| Introduction | p. 1 |
| Classifying Individual Samples into One of Two Categories | p. 9 |
| Introduction | p. 9 |
| Presence/Absence Measurements | p. 11 |
| Exhaustive Retesting | p. 12 |
| Sequential Retesting | p. 15 |
| Binary Split Retesting | p. 18 |
| Curtailed Exhaustive Retesting | p. 23 |
| Curtailed Sequential Retesting | p. 27 |
| Curtailed Binary Split Retesting | p. 31 |
| Entropy-Based Retesting | p. 33 |
| Exhaustive Retesting in the Presence of Classification Errors | p. 38 |
| Other Costs | p. 40 |
| Continuous Response Variables | p. 41 |
| Quantitatively Curtailed Exhaustive Retesting | p. 45 |
| Binary Split Retesting | p. 46 |
| Entropy-Based Retesting | p. 49 |
| Cost Analysis of Composite Sampling for Classification | p. 49 |
| Introduction | p. 49 |
| General Cost Expression | p. 49 |
| Effect of False Positives and False Negatives on Composite Sample Classification | p. 50 |
| Presence/Absence Measurements | p. 51 |
| Continuous Measurements | p. 53 |
| Identifying Extremely Large Observations | p. 55 |
| Introduction | p. 55 |
| Prediction of the Sample Maximum | p. 56 |
| The Sweep-Out Method to Identify the Sample Maximum | p. 58 |
| Extensive Search of Extreme Values | p. 59 |
| Application | p. 60 |
| Two-Way Composite Sampling Design | p. 68 |
| Illustrative Example | p. 70 |
| Analysis of Composite Sampling Data Using the Principle of Maximum Entropy | p. 76 |
| Introduction | p. 76 |
| Modeling Composite Sampling Using the Principle of Maximum Entropy | p. 77 |
| When Is the Maximum Entropy Model Reasonable in Practice? | p. 78 |
| Estimating Prevalence of a Trait | p. 81 |
| Introduction | p. 81 |
| The Maximum Likelihood Estimator | p. 82 |
| Alternative Estimators | p. 84 |
| Comparison Between p and p | p. 85 |
| Estimation of Prevalence in Presence of Measurement Error | p. 85 |
| A Bayesian Approach to the Classification Problem | p. 87 |
| Introduction | p. 87 |
| Bayesian Updating of p | p. 90 |
| Minimization of the Expected Relative Cost | p. 93 |
| Discussion | p. 95 |
| Inference on Mean and Variance | p. 97 |
| Introduction | p. 97 |
| Notation and Basic Results | p. 98 |
| Notation | p. 98 |
| Basic Results | p. 99 |
| Estimation Without Measurement Error | p. 101 |
| Estimation in the Presence of Measurement Error | p. 103 |
| Maintaining Precision While Reducing Cost | p. 104 |
| Estimation of x2 and 2 | p. 105 |
| Estimation of Population Variance | p. 106 |
| Confidence Interval for the Population Mean | p. 109 |
| Tests of Hypotheses in the Population Mean | p. 110 |
| One-Sample Tests | p. 110 |
| Two-Sample Tests | p. 111 |
| Applications | p. 112 |
| Comparison of Arithmetic Averages of Soil pH Values with the pH Values of Composite Samples | p. 112 |
| Comparison of Random and Composite Sampling Methods for the Estimation of Fat Contents of Bulk Milk Supplies | p. 112 |
| Optimization of Sampling for the Determination of Mean Radium-226 Concentration in Surface Soil | p. 113 |
| Composite Sampling with Random Weights | p. 115 |
| Introduction | p. 115 |
| Expected Value, Variance, and Covariance of Bilinear Random Forms | p. 116 |
| Models for the Weights | p. 118 |
| Assumptions on the First Two Moments | p. 119 |
| Distributional Assumptions | p. 119 |
| The Model for Composite Sample Measurements | p. 121 |
| Subsampling a Composite Sample | p. 121 |
| Several Composite Samples | p. 124 |
| Subsampling of Several Composite Samples | p. 125 |
| Measurement Error | p. 126 |
| Applications | p. 128 |
| Sampling Frequency and Comparison of Grab and Composite Sampling Programs for Effluents | p. 128 |
| Theoretical Comparison of Grab and Composite Sampling Programs | p. 128 |
| Grab vs. Composite Sampling: A Primer for the Manager and Engineer | p. 129 |
| Composite Samples Overestimate Waste Loads | p. 129 |
| Composite Samples for Foliar Analysis | p. 132 |
| Lateral Variability of Forest Floor Properties Under Second-Growth Douglas-Fir Stands and the Usefulness of Composite Sampling Techniques | p. 133 |
| A Linear Model for Estimation with Composite Sample Data | p. 135 |
| Introduction | p. 135 |
| Motivation for a Unified Model | p. 136 |
| The Model | p. 137 |
| Discussion of the Assumptions | p. 139 |
| The Structural/Sampling Submodel | p. 139 |
| The Compositing/Subsampling Submodel | p. 140 |
| The Structure of the Matrices W, Mw, and w | p. 140 |
| Moments of x and y | p. 146 |
| Complex Sampling Schemes Before Compositing | p. 146 |
| Segmented Populations | p. 147 |
| Estimating the Mean in Segmented Populations | p. 147 |
| Estimating Variance Components in Segmented Populations | p. 150 |
| Estimating the Effect of a Binary Factor | p. 153 |
| Fully Segregated Composites | p. 157 |
| Fully Confounded Composities | p. 161 |
| Elementary Matrices and Kronecker Products | p. 164 |
| Decomposition of Block Matrices | p. 165 |
| Expectation and Dispersion Matrix When Both W and x Are Random | p. 168 |
| The Expectation of W x | p. 168 |
| Variance/Covariance Matrix of W x | p. 172 |
| Composite Sampling for Site Characterization and Cleanup Evaluation | p. 175 |
| Data Quality Objectives | p. 175 |
| Optimal Composite Designs | p. 178 |
| Cost of a Sampling Program | p. 179 |
| Optimal Allocation of Resources | p. 179 |
| Power of a Test and Determination of Sample Size | p. 180 |
| Algorithms for Determination of Sample Size | p. 181 |
| Spatial Structures of Site Characteristics and Composite Sampling | p. 183 |
| Introduction | p. 183 |
| Models for Spatial Processes | p. 183 |
| Composite Sampling | p. 187 |
| Application to Two Superfund Sites | p. 190 |
| The Two Sites | p. 190 |
| Methods | p. 191 |
| Results | p. 192 |
| Discussion | p. 195 |
| Compositing by Spatial Contiguity | p. 198 |
| Introduction | p. 198 |
| Retesting Strategies | p. 199 |
| Composite Sample-Forming Schemes | p. 200 |
| Compositing of Ranked Set Samples | p. 201 |
| Ranked Set Sampling | p. 201 |
| Relative Precision of the RSS Estimator of a Population Mean Relative to Its SRS Estimator | p. 204 |
| Unequal Allocation of Sample Sizes | p. 205 |
| Formation of Homogeneous Composite Samples | p. 206 |
| Composite Sampling of Soils and Sediments | p. 209 |
| Detection of Contamination | p. 209 |
| Detecting PCB Spills | p. 209 |
| Compositing Strategy for Analysis of Samples | p. 211 |
| Estimation of the Average Level of Contamination | p. 213 |
| Estimation of the Average PCB Concentration on the Spill Area | p. 213 |
| Onsite Surface Soil Sampling for PCB at the Armagh Site | p. 214 |
| The Armagh Site | p. 215 |
| Simulating Composite Samples | p. 218 |
| Locating Individual Samples with High PCB Concentrations | p. 221 |
| Estimation of Trace Metal Storage in Lake St. Clair Post-settlement Sediments Using Composite Samples | p. 222 |
| Composite Sampling of Liquids and Fluids | p. 227 |
| Comparison of Random and Composite Sampling Methods for the Estimation of Fat Content of Bulk Milk Supplies | p. 227 |
| Experiment | p. 227 |
| Estimation Methods | p. 228 |
| Results | p. 228 |
| Composite Compared with Yield-Weighted Estimate of Fat Percentage | p. 229 |
| Composite Sampling of Highway Runoff | p. 229 |
| Composite Samples Overestimate Waste Loads | p. 232 |
| Composite Sampling and Indoor Air Pollution | p. 235 |
| Household Dust Samples | p. 235 |
| Composite Sampling and Bioaccumulation | p. 239 |
| Example: National Human Adipose Tissue Survey | p. 241 |
| Results from the Analysis of 1987 NHATS Data | p. 241 |
| Glossary and Terminology | p. 243 |
| Bibliography | p. 249 |
| Index | p. 267 |
| Table of Contents provided by Ingram. All Rights Reserved. |
