Studiebog
    Survey Sampling Theory and Applications

    Survey Sampling Theory and Applications

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    Bog E-bog
    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling. The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students.

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    Detaljer
    • SprogEngelsk
    • Sidetal930
    • Udgivelsesdato09-03-2017
    • ISBN139780128118481
    • Forlag Academic Press Inc
    • FormatPaperback
    Størrelse og vægt
  • Vægt1340 g
    • coffee cup img
    10 cm
    book img
    15,1 cm
    22,9 cm

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    Efficiency Nonlinear programming Order statistics Eigenvalues Population Linear programming Bird banding Data Combinatorics cluster Adaptive sampling Confidence interval Domain Likelihood Distribution function Bootstrap Method Nonparametric regression Bayes Estimator Categorical Data Design effect Mean square error Logistic regression Measurement error Bootstrap Multiple imputation Parameter Distribution functions Point estimation Inverse sampling Estimating Functions Determination of Sample Size Balanced repeated replication Nonresponse Auxiliary information Maximum Likelihood Jackknife Empirical Likelihood Multistage sampling Poststratification Network Sampling Imputation Randomized response Estimator Admissible estimator Almost unbiased ratio estimator Area-level model Balanced repeated replication method Balanced sampling designs Balanced sample Bayesian imputation Balanced Incomplete Block Design Balanced systematic sampling Analytic Inference Circular systematic sampling Coefficient of variation CC method Complex Survey Autocorrelated population Concomitant variables Cluster Sampling Controlled sampling Calibration estimator Coordination of samples over time Combined regression estimator Complex survey design Composite estimator Chebyshev inequality Design-based inference Difference correlation method Difference estimator Complex design Calibration method Design-based approach Estimation of variance Dual to ratio estimator Direct estimation Equal probability sampling design Empirical Bayes Closed population Full optional response Combined Ratio Estimator Composite method complex designs Generalized jackknife estimator Generalized regression estimator End corrections Fay's method First-stage unit First-stage units Hartley�Ross estimator Fay�Harriet model Horvitz�Thompson estimator Higher-order jackknife estimator Inclusion probability proportional to size Deterministic imputation Ignorable nonresponse Hansen�Hurwitz estimator Inclusion probabilities Difference method of estimation Hypergeometric model interval estimation Hierarchical Bayes model Effective Sample Size Empirical best linear unbiased prediction Interpenetrating subsamples Exponential distribution Estimating equations Jackknife method Linear unbiased estimator Judgment ranking Exchangeable model Model design unbiased Mean imputation Linearization method Missing at random Location sampling Model-based approach Mail Questionnaire General Linear Mixed Model Multiframe sampling Murthy's estimator Measure of protection privacy Nearest probability proportional to size sampling design Model-assisted inference Not missing at random open population Optional randomized response Neyman's allocation Nearest proportional to size Nested Error Regression Model Optimum allocation Noninformative sampling Generalized variance functions Probability proportional to size Hot deck imputation Product estimator Raj estimator Random start Ranked set sample quantiles Post stratification PPS systematic sampling Proportional Allocation Raj's regression estimator Rao�Blackwellization Random imputation Random permutation model Intersection probabilities Mean square estimation Nonresponse Error Matched sample Mean per unit Optimum cluster size Parameter Space Partial optional response Lincoln method Missing completely at random Mobile Population Poisson sampling Primary unit Nonsampling error Pseudoempirical likelihood Measurement Bias Random group Mirror-match bootstrap Multiple marking Politz and Simmons method Probability proportional to size without replacement Optimal estimator Optimum estimating functions Ordered sample Probability proportional to aggregate size Probability proportional to size with replacement Raj's estimator Random group method

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