Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Similar documents
Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Notation for Mixed Models for Finite Populations

Unbalanced Panel Data Models

Derivation of a Predictor of Combination #1 and the MSE for a Predictor of a Position in Two Stage Sampling with Response Error.

ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION

3.4 Properties of the Stress Tensor

Introduction to logistic regression

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Introduction. Generation of the Population Data

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Nuclear Chemistry -- ANSWERS

Estimating A Realized Random Effect in Mixed Models Using Collapsed SSUs. Edward J. Stanek III. c01ed36.doc 9/19/01 6:04 AM

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

Chapter 6. pn-junction diode: I-V characteristics

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Correlation in tree The (ferromagnetic) Ising model

ANOVA- Analyisis of Variance

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

Reliability of time dependent stress-strength system for various distributions

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

Lecture 5. Estimation of Variance Components

Power Spectrum Estimation of Stochastic Stationary Signals

ME 501A Seminar in Engineering Analysis Page 1

Linear Algebra. Definition The inverse of an n by n matrix A is an n by n matrix B where, Properties of Matrix Inverse. Minors and cofactors

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Petru P. Blaga-Reducing of variance by a combined scheme based on Bernstein polynomials

Note on the Computation of Sample Size for Ratio Sampling

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Numerical Method: Finite difference scheme

Simple Linear Regression Analysis

(( ) ( ) ( ) ( ) ( 1 2 ( ) ( ) ( ) ( ) Two Stage Cluster Sampling and Random Effects Ed Stanek

ε. Therefore, the estimate

Lecture 1: Empirical economic relations

Noise in electronic components.

HANDY REFERENCE SHEET HRP/STATS 261, Discrete Data

ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION

Note: Torque is prop. to current Stationary voltage is prop. to speed

APPLICATION OF THE DISTRIBUTED TRANSFER FUNCTION METHOD AND THE RIGID FINITE ELEMENT METHOD FOR MODELLING OF 2-D AND 3-D SYSTEMS

Outlier-tolerant parameter estimation

Chemistry 222. Exam 1: Chapters 1-4

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14)

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

On the energy of complement of regular line graphs

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then

Independent Domination in Line Graphs

ENGR 7181 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT

Calculus Revision A2 Level

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

A Note on Estimability in Linear Models

Chapter 3 Sampling For Proportions and Percentages

ME 200 Thermodynamics I Spring 2014 Examination 3 Thu 4/10/14 6:30 7:30 PM WTHR 200, CL50 224, PHY 112 LAST NAME FIRST NAME

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

A Probabilistic Characterization of Simulation Model Uncertainties

Homework 1: Solutions

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION

Calculation of electromotive force induced by the slot harmonics and parameters of the linear generator

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

Outline. Types of Experimental Designs. Terminology. EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Digital Signal Processing, Fall 2006

STK4011 and STK9011 Autumn 2016

Standard Deviation for PDG Mass Data

ONLY AVAILABLE IN ELECTRONIC FORM

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)

A study of stochastic programming having some continuous random variables

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines

Consider serial transmission. In Proakis notation, we receive

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS

Aotomorphic Functions And Fermat s Last Theorem(4)

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

Linear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption?

8 The independence problem

Simulation Output Analysis

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

UNIT 8 TWO-WAY ANOVA WITH m OBSERVATIONS PER CELL

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

CHAPTER 6 CONVENTIONAL SINGLE-PHASE TO THREE-PHASE POWER CONVERTERS. 6.1 Introduction

Ordinary Least Squares at advanced level

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

EAcos θ, where θ is the angle between the electric field and

SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL

Equil. Properties of Reacting Gas Mixtures. So far have looked at Statistical Mechanics results for a single (pure) perfect gas

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR

Introduction to logistic regression

ELG3150 Assignment 3

4.8 Huffman Codes. Wordle. Encoding Text. Encoding Text. Prefix Codes. Encoding Text

Handout #1. Title: Foundations of Econometrics. POPULATION vs. SAMPLE

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Transcription:

Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd clutr a W df th varac of th ut paratr a M ( µ t µ ug th gratd ut paratr Notc that w oly grat t M o t of ut ffct for th populato Th ut ffct arud to crat paratr for clutr-ut for ach clutr W grat th ut ffct th ar to forc th varac of ut wth clutr to b cotat for all clutr Th coo wth clutr varac whch w rprt by qual to th avrag wth clutr varac, N Prdctor ar cotructd for ach tral To dvlop a prdctor, w u N quatt calculatd for ach lcto of a clutr (PSU, ad avrag of th quatt ovr lctd clutr (PSU th apl (tral Th prdctor obtad by cobg th rult W dcrb th proc bad o dvlopt of th xd odl prdctor (gv by quato (4 Stak ad Sgr(003 Frt, w valuat N M ( u + ut t Th corrpod to th ralzd varac for th t th lctd clutr Th, for ach lctd clutr, w valuat th quatt Y, ad Y v + W alo for th quatt v ad v Th xt tp to u th quatt ovr th lctd clutr th tral rultg Y v ad / v, fro whch w dtr ˆ µ wy whr w, v / v quvalt to ˆ µ Y v Fally, cobg th rult back to th apl, w v obta th xd odl prdctor gv by pˆ ˆ µ k ( ˆ Y µ Varac Matrx for Sapl + C03d3doc 5//003 0:00 AM

Th varac atrx for a apl gv by VI ( + I + ( I J J W tat ach tr th xpro N ug a ultaou quato Th quato bad o ttg th tatd varac (( atrx gv by ( Yk Y( Y Y k qual to th thr varac copot by ply avragg th varou porto of th varac atrx J Not that ( I P YI (( Yk Y ad that P I (( Y Y Y A a rult, var ( I P YI ( + ( I P, whl J J var P I ( + + ( Y P P J Hc, E ( Yk Y ( ( +, whl E ( Y Y ( ( + + Ug th xpro, ( Yk Y ( Y Y E ( +, whl E ( ( ( + + ( Yk Y Mthod of ot tat ar gv by ˆ ( ( ˆ +, ad ( ( ˆ ( ( Y Y Yk Y Fally, w valuat ( Yk Y( Y Y k ( Yk Y Y I, for ad Sc var ( Yk Y( Y Y var ( I k Y, whch plf to C03d3doc 5//003 0:00 AM

( Yk Y( Y Y var ( I var k Y ( + + ( + I J N J Now, ( I J, ad 0 J Thu, var ( Yk Y( Y Y ( + + k W ca u thod of ot tat to tat ( Yk Y ˆ ( ( ˆ + To parat tat for th varac copot, w d a xtral dpdt tat of ˆ Wh thr o rpo rror, th th tator ˆ plf to a tat of Sc, a tat of gv by olvg th quato M ( Y Y ( Yk Y ˆ ˆ, or ( ( M ( Y Y ( Yk Y ˆ + ˆ If th xpro l tha ( ( M zro, w tat by zro C03d3doc 5//003 0:00 AM 3

To uarz, w tat by ( Y Y ( Yk Y ˆ ˆ ax +,0, ad ( ( M vˆ ˆ ( ˆ + ( Yk Y xtral ourc, th ˆ ˆ ˆ ˆ M ( Aug w hav a tat of fro o ( Yk Y ˆ ( Suary of Varac Copot Etat Lt u df MSB ( Y Y (, ad w tat by ad MSE ( Yk Y ( Alo, lt u au w hav a dpdt tat of whch w rprt by vbb _ W tat ( by ˆ v ˆ ( MSE _ ax,0 W tat by ˆ v _ MSE W tat by v _ u ˆ ax MSB MSE +,0 M Fally, w tat by vtar _ ˆ ax ( MSB MSE,0 W uarz th tat th followg tabl ˆ C03d3doc 5//003 0:00 AM 4

Varabl SAS Na Dcrpto ( Y Y ( b Sapl a quar btw clutr ( Yk Y ( Sapl a quard rror wth clutr ˆ vbb_ Etatd rpo rror varac (( MSE ˆ ˆ ax,0 v_ Etatd avrag varac of ut for a clutr ˆ MSE v_ Etatd varac of lctd clutr ad rpo rror ˆ ˆ ax MSB MSE +,0 M v_u Etatd Var btw clutr ˆ ax ( MSB MSE,0 vtar_ Etatd ad varac of lctd clutr for RP odl Ug th tr, w tat th hrkag cotat uch that Varabl SAS Na Dcrpto ˆ ˆ k ˆ + ˆ k_ Etatd Mxd Modl hrkag cotat ˆ ˆ k ˆ + kˆ ˆ ˆ ˆ + + ˆ ( ˆ k_ Etatd RP Modl hrkag cotat ktar_ Etatd RP Modl hrkag cotat wth doator rpo rror C03d3doc 5//003 0:00 AM 5

kˆ r ˆ + ˆ + + ˆ ( ˆ ˆ ( µ ˆ ( µ krtar_ p ˆ ˆ + k Y ˆ p6 Et MM ( ( ( µ ˆ µ ( ( ( ˆ ( ˆ ( f ( Y kˆ Y Y RP Modl hrkag cotat wth urator ut varac ad doator rpo rror Pˆ fy + f ˆ + k Y ˆ p7 Et Scott&Sth Tˆ fy + f Y + k Y Y p8 Et RP Tˆ f Y + k Y Y r ( ( + + p9 Et RP + Rp Err C03d3doc 5//003 0:00 AM 6