Contributions to Survey Sampling and Applied Statistics - 1st Edition - ISBN: 9780122047503, 9781483260884

Contributions to Survey Sampling and Applied Statistics

1st Edition

Papers in Honor of H.O Hartley

Editors: H. A. David
Authors: H. O. Hartley
eBook ISBN: 9781483260884
Imprint: Academic Press
Published Date: 1st January 1978
Page Count: 346
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Contributions to Survey Sampling and Applied Statistics: Papers in Honor of H. O. Hartley covers the significant advances in survey sampling, modeling, and applied statistics. This book is organized into five parts encompassing 20 chapters.

The opening part looks into some aspects of statistics, sampling, randomization, predictive estimation, and internal congruency. This part also considers the properties of variance estimation for a specified multiple frame survey design and some sampling designs involving unequal probabilities of selection and robust estimation of a finite population total. The next parts present the analysis and the theoretical and practical aspects of linear models, as well as the applications of time series analysis. These topics are followed by discussions of the testing for outliers in linear regression; the robustness of location estimators; and completeness comparisons among sample sequences. The closing part deals with the properties of norm estimators in regression and geometric programming. This part also provides tables of the normal conditioned on t-distribution.

This book will prove useful to mathematicians and statisticians.

Table of Contents

List of Contributors


Greetings to HOH for 1977

Published Works of H. O. Hartley

Part I Sampling

Laplace's Ratio Estimator

1. Introduction

2. The Survey and the Estimate

3. The Sampling Error: Standard Methods

4. Laplace's Analysis of the Sampling Error


Some Aspects of Statistics, Sampling, and Randomization

1. Introduction

2. The General Nature of Conventional Mathematical Statistics

3. What Is Inference?

4. The Finite Population Problem

5. The Labeled Case

6. The Matter of Labeling

7. Admissibility

8. Pivotality

9. Priors

10. Conclusion


Predictive Estimation and Internal Congruency

1. Introduction

2. Predictive Estimators

3. Model-Free Prediction

4. An Internally Congruent Ratio-Type Estimator

5. Some Sampling Investigations

6. Conclusions


Survey Statistics in Social Program Evaluation

1. Introduction

2. The Survey Role in Evaluation

3. The Evaluation Setting

4. The Use of Comparison Groups

5. Matching

6. Classification Versus Regression

7. Variable Sampling Weights

8. Summary


Variance Estimation for a Specified Multiple Frame Survey Design

1. Background

2. Estimation from Survey Data

3. Variance Estimates under Some Simplifying Assumptions

4. Generalized Estimates of Variance—To Provide Rough but Simply Computed Approximations

5. Evaluation of the Above Approximations Based on More Exact Variance Estimates

6. Composite Estimators


Sampling Designs Involving Unequal Probabilities of Selection and Robust Estimation of a Finite Population Total

1. Introduction

2. Unequal Probability Sampling without Replacement

3. Variance Estimators for ŶR in SRS

4. Robust Estimation of a Total


Selection Biases in Fixed Panel Surveys

1. Introduction

2. A Simple Two Category Model Repeated at Two Observation Times

3. Sampling at Three Observation Times

4. Summary Discussion


Sampling in Two or More Dimensions

1. Introduction

2. General Consideration

3. Specific Examples of Sampling Procedures


Part II The Linear Model

The Analysis of Linear Models with Unbalanced Data

1. Introduction

2. Computational Procedures

3. Two-Way Classification with Interaction

4. Two-Way Classification without Interaction

5. Two-Fold Nested Model

6. Summary


Nonhomogeneous Variances in the Mixed AOV Model; Maximum Likelihood Estimation

1. Introduction

2. The Mixed AOV Model with Unequal Error Variances

3. Constraining the Estimators

4. The General Algorithm—An Example

5. The Case of Proportional Variances

6. Measuring Instrument Models

7. The lt Algorithm for Balanced Data

8. The Missing Data Algorithm


Concurrency of Regression Equations with k Regressors

1. Introduction

2. Goodness of Fit of a Hypothetical Point of Concurrence

3. Test Statistics T02, T12, T22

4. Estimation of ξ and η

5. Test of Goodness of Fit of a Proposed ξ When η Is Known


A Univariate Formulation of the Multivariate Linear Model

1. The Vec Operator and Some Associated Results

2. The Model

3. Estimation

4. Independence under Normality

5. Hypothesis Testing

6. Jacobians


Multinomial Selection Index

1. Introduction

2. Estimation Procedure

3. Simulation Studies

4. Conclusions


Part III Time Series

Applications of Time Series Analysis

1. Introduction

2. Serial and Nonserial Models

3. A Canonical Analysis Useful for Detecting Contemporaneous and Other Relationships

4. Intervention Analysis for Detecting and Estimating Changes in Time Series


Part IV Outliers, Robustness, and Censoring

Testing for Outliers in Linear Regression

1. Introduction

2. On the Distribution of Rn

3. Equivalent Criteria for Single Outliers

4. Performance of Procedure for Identifying Single Outlier

5. Multiple Outlier Procedures

6. Example

7. Further Comments


Robustness of Location Estimators in the Presence of an Outlier

1. Introduction and Summary

2. Basic Theory

3. Outlying Population Differing in Location

4. Outlying Population Differing in Scale

5. Numerical Results in the Normal Cases

6. Concluding Remarks



The Ninther, a Technique for Low-Effort Robust (Resistant) Location in Large Samples

1. Introduction

2. The Ninther

3. Distribution of Ninthers

4. Computing Effort

5. The Ninther-Median Combination

6. Impractically Large Data Sets

7. The Ninther-Mean Combination

8. A Comment

9. A Permutation Result

10. A Sampling Result


Completeness Comparisons among Sequences of Samples

1. Introduction

2. "Parametric" Test Procedures

3. Distribution-Free Tests

4. Some Other Problems


Part V Mathematical Programming and Computing

Absolute Deviations Curve Fitting: An Alternative to Least Squares

1. Introduction

2. M.A.D. Estimation and Geometric Programming

3. Properties of l1 Norm Estimators in Regression for Small Samples

4. Summary



Tables of the Normal Conditioned on t-Distribution

1. Introduction

2. Model Development

3. Mathematical Development


Table 1


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© Academic Press 1978
Academic Press
eBook ISBN:

About the Editor

H. A. David

About the Author

H. O. Hartley

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