relationship between P zz and λ z. We know that the nominal stress is expressed as: P zz = ψ λ z where λ z = λ λ z. Therefore, applying

Διαβάστε περισσότερα

Proof of Lemmas Lemma 1 Consider ξ nt = r

Proof of Lemmas Lemma 1 Consider ξ nt = r Supplemetary Material to "GMM Estimatio of Spatial Pael Data Models with Commo Factors ad Geeral Space-Time Filter" (Not for publicatio) Wei Wag & Lug-fei Lee April 207 Proof of Lemmas Lemma Cosider =

Διαβάστε περισσότερα

Solve the difference equation

Solve the difference equation Solve the differece equatio Solutio: y + 3 3y + + y 0 give tat y 0 4, y 0 ad y 8. Let Z{y()} F() Taig Z-trasform o both sides i (), we get y + 3 3y + + y 0 () Z y + 3 3y + + y Z 0 Z y + 3 3Z y + + Z y

Διαβάστε περισσότερα

Finite Field Problems: Solutions

Finite Field Problems: Solutions Finite Field Problems: Solutions 1. Let f = x 2 +1 Z 11 [x] and let F = Z 11 [x]/(f), a field. Let Solution: F =11 2 = 121, so F = 121 1 = 120. The possible orders are the divisors of 120. Solution: The

Διαβάστε περισσότερα

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β 3.4 SUM AND DIFFERENCE FORMULAS Page Theorem cos(αβ cos α cos β -sin α cos(α-β cos α cos β sin α NOTE: cos(αβ cos α cos β cos(α-β cos α -cos β Proof of cos(α-β cos α cos β sin α Let s use a unit circle

Διαβάστε περισσότερα

The Heisenberg Uncertainty Principle

The Heisenberg Uncertainty Principle Chemistry 460 Sprig 015 Dr. Jea M. Stadard March, 015 The Heiseberg Ucertaity Priciple A policema pulls Werer Heiseberg over o the Autobah for speedig. Policema: Sir, do you kow how fast you were goig?

Διαβάστε περισσότερα

SCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions

SCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions SCHOOL OF MATHEMATICAL SCIENCES GLMA Linear Mathematics 00- Examination Solutions. (a) i. ( + 5i)( i) = (6 + 5) + (5 )i = + i. Real part is, imaginary part is. (b) ii. + 5i i ( + 5i)( + i) = ( i)( + i)

Διαβάστε περισσότερα

1. Matrix Algebra and Linear Economic Models

1. Matrix Algebra and Linear Economic Models Matrix Algebra ad Liear Ecoomic Models Refereces Ch 3 (Turkigto); Ch 4 5 (Klei) [] Motivatio Oe market equilibrium Model Assume perfectly competitive market: Both buyers ad sellers are price-takers Demad:

Διαβάστε περισσότερα

Homework 3 Solutions

Homework 3 Solutions Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For

Διαβάστε περισσότερα

Exam Statistics 6 th September 2017 Solution

Exam Statistics 6 th September 2017 Solution Exam Statstcs 6 th September 17 Soluto Maura Mezzett Exercse 1 Let (X 1,..., X be a raom sample of... raom varables. Let f θ (x be the esty fucto. Let ˆθ be the MLE of θ, θ be the true parameter, L(θ be

Διαβάστε περισσότερα

α β

α β 6. Eerg, Mometum coefficiets for differet velocit distributios Rehbock obtaied ) For Liear Velocit Distributio α + ε Vmax { } Vmax ε β +, i which ε v V o Give: α + ε > ε ( α ) Liear velocit distributio

Διαβάστε περισσότερα

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality The Probabilistic Method - Probabilistic Techniques Lecture 7: The Janson Inequality Sotiris Nikoletseas Associate Professor Computer Engineering and Informatics Department 2014-2015 Sotiris Nikoletseas,

Διαβάστε περισσότερα

n r f ( n-r ) () x g () r () x (1.1) = Σ g() x = Σ n f < -n+ r> g () r -n + r dx r dx n + ( -n,m) dx -n n+1 1 -n -1 + ( -n,n+1)

n r f ( n-r ) () x g () r () x (1.1) = Σ g() x = Σ n f < -n+ r> g () r -n + r dx r dx n + ( -n,m) dx -n n+1 1 -n -1 + ( -n,n+1) 8 Higher Derivative of the Product of Two Fuctios 8. Leibiz Rule about the Higher Order Differetiatio Theorem 8.. (Leibiz) Whe fuctios f ad g f g are times differetiable, the followig epressio holds. r

Διαβάστε περισσότερα

Matrices and Determinants

Matrices and Determinants Matrices and Determinants SUBJECTIVE PROBLEMS: Q 1. For what value of k do the following system of equations possess a non-trivial (i.e., not all zero) solution over the set of rationals Q? x + ky + 3z

Διαβάστε περισσότερα

Concrete Mathematics Exercises from 30 September 2016

Concrete Mathematics Exercises from 30 September 2016 Concrete Mathematics Exercises from 30 September 2016 Silvio Capobianco Exercise 1.7 Let H(n) = J(n + 1) J(n). Equation (1.8) tells us that H(2n) = 2, and H(2n+1) = J(2n+2) J(2n+1) = (2J(n+1) 1) (2J(n)+1)

Διαβάστε περισσότερα

derivation of the Laplacian from rectangular to spherical coordinates

derivation of the Laplacian from rectangular to spherical coordinates derivation of the Laplacian from rectangular to spherical coordinates swapnizzle 03-03- :5:43 We begin by recognizing the familiar conversion from rectangular to spherical coordinates (note that φ is used

Διαβάστε περισσότερα

Presentation of complex number in Cartesian and polar coordinate system

Presentation of complex number in Cartesian and polar coordinate system 1 a + bi, aεr, bεr i = 1 z = a + bi a = Re(z), b = Im(z) give z = a + bi & w = c + di, a + bi = c + di a = c & b = d The complex cojugate of z = a + bi is z = a bi The sum of complex cojugates is real:

Διαβάστε περισσότερα

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p)

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p) Uppsala Universitet Matematiska Institutionen Andreas Strömbergsson Prov i matematik Funktionalanalys Kurs: F3B, F4Sy, NVP 2005-03-08 Skrivtid: 9 14 Tillåtna hjälpmedel: Manuella skrivdon, Kreyszigs bok

Διαβάστε περισσότερα

Example of the Baum-Welch Algorithm

Example of the Baum-Welch Algorithm Example of the Baum-Welch Algorithm Larry Moss Q520, Spring 2008 1 Our corpus c We start with a very simple corpus. We take the set Y of unanalyzed words to be {ABBA, BAB}, and c to be given by c(abba)

Διαβάστε περισσότερα

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER ORDINAL ARITHMETIC JULIAN J. SCHLÖDER Abstract. We define ordinal arithmetic and show laws of Left- Monotonicity, Associativity, Distributivity, some minor related properties and the Cantor Normal Form.

Διαβάστε περισσότερα

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ting Zhang Stanford May 11, 2001 Stanford, 5/11/2001 1 Outline Ordinal Classification Ordinal Addition Ordinal Multiplication Ordinal

Διαβάστε περισσότερα

Homework 4.1 Solutions Math 5110/6830

Homework 4.1 Solutions Math 5110/6830 Homework 4. Solutios Math 5/683. a) For p + = αp γ α)p γ α)p + γ b) Let Equilibria poits satisfy: p = p = OR = γ α)p ) γ α)p + γ = α γ α)p ) γ α)p + γ α = p ) p + = p ) = The, we have equilibria poits

Διαβάστε περισσότερα

Empirical best prediction under area-level Poisson mixed models

Empirical best prediction under area-level Poisson mixed models Noname manuscript No. (will be inserted by the editor Empirical best prediction under area-level Poisson mixed models Miguel Boubeta María José Lombardía Domingo Morales eceived: date / Accepted: date

Διαβάστε περισσότερα

Srednicki Chapter 55

Srednicki Chapter 55 Srednicki Chapter 55 QFT Problems & Solutions A. George August 3, 03 Srednicki 55.. Use equations 55.3-55.0 and A i, A j ] = Π i, Π j ] = 0 (at equal times) to verify equations 55.-55.3. This is our third

Διαβάστε περισσότερα

F19MC2 Solutions 9 Complex Analysis

F19MC2 Solutions 9 Complex Analysis F9MC Solutions 9 Complex Analysis. (i) Let f(z) = eaz +z. Then f is ifferentiable except at z = ±i an so by Cauchy s Resiue Theorem e az z = πi[res(f,i)+res(f, i)]. +z C(,) Since + has zeros of orer at

Διαβάστε περισσότερα

Probability theory STATISTICAL MODELING OF MULTIVARIATE EXTREMES, FMSN15/MASM23 TABLE OF FORMULÆ. Basic probability theory

Probability theory STATISTICAL MODELING OF MULTIVARIATE EXTREMES, FMSN15/MASM23 TABLE OF FORMULÆ. Basic probability theory Lud Istitute of Techology Cetre for Mathematical Scieces Mathematical Statistics STATISTICAL MODELING OF MULTIVARIATE EXTREMES, FMSN5/MASM3 Probability theory Basic probability theory TABLE OF FORMULÆ

Διαβάστε περισσότερα

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics Fourier Series MATH 211, Calculus II J. Robert Buchanan Department of Mathematics Spring 2018 Introduction Not all functions can be represented by Taylor series. f (k) (c) A Taylor series f (x) = (x c)

Διαβάστε περισσότερα

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X.

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X. Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequalit for metrics: Let (X, d) be a metric space and let x,, z X. Prove that d(x, z) d(, z) d(x, ). (ii): Reverse triangle inequalit for norms:

Διαβάστε περισσότερα

Homework 8 Model Solution Section

Homework 8 Model Solution Section MATH 004 Homework Solution Homework 8 Model Solution Section 14.5 14.6. 14.5. Use the Chain Rule to find dz where z cosx + 4y), x 5t 4, y 1 t. dz dx + dy y sinx + 4y)0t + 4) sinx + 4y) 1t ) 0t + 4t ) sinx

Διαβάστε περισσότερα

On Certain Subclass of λ-bazilevič Functions of Type α + iµ

On Certain Subclass of λ-bazilevič Functions of Type α + iµ Tamsui Oxford Joural of Mathematical Scieces 23(2 (27 141-153 Aletheia Uiversity O Certai Subclass of λ-bailevič Fuctios of Type α + iµ Zhi-Gag Wag, Chu-Yi Gao, ad Shao-Mou Yua College of Mathematics ad

Διαβάστε περισσότερα

Exercises to Statistics of Material Fatigue No. 5

Exercises to Statistics of Material Fatigue No. 5 Prof. Dr. Christine Müller Dipl.-Math. Christoph Kustosz Eercises to Statistics of Material Fatigue No. 5 E. 9 (5 a Show, that a Fisher information matri for a two dimensional parameter θ (θ,θ 2 R 2, can

Διαβάστε περισσότερα

Lecture 2. Soundness and completeness of propositional logic

Lecture 2. Soundness and completeness of propositional logic Lecture 2 Soundness and completeness of propositional logic February 9, 2004 1 Overview Review of natural deduction. Soundness and completeness. Semantics of propositional formulas. Soundness proof. Completeness

Διαβάστε περισσότερα

SUPPLEMENT TO ROBUSTNESS, INFINITESIMAL NEIGHBORHOODS, AND MOMENT RESTRICTIONS (Econometrica, Vol. 81, No. 3, May 2013, )

SUPPLEMENT TO ROBUSTNESS, INFINITESIMAL NEIGHBORHOODS, AND MOMENT RESTRICTIONS (Econometrica, Vol. 81, No. 3, May 2013, ) Ecoometrica Supplemetary Material SUPPLEMENT TO ROBUSTNESS, INFINITESIMAL NEIGHBORHOODS, AND MOMENT RESTRICTIONS (Ecoometrica, Vol. 81, No. 3, May 213, 1185 121) BY YUICHI KITAMURA,TAISUKE OTSU, ANDKIRILL

Διαβάστε περισσότερα

( ) 2 and compare to M.

( ) 2 and compare to M. Problems and Solutions for Section 4.2 4.9 through 4.33) 4.9 Calculate the square root of the matrix 3!0 M!0 8 Hint: Let M / 2 a!b ; calculate M / 2!b c ) 2 and compare to M. Solution: Given: 3!0 M!0 8

Διαβάστε περισσότερα

( y) Partial Differential Equations

( y) Partial Differential Equations Partial Dierential Equations Linear P.D.Es. contains no owers roducts o the deendent variables / an o its derivatives can occasionall be solved. Consider eamle ( ) a (sometimes written as a ) we can integrate

Διαβάστε περισσότερα

On Inclusion Relation of Absolute Summability

On Inclusion Relation of Absolute Summability It. J. Cotemp. Math. Scieces, Vol. 5, 2010, o. 53, 2641-2646 O Iclusio Relatio of Absolute Summability Aradhaa Dutt Jauhari A/66 Suresh Sharma Nagar Bareilly UP) Idia-243006 aditya jauhari@rediffmail.com

Διαβάστε περισσότερα

Μια εισαγωγή στα Μαθηματικά για Οικονομολόγους

Μια εισαγωγή στα Μαθηματικά για Οικονομολόγους Μια εισαγωγή στα Μαθηματικά για Οικονομολόγους Μαθηματικά Ικανές και αναγκαίες συνθήκες Έστω δυο προτάσεις Α και Β «Α είναι αναγκαία συνθήκη για την Β» «Α είναι ικανή συνθήκη για την Β» Α is ecessary for

Διαβάστε περισσότερα

w o = R 1 p. (1) R = p =. = 1

w o = R 1 p. (1) R = p =. = 1 Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-570: Στατιστική Επεξεργασία Σήµατος 205 ιδάσκων : Α. Μουχτάρης Τριτη Σειρά Ασκήσεων Λύσεις Ασκηση 3. 5.2 (a) From the Wiener-Hopf equation we have:

Διαβάστε περισσότερα

Generating Set of the Complete Semigroups of Binary Relations

Generating Set of the Complete Semigroups of Binary Relations Applied Mathematics 06 7 98-07 Published Online January 06 in SciRes http://wwwscirporg/journal/am http://dxdoiorg/036/am067009 Generating Set of the Complete Semigroups of Binary Relations Yasha iasamidze

Διαβάστε περισσότερα

ENGR 691/692 Section 66 (Fall 06): Machine Learning Assigned: August 30 Homework 1: Bayesian Decision Theory (solutions) Due: September 13

ENGR 691/692 Section 66 (Fall 06): Machine Learning Assigned: August 30 Homework 1: Bayesian Decision Theory (solutions) Due: September 13 ENGR 69/69 Section 66 (Fall 06): Machine Learning Assigned: August 30 Homework : Bayesian Decision Theory (solutions) Due: Septemer 3 Prolem : ( pts) Let the conditional densities for a two-category one-dimensional

Διαβάστε περισσότερα

Adaptive Covariance Estimation with model selection

Adaptive Covariance Estimation with model selection Adaptive Covariace Estimatio with model selectio Rolado Biscay, Hélèe Lescorel ad Jea-Michel Loubes arxiv:03007v [mathst Mar 0 Abstract We provide i this paper a fully adaptive pealized procedure to select

Διαβάστε περισσότερα

6.1. Dirac Equation. Hamiltonian. Dirac Eq.

6.1. Dirac Equation. Hamiltonian. Dirac Eq. 6.1. Dirac Equation Ref: M.Kaku, Quantum Field Theory, Oxford Univ Press (1993) η μν = η μν = diag(1, -1, -1, -1) p 0 = p 0 p = p i = -p i p μ p μ = p 0 p 0 + p i p i = E c 2 - p 2 = (m c) 2 H = c p 2

Διαβάστε περισσότερα

ECE Spring Prof. David R. Jackson ECE Dept. Notes 2

ECE Spring Prof. David R. Jackson ECE Dept. Notes 2 ECE 634 Spring 6 Prof. David R. Jackson ECE Dept. Notes Fields in a Source-Free Region Example: Radiation from an aperture y PEC E t x Aperture Assume the following choice of vector potentials: A F = =

Διαβάστε περισσότερα