Theory of sample surveys:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London u.a.
Chapman & Hall
1997
|
| Edition: | 1. ed. |
| Series: | Monographs on statistics and applied probability
74 |
| Subjects: | |
| Online Access: | Publisher descriptionPublisher description Inhaltsverzeichnis |
| Physical Description: | XI, 305 S. graph. Darst. 23 cm |
| ISBN: | 041231780X |
Staff View
MARC
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| 100 | 1 | |a Thompson, Mary E. |d 1944- |e Verfasser |0 (DE-588)136660819 |4 aut | |
| 245 | 1 | 0 | |a Theory of sample surveys |c M. E. Thompson |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a London u.a. |b Chapman & Hall |c 1997 | |
| 300 | |a XI, 305 S. |b graph. Darst. |c 23 cm | ||
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| 650 | 4 | |a Échantillonnage (Statistique) | |
| 650 | 4 | |a Statistik | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
PREFACE xiii
1 Introduction 1
1.1 Survey populations and samples 3
1.2 Population quantities 5
1.3 Survey error 6
1.4 Sampling and non sampling errors 7
1.5 Bias and variability 7
1.6 Focus on sampling error 8
2 The mathematics of probability sampling designs 9
2.1 Randomized sampling designs 9
2.2 Expectations and variances of sample sums; the HT
estimator 12
2.3 Linear estimators for population totals 19
2.4 Sampling strategies and local relative efficiency 21
2.5 Finite population cumulants 25
2.5.1 Cumulants and K statistics 25
2.5.2 Cumulants of the sample sum in SRS 29
2.5.3 Variance of the sample variance in SRS 31
2.6 Multi stage sampling 31
2.7 Estimation in multi stage sampling 33
2.8 Implementation of unequal probability designs 37
Exercises 43
3 Distributions induced by random sampling designs 49
3.1 Distribution of sample numbers and proportions in SRS 50
3.2 Confidence limits for population numbers and propor¬
tions in SRS 52
3.3 The characteristic function of the sample sum in SRS 56
3.4 The finite population central limit theorem for SRS 58
viii CONTENTS
3.4.1 Distribution of univariate sample sum 58
3.4.2 Distribution of multivariate sample sum 60
3.5 Asymptotic normality and applications 61
3.5.1 Conditions for asymptotic normality 61
3.5.2 Normal based confidence intervals for totals 64
3.5.3 Sample size determination 64
3.6 Formal Edgeworth expansions 65
3.7 Edgeworth expansions for the distribution of the sample
sum in SRS 68
3.8 Edgeworth expansions for the distribution of the
studentized mean in SRS 71
3.9 Saddlepoint approximations 73
3.10 Saddlepoint approximations for SRS 75
3.10.1 Approximations to the distribution of the sample
sum 75
3.10.2 Approximations to tail probabilities 78
3.11 Use of saddlepoint in constructing confidence intervals
in SRS 80
3.11.1 Construction of the test array 81
3.11.2 Approximation of the tail probabilities 82
3.12 Monetary unit sampling 83
3.13 Bootstrap resampling methods for confidence interval
construction 85
3.13.1 The idea of the bootstrap 86
3.13.2 Bootstrap t confidence intervals 91
4 Design based estimation for general finite population
quantities 93
4.1 Quantities defined as roots of simple estimating
functions 94
4.1.1 Design frequency properties of the estimating
functions 96
4.1.2 Particular cases 98
4.1.3 Intervals based on refined approximations 102
4.1.4 Asymptotic properties of the point estimates 104
4.2 Quantities defined as functions of population totals 106
4.2.1 Linearization of the error 107
4.2.2 Linearization confidence intervals 108
4.2.3 Method of random groups 11 *
4.2.4 Balanced repeated replication I14
CONTENTS ix
4.2.5 Jackknife methods for variance estimation 121
4.2.6 Bootstrap variance estimation 125
4.2.7 Properties of the methods of variance estimation 126
4.2.8 Bias reduction from resampling methods 130
4.2.9 Bootstrap t confidence intervals 132
4.3 Quantities which are functions of population [/ statistics 132
4.4 Quantities defined as roots of estimating functions with
nuisance parameters 135
5 Inference for descriptive parameters 143
5.1 Elements of descriptive sampling inference 143
5.1.1 Classical design based inference 143
5.1.2 Applicability to survey populations 145
5.1.3 Conditioning and post sampling stratification 146
5.1.4 Labels and likelihood 147
5.1.5 The role of prior knowledge 148
5.2 Superpopulation models 149
5.2.1 Exchangeable superpopulation models 150
5.2.2 Models with auxiliary variates 151
5.2.3 Time series and spatial process models 153
5.3 Prediction and inference 155
5.4 Randomization as support for statements of inference 157
5.4.1 Inferences based on exchangeability 157
5.4.2 Formal justification of conditioning 158
5.4.3 Survey weights 160
5.5 Evaluation of a sampling strategy 163
5.5.1 General considerations 163
5.5.2 Minimum expected variance criterion 165
5.5.3 Estimating function determination 168
5.6 Use of auxiliary information in estimation of means
and totals 171
5.7 Model assisted estimation through optimal estimating
functions 172
5.7.1 Estimating totals through means 172
5.7.2 Estimating totals through models: ratio and
regression estimators 173
5.8 GREG estimators 176
5.9 Calibration methods 178
5.10 Predictive approach to regression estimators of totals 181
5.11 The uncertainty in ratio and regression estimators 182
x CONTENTS
5.11.1 Approximate variance estimators 182
5.11.2 Variance estimators and survey weights 187
5.12 Conditional sampling properties of the ratio and
regression estimators 187
5.12.1 The ratio estimator 188
5.12.2 Inverse Gaussian based intervals 191
5.12.3 Simple regression estimator 192
5.12.4 Implications for inference 193
5.13 Robustness and mean function fitting 194
5.14 Estimating a population distribution function using
covariates 196
6 Analytic uses of survey data 199
6.1 What is analytic survey inference? 199
6.2 Single stage designs and the use of weights 202
6.2.1 Likelihood estimation with independent obser¬
vations 202
6.2.2 Estimating functions with weighted terms 204
6.2.3 Likelihood analysis under response dependent
Bernoulli sampling 206
6.2.4 Case control studies 208
6.2.5 Pseudolikelihood constructions in life testing 209
6.2.6 Estimation of the average of a mean function 210
6.3 Estimating functions for vector parameters 211
6.4 The generalized linear model for survey populations 214
6.4.1 The simple generalized linear model 215
6.4.2 Confidence regions 217
6.4.3 Nested models 219
6.4.4 An exponential model form 220
6.4.5 Summary of homogeneous population methods
for generalized linear models 222
6.4.6 Incorporating population heterogeneity 223
6.4.7 Generalized linear mixed model 223
6.4.8 Poisson overdispersion models 226
6.4.9 The marginal linear exponential model 228
6.4.10 Estimation when the population is clustered 230
6.4.11 Estimation of random effects and conditional
means 233
6.5 Sampling and the generalized linear model 236
CONTENTS xi
6.5.1 Estimation based on the model at the sample
level 238
6.5.2 Estimation through a pseudolikelihood 242
6.5.3 Design effect approach 246
7 Sampling strategies in time and space 251
7.1 Models for spatial populations 252
7.1.1 Models for the noise term in (7.1) 254
7.1.2 Models for the underlying process n, 254
7.1.3 Correlation and semivariogram models 255
7.1.4 Properties of realizations of the z process 257
7.1.5 Discrete population case 258
7.2 Spatial sampling designs 259
7.3 Pointwise prediction and predictive estimation of means
and totals 262
7.3.1 Prediction of Yk 264
7.3.2 Prediction of nh 266
7.3.3 Estimation of the deterministic part of the trend 267
7.3.4 Estimating totals and means 267
7.3.5 Determining coefficients for predictors 268
7.3.6 Estimating the uncertainty of prediction 269
7.3.7 Bayesian prediction 271
7.3.8 Prediction of {/*,: t e U) 274
7.4 Global means and totals as integrals 275
7.4.1 One spatial dimension 275
7.4.2 More than one spatial dimension 277
7.5 Choosing purposive samples 279
7.6 Choice of randomized sampling designs 281
References 287
Index 301
|
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| dewey-sort | 11.4222 |
| dewey-tens | 000 - Computer science, information, general works 510 - Mathematics |
| discipline | Allgemeines Mathematik |
| edition | 1. ed. |
| format | Book |
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| id | DE-604.BV011822940 |
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| indexdate | 2024-07-09T18:16:19Z |
| institution | BVB |
| isbn | 041231780X |
| language | English |
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| physical | XI, 305 S. graph. Darst. 23 cm |
| publishDate | 1997 |
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| publishDateSort | 1997 |
| publisher | Chapman & Hall |
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| series | Monographs on statistics and applied probability |
| series2 | Monographs on statistics and applied probability |
| spelling | Thompson, Mary E. 1944- Verfasser (DE-588)136660819 aut Theory of sample surveys M. E. Thompson 1. ed. London u.a. Chapman & Hall 1997 XI, 305 S. graph. Darst. 23 cm txt rdacontent n rdamedia nc rdacarrier Monographs on statistics and applied probability 74 Statistiek gtt Waarschijnlijkheidstheorie gtt Échantillonnage (Statistique) Statistik Sampling (Statistics) Stichprobe (DE-588)4057502-0 gnd rswk-swf Stichprobe (DE-588)4057502-0 s DE-604 Monographs on statistics and applied probability 74 (DE-604)BV002494005 74 http://www.loc.gov/catdir/enhancements/fy0731/97068068-d.html Publisher descriptionPublisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007982792&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Thompson, Mary E. 1944- Theory of sample surveys Monographs on statistics and applied probability Statistiek gtt Waarschijnlijkheidstheorie gtt Échantillonnage (Statistique) Statistik Sampling (Statistics) Stichprobe (DE-588)4057502-0 gnd |
| subject_GND | (DE-588)4057502-0 |
| title | Theory of sample surveys |
| title_auth | Theory of sample surveys |
| title_exact_search | Theory of sample surveys |
| title_full | Theory of sample surveys M. E. Thompson |
| title_fullStr | Theory of sample surveys M. E. Thompson |
| title_full_unstemmed | Theory of sample surveys M. E. Thompson |
| title_short | Theory of sample surveys |
| title_sort | theory of sample surveys |
| topic | Statistiek gtt Waarschijnlijkheidstheorie gtt Échantillonnage (Statistique) Statistik Sampling (Statistics) Stichprobe (DE-588)4057502-0 gnd |
| topic_facet | Statistiek Waarschijnlijkheidstheorie Échantillonnage (Statistique) Statistik Sampling (Statistics) Stichprobe |
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