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Annual design-based estimation for the annualized inventories of forest inventory and analysis : sample size determination / / Hans T. Schreuder, Jin-Mann S. Lin, John Teply
| Annual design-based estimation for the annualized inventories of forest inventory and analysis : sample size determination / / Hans T. Schreuder, Jin-Mann S. Lin, John Teply |
| Autore | Schreuder Hans T. |
| Pubbl/distr/stampa | [Ogden, Utah] : , : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, , 2000 |
| Descrizione fisica | 1 online resource (3 pages) |
| Collana | General technical report RMRS |
| Soggetto topico |
Forest surveys - United States
Multiple imputation (Statistics) Forest surveys |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Annual design-based estimation for the annualized inventories of forest inventory and analysis |
| Record Nr. | UNINA-9910707492003321 |
Schreuder Hans T.
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| [Ogden, Utah] : , : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, , 2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data estimation and prediction for natural resources public data / / Hans T. Schreuder, Robin M. Reich
| Data estimation and prediction for natural resources public data / / Hans T. Schreuder, Robin M. Reich |
| Autore | Schreuder Hans T. |
| Pubbl/distr/stampa | Fort Collins, CO : , : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, , 1998 |
| Descrizione fisica | 1 online resource (5 pages) |
| Collana | Research note RMRS |
| Soggetto topico |
Forest surveys - United States - Databases - Management
Natural resources surveys - United States - Databases - Management Multiple imputation (Statistics) Missing observations (Statistics) Forest surveys |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910707115203321 |
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Schreuder Hans T.
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| Fort Collins, CO : , : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, , 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Estimates of alcohol involvement in fatal crashes [[electronic resource] ] : new alcohol methodology
| Estimates of alcohol involvement in fatal crashes [[electronic resource] ] : new alcohol methodology |
| Pubbl/distr/stampa | Washington, D.C. : , : National Center for Statistics and Analysis, , [2002] |
| Descrizione fisica | 6 pages : digital, PDF file |
| Soggetto topico |
Blood alcohol - Mathematical models
Drinking and traffic accidents - Mathematical models Traffic fatalities Multiple imputation (Statistics) Estimation theory |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Estimates of alcohol involvement in fatal crashes |
| Record Nr. | UNINA-9910697367203321 |
| Washington, D.C. : , : National Center for Statistics and Analysis, , [2002] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multiple Imputation and Its Application / / James R. Carpenter [and five others]
| Multiple Imputation and Its Application / / James R. Carpenter [and five others] |
| Autore | Carpenter James R. <1933-> |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Chichester, England : , : John Wiley & Sons Ltd, , [2023] |
| Descrizione fisica | 1 online resource (467 pages) |
| Disciplina | 610.724 |
| Collana | Statistics in Practice Series |
| Soggetto topico |
Medical statistics
Medicine - Research - Statistical methods Multiple imputation (Statistics) |
| ISBN |
1-119-75611-1
1-119-75609-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Part I FOUNDATIONS -- Chapter 1 Introduction -- 1.1 Reasons for missing data -- 1.2 Examples -- 1.3 Patterns of missing data -- 1.3.1 Consequences of missing data -- 1.4 Inferential framework and notation -- 1.4.1 Missing completely at random (MCAR) -- 1.4.2 Missing at random (MAR) -- 1.4.3 Missing not at random (MNAR) -- 1.4.4 Ignorability -- 1.5 Using observed data to inform assumptions about the missingness mechanism -- 1.6 Implications of missing data mechanisms for regression analyses -- 1.6.1 Partially observed response -- 1.6.2 Missing covariates -- 1.6.3 Missing covariates and response -- 1.6.4 Subtle issues I: the odds ratio -- 1.6.5 Implication for linear regression -- 1.6.6 Subtle issues II: sub‐sample ignorability -- 1.6.7 Summary: when restricting to complete records is valid -- Summary -- Exercises -- Chapter 2 The multiple imputation procedure and its justification -- 2.1 Introduction -- 2.2 Intuitive outline of the MI procedure -- 2.3 The generic MI procedure -- 2.4 Bayesian justification of MI -- 2.5 Frequentist inference -- 2.5.1 Large number of imputations -- 2.5.2 Small number of imputations -- 2.5.3 Inference for vector & -- bfitbeta -- -- 2.5.4 Combining likelihood ratio tests -- 2.6 Choosing the number of imputations -- 2.7 Some simple examples -- 2.7.1 Estimating the mean with σ2 known by the imputer and analyst -- 2.7.2 Estimating the mean with σ2 known only by the imputer -- 2.7.3 Estimating the mean with σ2 unknown -- 2.7.4 General linear regression with σ2 known -- 2.8 MI in more general settings -- 2.8.1 Proper imputation -- 2.8.2 Congenial imputation and substantive model -- 2.8.3 Uncongenial imputation and substantive models -- 2.8.4 Survey sample settings -- 2.9 Constructing congenial imputation models -- Discussion -- Exercises.
Part II MULTIPLE IMPUTATION FOR SIMPLE DATA STRUCTURES -- Chapter 3 Multiple imputation of quantitative data -- 3.1 Regression imputation with a monotone missingness pattern -- 3.1.1 MAR mechanisms consistent with a monotone pattern -- 3.1.2 Justification -- 3.2 Joint modelling -- 3.2.1 Fitting the imputation model -- 3.2.2 Adding covariates -- 3.3 Full conditional specification -- 3.3.1 Justification -- 3.4 Full conditional specification versus joint modelling -- 3.5 Software for multivariate normal imputation -- 3.6 Discussion -- 3.6 Exercises -- Chapter 4 Multiple imputation of binary and ordinal data -- 4.1 Sequential imputation with monotone missingness pattern -- 4.2 Joint modelling with the multivariate normal distribution -- 4.3 Modelling binary data using latent normal variables -- 4.3.1 Latent normal model for ordinal data -- 4.4 General location model -- 4.5 Full conditional specification -- 4.5.1 Justification -- 4.6 Issues with over‐fitting -- 4.7 Pros and cons of the various approaches -- 4.8 Software -- Discussion -- Exercises -- Chapter 5 Imputation of unordered categorical data -- 5.1 Monotone missing data -- 5.2 Multivariate normal imputation for categorical data -- 5.3 Maximum indicant model -- 5.3.1 Continuous and categorical variable -- 5.3.2 Imputing missing data -- 5.4 General location model -- 5.5 FCS with categorical data -- 5.6 Perfect prediction issues with categorical data -- 5.7 Software -- Discussion -- Exercises -- Part III Multiple imputation in practice -- Chapter 6 Non‐linear relationships, interactions, and other derived variables -- 6.1 Introduction -- 6.1.1 Interactions -- 6.1.2 Squares -- 6.1.3 Ratios -- 6.1.4 Sum scores -- 6.1.5 Composite endpoints -- 6.2 No missing data in derived variables -- 6.3 Simple methods -- 6.3.1 Impute then transform -- 6.3.2 Transform then impute/just another variable. 6.3.3 Adapting standard imputation models and passive imputation -- 6.3.4 Predictive mean matching -- 6.3.5 Imputation separately by groups for interactions -- 6.4 Substantive‐model‐compatible imputation -- 6.4.1 The basic idea -- 6.4.2 Latent‐normal joint model SMC imputation -- 6.4.3 Factorised conditional model SMC imputation -- 6.4.4 Substantive model compatible fully conditional specification -- 6.4.5 Auxiliary variables -- 6.4.6 Missing outcome values -- 6.4.7 Congeniality versus compatibility -- 6.4.8 Discussion of SMC imputation -- 6.5 Returning to the problems -- 6.5.1 Ratios -- 6.5.2 Splines -- 6.5.3 Fractional polynomials -- 6.5.4 Multiple imputation with conditional questions or 'skips' -- Exercises -- Chapter 7 Survival data -- 7.1 Missing covariates in time‐to‐event data -- 7.1.1 Approximately compatible approaches -- 7.1.2 Substantive model compatible approaches -- 7.2 Imputing censored event times -- 7.3 Non‐parametric, or 'hot deck' imputation -- 7.3.1 Non‐parametric imputation for time‐to‐event data -- 7.4 Case-cohort designs -- 7.4.1 Standard analysis of case-cohort studies -- 7.4.2 Multiple imputation for case-cohort studies -- 7.4.3 Full cohort -- 7.4.4 Intermediate approaches -- 7.4.5 Sub‐study approach -- Discussion -- Exercises -- Chapter 8 Prognostic models, missing data, and multiple imputation -- 8.1 Introduction -- 8.2 Motivating example -- 8.3 Missing data at model implementation -- 8.4 Multiple imputation for prognostic modelling -- 8.5 Model building -- 8.5.1 Model building with missing data -- 8.5.2 Imputing predictors when model building is to be performed -- 8.6 Model performance -- 8.6.1 How should we pool MI results for estimation of performance? -- 8.6.2 Calibration -- 8.6.3 Discrimination -- 8.6.4 Model performance measures with clinical interpretability -- 8.7 Model validation -- 8.7.1 Internal model validation. 8.7.2 External model validation -- 8.8 Incomplete data at implementation -- 8.8.1 MI for incomplete data at implementation -- 8.8.2 Alternatives to multiple imputation -- Exercises -- Chapter 9 Multi‐level multiple imputation -- 9.1 Multi‐level imputation model -- 9.1.1 Imputation of level‐1 variables -- 9.1.2 Imputation of level 2 variables -- 9.1.3 Accommodating the substantive model -- 9.2 MCMC algorithm for imputation model -- 9.2.1 Ordered and unordered categorical data -- 9.2.2 Imputing missing values -- 9.2.3 Substantive model compatible imputation -- 9.2.4 Checking model convergence -- 9.3 Extensions -- 9.3.1 Cross‐classification and three‐level data -- 9.3.2 Random level 1 covariance matrices -- 9.3.3 Model fit -- 9.4 Other imputation methods -- 9.4.1 One‐step and two‐step FCS -- 9.4.2 Substantive model compatible imputation -- 9.4.3 Non‐parametric methods -- 9.4.4 Comparisons of different methods -- 9.5 Individual participant data meta‐analysis -- 9.5.1 Different measurement scales -- 9.5.2 When to apply Rubin's rules -- 9.5.3 Homoscedastic versus heteroscedastic imputation model -- 9.6 Software -- Discussion -- Exercises -- Chapter 10 Sensitivity analysis: MI unleashed -- 10.1 Review of MNAR modelling -- 10.2 Framing sensitivity analysis: estimands -- 10.2.1 Definition of the estimand -- 10.2.2 Two common estimands -- 10.3 Pattern mixture modelling with MI -- 10.3.1 Missing covariates -- 10.3.2 Sensitivity with multiple variables: the NAR FCS procedure -- 10.3.3 Application to survival analysis -- 10.4 Pattern mixture approach with longitudinal data via MI -- 10.4.1 Change in slope post‐deviation -- 10.5 Reference based imputation -- 10.5.1 Constructing joint distributions of pre‐ and post‐intercurrent event data -- 10.5.2 Technical details -- 10.5.3 Software -- 10.5.4 Information anchoring. 10.6 Approximating a selection model by importance weighting -- 10.6.1 Weighting the imputations -- 10.6.2 Stacking the imputations and applying the weights -- Discussion -- Exercises -- Chapter 11 Multiple imputation for measurement error and misclassification -- 11.1 Introduction -- 11.2 Multiple imputation with validation data -- 11.2.1 Measurement error -- 11.2.2 Misclassification -- 11.2.3 Imputing assuming error is non‐differential -- 11.2.4 Non‐linear outcome models -- 11.3 Multiple imputation with replication data -- 11.3.1 Measurement error -- 11.3.2 Misclassification -- 11.4 External information on the measurement process -- Discussion -- Exercises -- Chapter 12 Multiple imputation with weights -- 12.1 Using model‐based predictions in strata -- 12.2 Bias in the MI variance estimator -- 12.3 MI with weights -- 12.3.1 Conditions for the consistency of & -- bfittheta -- & -- wHat -- MI -- 12.3.2 Conditions for the consistency of V& -- wHat -- MI -- 12.4 A multi‐level approach -- 12.4.1 Evaluation of the multi‐level multiple imputation approach for handling survey weights -- 12.4.2 Results -- 12.5 Further topics -- 12.5.1 Estimation in domains -- 12.5.2 Two‐stage analysis -- 12.5.3 Missing values in the weight model -- Discussion -- Exercises -- Chapter 13 Multiple imputation for causal inference -- 13.1 Multiple imputation for causal inference in point exposure studies -- 13.1.1 Randomised trials -- 13.1.2 Observational studies -- 13.2 Multiple imputation and propensity scores -- 13.2.1 Propensity scores for confounder adjustment -- 13.2.2 Multiple imputation of confounders -- 13.2.3 Imputation model specification -- 13.3 Principal stratification via multiple imputation -- 13.3.1 Principal strata effects -- 13.3.2 Estimation -- 13.4 Multiple imputation for IV analysis -- 13.4.1 Instrumental variable analysis for non‐adherence. 13.4.2 Instrumental variable analysis via multiple imputation. |
| Record Nr. | UNINA-9910830946003321 |
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Carpenter James R. <1933->
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| Chichester, England : , : John Wiley & Sons Ltd, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multiple imputation and its application [[electronic resource] /] / James R. Carpenter and Michael G. Kenward
| Multiple imputation and its application [[electronic resource] /] / James R. Carpenter and Michael G. Kenward |
| Autore | Carpenter James R |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2013 |
| Descrizione fisica | 1 online resource (490 pages) : illustrations, tables |
| Disciplina | 610.724 |
| Altri autori (Persone) | KenwardMichael G. <1956-> |
| Collana |
Statistics in Practice
Statistics in practice |
| Soggetto topico |
Statistics
Multiple imputation (Statistics) Medicine - Research - Statistical methods |
| ISBN |
1-119-94228-4
1-119-94227-6 1-118-44261-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Reasons for missing data Summary -- The Multiple Imputation Procedure and Its Justification -- Multiple imputation of binary and ordinal data -- Imputation of unordered categorical data -- Non-linear relationships -- Interactions -- Survival data, skips and large datasets -- Multilevel multiple imputation -- Sensitivity analysis: MI unleashed -- Including survey weights -- Robust Multiple Imputation. |
| Record Nr. | UNINA-9910141616403321 |
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Carpenter James R
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| Chichester, West Sussex, U.K., : Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multiple imputation and its application / / James R. Carpenter and Michael G. Kenward
| Multiple imputation and its application / / James R. Carpenter and Michael G. Kenward |
| Autore | Carpenter James R |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2013 |
| Descrizione fisica | 1 online resource (490 pages) : illustrations, tables |
| Disciplina | 610.724 |
| Altri autori (Persone) | KenwardMichael G. <1956-> |
| Collana |
Statistics in Practice
Statistics in practice |
| Soggetto topico |
Statistics
Multiple imputation (Statistics) Medicine - Research - Statistical methods |
| ISBN |
1-119-94228-4
1-119-94227-6 1-118-44261-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Reasons for missing data Summary -- The Multiple Imputation Procedure and Its Justification -- Multiple imputation of binary and ordinal data -- Imputation of unordered categorical data -- Non-linear relationships -- Interactions -- Survival data, skips and large datasets -- Multilevel multiple imputation -- Sensitivity analysis: MI unleashed -- Including survey weights -- Robust Multiple Imputation. |
| Record Nr. | UNINA-9910819826003321 |
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Carpenter James R
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| Chichester, West Sussex, U.K., : Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multiple imputation for nonresponse in surveys [[electronic resource] /] / Donald B. Rubin
| Multiple imputation for nonresponse in surveys [[electronic resource] /] / Donald B. Rubin |
| Autore | Rubin Donald B |
| Pubbl/distr/stampa | Hoboken, N.J. ; , : Wiley-Interscience, 2004 |
| Descrizione fisica | 1 online resource (xxix, 287 p. ) : ill |
| Disciplina | 001.422 |
| Collana |
Wiley series in probability and mathematical statistics. Multiple imputation for nonresponse in surveys
Wiley classics library |
| Soggetto topico |
Multiple imputation (Statistics)
Nonresponse (Statistics) Social surveys - Response rate Multiple imputation (Statistics) - Response rate Social surveys Social Sciences Statistics - General |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-30759-2
9786612307591 0-470-31669-1 0-470-31736-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Tables and Figures. Glossary. 1. Introduction. 1.1 Overview. 1.2 Examples of Surveys with Nonresponse. 1.3 Properly Handling Nonresponse. 1.4 Single Imputation. 1.5 Multiple Imputation. 1.6 Numerical Example Using Multiple Imputation. 1.7 Guidance for the Reader. 2. Statistical Background. 2.1 Introduction. 2.2 Variables in the Finite Population. 2.3 Probability Distributions and Related Calculations. 2.4 Probability Specifications for Indicator Variables. 2.5 Probability Specifications for ( X,Y ). 2.6 Bayesian Inference for a Population Quality. 2.7 Interval Estimation. 2.8 Bayesian Procedures for Constructing Interval Estimates, Including Significance Levels and Point Estimates. 2.9 Evaluating the Performance of Procedures. 2.10 Similarity of Bayesian and Randomization-Based Inferences in Many Practical Cases. 3. Underlying Bayesian Theory. 3.1 Introduction and Summary of Repeated-Imputation Inferences. 3.2 Key Results for Analysis When the Multiple Imputations are Repeated Draws from the Posterior Distribution of the Missing Values. 3.3 Inference for Scalar Estimands from a Modest Number of Repeated Completed-Data Means and Variances. 3.4 Significance Levels for Multicomponent Estimands from a Modest Number of Repeated Completed-Data Means and Variance-Covariance Matrices. 3.5 Significance Levels from Repeated Completed-Data Significance Levels. 3.6 Relating the Completed-Data and Completed-Data Posterior Distributions When the Sampling Mechanism is Ignorable. 4. Randomization-Based Evaluations. 4.1 Introduction. 4.2 General Conditions for the Randomization-Validity of Infinite- m Repeated-Imputation Inferences. 4.3Examples of Proper and Improper Imputation Methods in a Simple Case with Ignorable Nonresponse. 4.4 Further Discussion of Proper Imputation Methods. 4.5 The Asymptotic Distibution of (&Qmacr; m ,Ū m ,B m ) for Proper Imputation Methods. 4.6 Evaluations of Finite- m Inferences with Scalar Estimands. 4.7 Evaluation of Significance Levels from the Moment-Based Statistics D m and &Dtilde; m with Multicomponent Estimands. 4.8 Evaluation of Significance Levels Based on Repeated Significance Levels. 5. Procedures with Ignorable Nonresponse. 5.1 Introduction. 5.2 Creating Imputed Values under an Explicit Model. 5.3 Some Explicit Imputation Models with Univariate Y I and Covariates. 5.4 Monotone Patterns of Missingness in Multivariate Y I . 5.5 Missing Social Security Benefits in the Current Population Survey. 5.6 Beyond Monotone Missingness. 6. Procedures with Nonignorable Nonresponse. 6.1 Introduction. 6.2 Nonignorable Nonresponse with Univariate Y I and No X I . 6.3 Formal Tasks with Nonignorable Nonresponse. 6.4 Illustrating Mixture Modeling Using Educational Testing Service Data. 6.5 Illustrating Selection Modeling Using CPS Data. 6.6 Extensions to Surveys with Follow-Ups. 6.7 Follow-Up Response in a Survey of Drinking Behavior Among Men of Retirement Age. References. Author Index. Subject Index. Appendix I. Report Written for the Social Security Administration in 1977. Appendix II. Report Written for the Census Bureau in 1983. |
| Record Nr. | UNINA-9910144694003321 |
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Rubin Donald B
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| Hoboken, N.J. ; , : Wiley-Interscience, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multiple imputation for nonresponse in surveys [[electronic resource] /] / Donald B. Rubin
| Multiple imputation for nonresponse in surveys [[electronic resource] /] / Donald B. Rubin |
| Autore | Rubin Donald B |
| Pubbl/distr/stampa | Hoboken, N.J. ; , : Wiley-Interscience, 2004 |
| Descrizione fisica | 1 online resource (xxix, 287 p. ) : ill |
| Disciplina | 001.422 |
| Collana |
Wiley series in probability and mathematical statistics. Multiple imputation for nonresponse in surveys
Wiley classics library |
| Soggetto topico |
Multiple imputation (Statistics)
Nonresponse (Statistics) Social surveys - Response rate Multiple imputation (Statistics) - Response rate Social surveys Social Sciences Statistics - General |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-30759-2
9786612307591 0-470-31669-1 0-470-31736-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Tables and Figures. Glossary. 1. Introduction. 1.1 Overview. 1.2 Examples of Surveys with Nonresponse. 1.3 Properly Handling Nonresponse. 1.4 Single Imputation. 1.5 Multiple Imputation. 1.6 Numerical Example Using Multiple Imputation. 1.7 Guidance for the Reader. 2. Statistical Background. 2.1 Introduction. 2.2 Variables in the Finite Population. 2.3 Probability Distributions and Related Calculations. 2.4 Probability Specifications for Indicator Variables. 2.5 Probability Specifications for ( X,Y ). 2.6 Bayesian Inference for a Population Quality. 2.7 Interval Estimation. 2.8 Bayesian Procedures for Constructing Interval Estimates, Including Significance Levels and Point Estimates. 2.9 Evaluating the Performance of Procedures. 2.10 Similarity of Bayesian and Randomization-Based Inferences in Many Practical Cases. 3. Underlying Bayesian Theory. 3.1 Introduction and Summary of Repeated-Imputation Inferences. 3.2 Key Results for Analysis When the Multiple Imputations are Repeated Draws from the Posterior Distribution of the Missing Values. 3.3 Inference for Scalar Estimands from a Modest Number of Repeated Completed-Data Means and Variances. 3.4 Significance Levels for Multicomponent Estimands from a Modest Number of Repeated Completed-Data Means and Variance-Covariance Matrices. 3.5 Significance Levels from Repeated Completed-Data Significance Levels. 3.6 Relating the Completed-Data and Completed-Data Posterior Distributions When the Sampling Mechanism is Ignorable. 4. Randomization-Based Evaluations. 4.1 Introduction. 4.2 General Conditions for the Randomization-Validity of Infinite- m Repeated-Imputation Inferences. 4.3Examples of Proper and Improper Imputation Methods in a Simple Case with Ignorable Nonresponse. 4.4 Further Discussion of Proper Imputation Methods. 4.5 The Asymptotic Distibution of (&Qmacr; m ,Ū m ,B m ) for Proper Imputation Methods. 4.6 Evaluations of Finite- m Inferences with Scalar Estimands. 4.7 Evaluation of Significance Levels from the Moment-Based Statistics D m and &Dtilde; m with Multicomponent Estimands. 4.8 Evaluation of Significance Levels Based on Repeated Significance Levels. 5. Procedures with Ignorable Nonresponse. 5.1 Introduction. 5.2 Creating Imputed Values under an Explicit Model. 5.3 Some Explicit Imputation Models with Univariate Y I and Covariates. 5.4 Monotone Patterns of Missingness in Multivariate Y I . 5.5 Missing Social Security Benefits in the Current Population Survey. 5.6 Beyond Monotone Missingness. 6. Procedures with Nonignorable Nonresponse. 6.1 Introduction. 6.2 Nonignorable Nonresponse with Univariate Y I and No X I . 6.3 Formal Tasks with Nonignorable Nonresponse. 6.4 Illustrating Mixture Modeling Using Educational Testing Service Data. 6.5 Illustrating Selection Modeling Using CPS Data. 6.6 Extensions to Surveys with Follow-Ups. 6.7 Follow-Up Response in a Survey of Drinking Behavior Among Men of Retirement Age. References. Author Index. Subject Index. Appendix I. Report Written for the Social Security Administration in 1977. Appendix II. Report Written for the Census Bureau in 1983. |
| Record Nr. | UNISA-996201251903316 |
|
Rubin Donald B
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| Hoboken, N.J. ; , : Wiley-Interscience, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Multiple imputation for nonresponse in surveys [[electronic resource] /] / Donald B. Rubin
| Multiple imputation for nonresponse in surveys [[electronic resource] /] / Donald B. Rubin |
| Autore | Rubin Donald B |
| Pubbl/distr/stampa | Hoboken, N.J. ; , : Wiley-Interscience, 2004 |
| Descrizione fisica | 1 online resource (xxix, 287 p. ) : ill |
| Disciplina | 001.422 |
| Collana |
Wiley series in probability and mathematical statistics. Multiple imputation for nonresponse in surveys
Wiley classics library |
| Soggetto topico |
Multiple imputation (Statistics)
Nonresponse (Statistics) Social surveys - Response rate Multiple imputation (Statistics) - Response rate Social surveys Social Sciences Statistics - General |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-30759-2
9786612307591 0-470-31669-1 0-470-31736-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Tables and Figures. Glossary. 1. Introduction. 1.1 Overview. 1.2 Examples of Surveys with Nonresponse. 1.3 Properly Handling Nonresponse. 1.4 Single Imputation. 1.5 Multiple Imputation. 1.6 Numerical Example Using Multiple Imputation. 1.7 Guidance for the Reader. 2. Statistical Background. 2.1 Introduction. 2.2 Variables in the Finite Population. 2.3 Probability Distributions and Related Calculations. 2.4 Probability Specifications for Indicator Variables. 2.5 Probability Specifications for ( X,Y ). 2.6 Bayesian Inference for a Population Quality. 2.7 Interval Estimation. 2.8 Bayesian Procedures for Constructing Interval Estimates, Including Significance Levels and Point Estimates. 2.9 Evaluating the Performance of Procedures. 2.10 Similarity of Bayesian and Randomization-Based Inferences in Many Practical Cases. 3. Underlying Bayesian Theory. 3.1 Introduction and Summary of Repeated-Imputation Inferences. 3.2 Key Results for Analysis When the Multiple Imputations are Repeated Draws from the Posterior Distribution of the Missing Values. 3.3 Inference for Scalar Estimands from a Modest Number of Repeated Completed-Data Means and Variances. 3.4 Significance Levels for Multicomponent Estimands from a Modest Number of Repeated Completed-Data Means and Variance-Covariance Matrices. 3.5 Significance Levels from Repeated Completed-Data Significance Levels. 3.6 Relating the Completed-Data and Completed-Data Posterior Distributions When the Sampling Mechanism is Ignorable. 4. Randomization-Based Evaluations. 4.1 Introduction. 4.2 General Conditions for the Randomization-Validity of Infinite- m Repeated-Imputation Inferences. 4.3Examples of Proper and Improper Imputation Methods in a Simple Case with Ignorable Nonresponse. 4.4 Further Discussion of Proper Imputation Methods. 4.5 The Asymptotic Distibution of (&Qmacr; m ,Ū m ,B m ) for Proper Imputation Methods. 4.6 Evaluations of Finite- m Inferences with Scalar Estimands. 4.7 Evaluation of Significance Levels from the Moment-Based Statistics D m and &Dtilde; m with Multicomponent Estimands. 4.8 Evaluation of Significance Levels Based on Repeated Significance Levels. 5. Procedures with Ignorable Nonresponse. 5.1 Introduction. 5.2 Creating Imputed Values under an Explicit Model. 5.3 Some Explicit Imputation Models with Univariate Y I and Covariates. 5.4 Monotone Patterns of Missingness in Multivariate Y I . 5.5 Missing Social Security Benefits in the Current Population Survey. 5.6 Beyond Monotone Missingness. 6. Procedures with Nonignorable Nonresponse. 6.1 Introduction. 6.2 Nonignorable Nonresponse with Univariate Y I and No X I . 6.3 Formal Tasks with Nonignorable Nonresponse. 6.4 Illustrating Mixture Modeling Using Educational Testing Service Data. 6.5 Illustrating Selection Modeling Using CPS Data. 6.6 Extensions to Surveys with Follow-Ups. 6.7 Follow-Up Response in a Survey of Drinking Behavior Among Men of Retirement Age. References. Author Index. Subject Index. Appendix I. Report Written for the Social Security Administration in 1977. Appendix II. Report Written for the Census Bureau in 1983. |
| Record Nr. | UNINA-9910830648503321 |
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Rubin Donald B
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| Hoboken, N.J. ; , : Wiley-Interscience, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Transitioning to multiple imputation [[electronic resource] ] : a new method to impute missing blood alcohol concentration (BAC) values in FARS / / Rajesh Subramanian
| Transitioning to multiple imputation [[electronic resource] ] : a new method to impute missing blood alcohol concentration (BAC) values in FARS / / Rajesh Subramanian |
| Autore | Subramanian Rajesh |
| Edizione | [Rev.] |
| Pubbl/distr/stampa | Washington, D.C : , : National Center for Statistics and Analysis, Research and Development, , [2002] |
| Descrizione fisica | 36 pages : digital, PDF file |
| Collana | NHTSA technical report |
| Soggetto topico |
Blood alcohol
Blood alcohol - Analysis Multiple imputation (Statistics) Drinking and traffic accidents - United States |
| Soggetto genere / forma | Statistics. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Transitioning to multiple imputation |
| Record Nr. | UNINA-9910696831103321 |
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Subramanian Rajesh
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| Washington, D.C : , : National Center for Statistics and Analysis, Research and Development, , [2002] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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