Small Area Prediction under Alternative Model Specifications By Wayne A. Fuller and Andreea L. Erciulescu Department of Statistics, Iowa State University.

Slides:

Advertisements

Similar presentations

Review bootstrap and permutation

Advertisements

Uncertainty and confidence intervals Statistical estimation methods, Finse Friday , 12.45–14.05 Andreas Lindén.

Sampling: Final and Initial Sample Size Determination

Psychology 202b Advanced Psychological Statistics, II February 8, 2011.

Psychology 202b Advanced Psychological Statistics, II February 10, 2011.

Jump to first page STATISTICAL INFERENCE Statistical Inference uses sample data and statistical procedures to: n Estimate population parameters; or n Test.

Semiparametric Mixed Models in Small Area Estimation Mark Delorey F. Jay Breidt Colorado State University September 22, 2002.

1 Estimation of GDP in Turkey by nonparametric regression models DURSUN AYDIN ANADOLU UNIVERSITY TURKEY.

BCOR 1020 Business Statistics Lecture 28 – May 1, 2008.

Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.

Statistical Concepts (continued) Concepts to cover or review today: –Population parameter –Sample statistics –Mean –Standard deviation –Coefficient of.

IEEM 3201 Two-Sample Estimation: Paired Observation, Difference.

Overview of STAT 270 Ch 1-9 of Devore + Various Applications.

Nonparametric, Model-Assisted Estimation for a Two-Stage Sampling Design Mark Delorey Joint work with F. Jay Breidt and Jean Opsomer September 8, 2005.

July 3, A36 Theory of Statistics Course within the Master’s program in Statistics and Data mining Fall semester 2011.

STAT 4060 Design and Analysis of Surveys Exam: 60% Mid Test: 20% Mini Project: 10% Continuous assessment: 10%

1 Inference About a Population Variance Sometimes we are interested in making inference about the variability of processes. Examples: –Investors use variance.

Modelling health care costs: practical examples and applications Andrew Briggs Philip Clarke University of Oxford & Daniel Polsky Henry Glick University.

Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.

1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.

Increasing Survey Statistics Precision Using Split Questionnaire Design: An Application of Small Area Estimation 1.

Chapter 7 Estimation: Single Population

2015 AprilUNIVERSITY OF HAIFA, DEPARTMENT OF STATISTICS, SEMINAR FOR M.A 1 Hastie, Tibshirani and Friedman.The Elements of Statistical Learning (2nd edition,

University of Ottawa - Bio 4118 – Applied Biostatistics © Antoine Morin and Scott Findlay 08/10/ :23 PM 1 Some basic statistical concepts, statistics.

Chapter 13 – Difference Between Two Parameters Math 22 Introductory Statistics.

1 Ratio estimation under SRS Assume Absence of nonsampling error SRS of size n from a pop of size N Ratio estimation is alternative to under SRS, uses.

Estimation (Point Estimation)

Dept of Bioenvironmental Systems Engineering National Taiwan University Lab for Remote Sensing Hydrology and Spatial Modeling STATISTICS Interval Estimation.

Limits to Statistical Theory Bootstrap analysis ESM April 2006.

Bootstraps and Jackknives Hal Whitehead BIOL4062/5062.

Properties of Estimators Statistics: 1.Sufficiency 2.Un-biased 3.Resistance 4.Efficiency Parameters:Describe the population Describe samples. But we use.

Mystery 1Mystery 2Mystery 3.

Introducing Communication Research 2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics.

Math 4030 – 9b Comparing Two Means 1 Dependent and independent samples Comparing two means.

AP Statistics Section 10.3: Estimating a Population Proportion.

Case Selection and Resampling Lucila Ohno-Machado HST951.

New Information Technologies in Learning Statistics M. Mihova, Ž. Popeska Institute of Informatics Faculty of Natural Sciences and Mathematics, Macedonia.

Statistical Data Analysis 2011/2012 M. de Gunst Lecture 6.

Statistical Data Analysis 2011/2012 M. de Gunst Lecture 4.

Lecture 4 Confidence Intervals. Lecture Summary Last lecture, we talked about summary statistics and how “good” they were in estimating the parameters.

Parameter Estimation. Statistics Probability specified inferred Steam engine pump “prediction” “estimation”

11.1 Heteroskedasticity: Nature and Detection Aims and Learning Objectives By the end of this session students should be able to: Explain the nature.

Quantifying Uncertainty

Bootstrapping James G. Anderson, Ph.D. Purdue University.

Small area estimation combining information from several sources Jae-Kwang Kim, Iowa State University Seo-Young Kim, Statistical Research Institute July.

Chapter 22 Inferential Data Analysis: Part 2 PowerPoint presentation developed by: Jennifer L. Bellamy & Sarah E. Bledsoe.

Chapter 3 INTERVAL ESTIMATES

Statistics in SPSS Lecture 5

STATISTICAL INFERENCE

Chapter 4. Inference about Process Quality

Chapter 6 Confidence Intervals.

Estimation Procedures

Statistics in SPSS Lecture 7

الأستاذ المساعد بقسم المناهج وطرق التدريس

Quantifying uncertainty using the bootstrap

Bootstrap Confidence Intervals using Percentiles

STAT 5372: Experimental Statistics

Putting It All Together: Which Method Do I Use?

BOOTSTRAPPING: LEARNING FROM THE SAMPLE

Chapter 6 Confidence Intervals.

Lecture 7 Nonparametric Regression: Nadaraya Watson Estimator

Ch13 Empirical Methods.

From Samples to Populations

Determining Which Method to use

Statistical Inference

Chapter 6 Confidence Intervals.

Sampling Distributions

Implementation of the Bayesian approach to imputation at SORS Zvone Klun and Rudi Seljak Statistical Office of the Republic of Slovenia Oslo, September.

Fractional-Random-Weight Bootstrap

Presentation transcript:

Small Area Prediction under Alternative Model Specifications By Wayne A. Fuller and Andreea L. Erciulescu Department of Statistics, Iowa State University Small Area Estimation 2014 Poznan, Poland, September, 2014

Outline I.Motivating example II.Models: auxiliary information III.Bootstrap for prediction MSE IV.Simulation 2

Conservation Effects Assessment Project (CEAP): Natural Resources Conservation Service Impacts of conservation practices Sample of fields Subsample: National Resources Inventory(NRI) Hydrologic Units 3

4

Unit Level Model 5

Auxiliary Data 6

Parameters 7

Parametric Bootstrap 8

Double Bootstrap Estimation 9

Fast Double Bootstrap 10

Telescoping Double Bootstrap 11

CEAP Simulation Model 12

Alternative Specifications for x Some external information Area means known Estimated random means No external information Area means fixed Area means random 13

Simulation Parameters 14

Estimation and Prediction 15

16 Size

17 2Rel Bias Rel Sd Rel Bias Rel Sd Rel Bias Rel Sd

Equal Efficiency Bootstrap Samples 18 BootstrapLevel OneTotal Telescoping (100, 1) Classic (100, 1) Classic (44, 50)

Summary Fast double bootstrap improves bootstrap efficiency Double bootstrap reduces bias (about 50%) Double bootstrap increases variance (15 to 30 %) Random x model has potential to reduce MSE 19

Future Work Confidence Intervals Triple Bootstrap Regression with Bootstrap Nonparametric Bootstrap Predictions for CEAP 20

Thank You 21