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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.
Language
Place of publication
Publishing group
Elsevier Science & Techn.
ISBN-13
978-1-4832-6088-4 (9781483260884)
Schweitzer Classification
List of ContributorsPrefaceGreetings to HOH for 1977Published Works of H. O. HartleyPart 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 References 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 References 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 Reference 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 References 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 References 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 YR in SRS 4. Robust Estimation of a Total References 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 References Sampling in Two or More Dimensions 1. Introduction 2. General Consideration 3. Specific Examples of Sampling Procedures ReferencesPart 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 References 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 References 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 ¿ and ¿ 5. Test of Goodness of Fit of a Proposed ¿ When ¿ Is Known References 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 References Multinomial Selection Index 1. Introduction 2. Estimation Procedure 3.