Hartley / David Contributions to Survey Sampling and Applied Statistics

Papers in Honor of H.O Hartley
1. Auflage 2014
ISBN: 978-1-4832-6088-4
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 346 Seiten, Web PDF

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.

Autoren/Hrsg.

Hartley, H. O.

David, H. A.

Inhaltsverzeichnis

1;Front Cover;1
2;Contributions to Survey Sampling and Applied Statistics: Papers in Honor of H. O. Hartley;4
3;Copyright Page;5
4;Table of Contents;6
5;List of Contributors;12
6;Preface;14
7;Greetings to HOH for 1977;16
8;Published Works of H. O. Hartley;22
9;Part I: SAMPLING;30
9.1;Chapter 1. Laplace's Ratio Estimator;32
9.1.1;1. Introduction;32
9.1.2;2. The Survey and the Estimate;33
9.1.3;3. The Sampling Error: Standard Methods;35
9.1.4;4. Laplace's Analysis of the Sampling Error;36
9.1.5;References;39
9.2;Chapter 2. Some Aspects of Statistics, Sampling, and Randomization;40
9.2.1;1. Introduction;40
9.2.2;2. The General Nature of Conventional Mathematical Statistics;41
9.2.3;3. What Is Inference?;43
9.2.4;4. The Finite Population Problem;47
9.2.5;5. The Labeled Case;50
9.2.6;6. The Matter of Labeling;51
9.2.7;7. Admissibility;54
9.2.8;8. Pivotality;55
9.2.9;9. Priors;55
9.2.10;10. Conclusion;56
9.2.11;References;57
9.3;Chapter 3. Predictive Estimation and Internal Congruency;58
9.3.1;1. Introduction;58
9.3.2;2. Predictive Estimators;59
9.3.3;3. Model-Free Prediction;60
9.3.4;4. An Internally Congruent Ratio-Type Estimator;62
9.3.5;5. Some Sampling Investigations;66
9.3.6;6. Conclusions;68
9.3.7;Reference;68
9.4;Chapter 4. Survey Statistics in Social Program Evaluation;70
9.4.1;1. Introduction;70
9.4.2;2. The Survey Role in Evaluation;71
9.4.3;3. The Evaluation Setting;72
9.4.4;4. The Use of Comparison Groups;77
9.4.5;5. Matching;78
9.4.6;6. Classification versus Regression;79
9.4.7;7. Variable Sampling Weights;83
9.4.8;8. Summary;84
9.4.9;References;84
9.5;Chapter 5. Variance Estimation for a Specified Multiple Frame Survey Design;86
9.5.1;1. Background;86
9.5.2;2. Estimation from Survey Data;88
9.5.3;3. Variance Estimates under Some Simplifying Assumptions;89
9.5.4;4. Generalized Estimates of Variance—To Provide Rough but Simply Computed Approximations;91
9.5.5;5. Evaluation of the above Approximations Based on More Exact Variance Estimates;93
9.5.6;6. Composite Estimators;95
9.5.7;References;96
9.6;Chapter 6. Sampling Designs Involving Unequal Probabilities of Selection and Robust Estimation of a Finite Population Total;98
9.6.1;1. Introduction;98
9.6.2;2. Unequal Probability Sampling without Replacement;99
9.6.3;3. Variance Estimators for YR in SRS;106
9.6.4;4. Robust Estimation of a Total;108
9.6.5;References;113
9.7;Chapter 7. Selection Biases in Fixed Panel Surveys;118
9.7.1;1. Introduction;118
9.7.2;2. A Simple Two Category Model Repeated at Two Observation Times;121
9.7.3;3. Sampling at Three Observation Times;137
9.7.4;4. Summary Discussion;140
9.7.5;References;141
9.8;Chapter 8. Sampling in Two or More Dimensions;142
9.8.1;1. Introduction;142
9.8.2;2. General Consideration;144
9.8.3;3. Specific Examples of Sampling Procedures;148
9.8.4;References;158
10;Part II: THE LINEAR MODEL;160
10.1;Chapter 9. The Analysis of Linear Models with Unbalanced Data;162
10.1.1;1. Introduction;162
10.1.2;2. Computational Procedures;165
10.1.3;3. Two-Way Classification with Interaction;167
10.1.4;4. Two-Way Classification without Interaction;177
10.1.5;5. Two-Fold Nested Model;178
10.1.6;6. Summary;179
10.1.7;References;179
10.2;Chapter 10. Nonhomogeneous Variances in the Mixed AOV Model; Maximum Likelihood Estimation;182
10.2.1;1. Introduction;182
10.2.2;2. The Mixed AOV Model with Unequal Error Variances;183
10.2.3;3. Constraining the Estimators;186
10.2.4;4. The General Algorithm—An Example;187
10.2.5;5. The Case of Proportional Variances;190
10.2.6;6. Measuring Instrument Models;192
10.2.7;7. The lt Algorithm for Balanced Data;194
10.2.8;8. The Missing Data Algorithm;197
10.2.9;References;200
10.3;Chapter 11. Concurrency of Regression Equations with k Regressors;202
10.3.1;1. Introduction;202
10.3.2;2. Goodness of Fit of a Hypothetical Point of Concurrence;203
10.3.3;3. Test Statistic T02, T12, T22;207
10.3.4;4. Estimation of £ and n;207
10.3.5;5. Test of Goodness of Fit of a Proposed . When n Is Known;208
10.3.6;References;209
10.4;Chapter 12. A Univariate Formulation of the Multivariate Linear Model;210
10.4.1;1. The Vec Operator and Some Associated Results;210
10.4.2;2. The Model;212
10.4.3;3. Estimation;212
10.4.4;4. Independence under Normality;213
10.4.5;5. Hypothesis Testing;214
10.4.6;6. Jacobians;216
10.4.7;References;218
10.5;Chapter 13. Multinomial Selection Index;220
10.5.1;1. Introduction;220
10.5.2;2. Estimation Procedure;222
10.5.3;3. Simulation Studies;225
10.5.4;4. Conclusions;228
10.5.5;References;229
11;Part III: TIMES ERIES;230
11.1;Chapter 14. Applications of Time Series Analysis;232
11.1.1;1. Introduction;232
11.1.2;2. Serial and Nonserial Models;233
11.1.3;3. A Canonical Analysis Useful for Detecting Contemporaneous and Other Relationships;235
11.1.4;4. Intervention Analysis for Detecting and Estimating Changes in Time Series;242
11.1.5;References;247
12;Part IV: OUTLIERS, ROBUSTNESS, AND CENSORING;250
12.1;Chapter 15. Testing for Outliers in Linear Regression;252
12.1.1;1. Introduction;252
12.1.2;2. On the Distribution of Rn;253
12.1.3;3. Equivalent Criteria for Single Outliers;254
12.1.4;4. Performance of Procedure for Identifying Single Outlier;255
12.1.5;5. Multiple Outlier Procedures;258
12.1.6;6. Example;259
12.1.7;7. Further Comments;261
12.1.8;References;261
12.2;Chapter 16. Robustness of Location Estimators in the Presence of an Outlier;264
12.2.1;1. Introduction and Summary;264
12.2.2;2. Basic Theory;266
12.2.3;3. Outlying Population Differing in Location;268
12.2.4;4. Outlying Population Differing in Scale;270
12.2.5;5. Numerical Results in the Normal Cases;271
12.2.6;6. Concluding Remarks;276
12.2.7;Appendix;277
12.2.8;References;278
12.3;Chapter 17. The Ninther, a Technique for Low-Effort Robust (Resistant) Location in Large Samples;280
12.3.1;1. Introduction;280
12.3.2;2. The Ninther;281
12.3.3;3. Distribution of Ninthers;281
12.3.4;4. Computing Effort;282
12.3.5;5. The Ninther-Median Combination;283
12.3.6;6. Impractically Large Data Sets;283
12.3.7;7. The Ninther-Mean Combination;284
12.3.8;8. A Comment;284
12.3.9;9. A Permutation Result;284
12.3.10;10. A Sampling Result;285
12.3.11;References;286
12.4;Chapter 18. Completeness Comparisons among Sequences of Samples;288
12.4.1;1. Introduction;288
12.4.2;2. "Parametric" Test Procedures;289
12.4.3;3. Distribution-Free Tests;296
12.4.4;4. Some Other Problems;302
12.4.5;References;304
13;Part V: MATHEMATICAL PROGRAMMING AND COMPUTING;306
13.1;Chapter 19. Absolute Deviations Curve Fitting: 0An Alternative to Least Squares;308
13.1.1;1. Introduction;308
13.1.2;2. M A . D . Estimation and Geometric Programming;309
13.1.3;3. Properties of lt Norm Estimators in Regression for Small Samples;315
13.1.4;4. Summary;317
13.1.5;Appendix;318
13.1.6;References;322
13.2;Chapter 20. Tables of the Normal Conditioned on t-Distribution;324
13.2.1;1. Introduction;324
13.2.2;2. Model Development;325
13.2.3;3. Mathematical Development;326
13.2.4;References;328
13.3;Table 1;329