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Causality [[electronic resource] ] : statistical perspectives and applications / / edited by Carlo Berzuini, Philip Dawid, Luisa Bernardinelli
| Causality [[electronic resource] ] : statistical perspectives and applications / / edited by Carlo Berzuini, Philip Dawid, Luisa Bernardinelli |
| Autore | Berzuini Carlo |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2012 |
| Descrizione fisica | 1 online resource (415 p.) |
| Disciplina | 519.5/44 |
| Altri autori (Persone) |
BerzuiniCarlo
DawidPhilip BernardinelliLuisa |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Estimation theory
Causation Causality (Physics) |
| ISBN |
1-119-94173-3
1-280-67923-9 9786613656162 1-119-94571-2 1-119-94570-4 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical causality : some historical remarks -- The language of potential outcomes -- Structural equations, graphs and interventions -- The decision-theoretic approach to causal -- Causal inference as a prediction problem : assumptions, identification, and evidence synthesis -- Graph-based criteria of identifiability of causal questions -- Causal inference from observational data : a Bayesian predictive approach -- Causal inference from observing sequences of actions -- Causal effects and natural laws : towards a conceptualization of causal counterfactuals -- For non-manipulable exposures, with application to the effects of race and sex -- Cross-classifications by joint potential outcomes -- Estimation of direct and indirect effects -- The mediation formula : a guide to the assessment of causal pathways in nonlinear models -- The sufficient cause framework in statistics, philosophy and the biomedical and social sciences -- Inference about biological mechanism on the basis of epidemiological data -- Ion channels and multiple sclerosis -- Supplementary variables for causal estimation -- Time-varying confounding : some practical considerations in a likelihood framework -- Natural experiments as a means of testing causal inferences -- Nonreactive and purely reactive doses in observational studies -- Evaluation of potential mediators in randomized trials of complex interventions (psychotherapies) -- Causal inference in clinical trials -- Granger causality and causal inference in time series analysis -- Dynamic molecular networks and mechanisms iIn the biosciences : a statistical framework. |
| Record Nr. | UNINA-9910139088403321 |
Berzuini Carlo
|
||
| Chichester, West Sussex, U.K., : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Causality [[electronic resource] ] : statistical perspectives and applications / / edited by Carlo Berzuini, Philip Dawid, Luisa Bernardinelli
| Causality [[electronic resource] ] : statistical perspectives and applications / / edited by Carlo Berzuini, Philip Dawid, Luisa Bernardinelli |
| Autore | Berzuini Carlo |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2012 |
| Descrizione fisica | 1 online resource (415 p.) |
| Disciplina | 519.5/44 |
| Altri autori (Persone) |
BerzuiniCarlo
DawidPhilip BernardinelliLuisa |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Estimation theory
Causation Causality (Physics) |
| ISBN |
1-119-94173-3
1-280-67923-9 9786613656162 1-119-94571-2 1-119-94570-4 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical causality : some historical remarks -- The language of potential outcomes -- Structural equations, graphs and interventions -- The decision-theoretic approach to causal -- Causal inference as a prediction problem : assumptions, identification, and evidence synthesis -- Graph-based criteria of identifiability of causal questions -- Causal inference from observational data : a Bayesian predictive approach -- Causal inference from observing sequences of actions -- Causal effects and natural laws : towards a conceptualization of causal counterfactuals -- For non-manipulable exposures, with application to the effects of race and sex -- Cross-classifications by joint potential outcomes -- Estimation of direct and indirect effects -- The mediation formula : a guide to the assessment of causal pathways in nonlinear models -- The sufficient cause framework in statistics, philosophy and the biomedical and social sciences -- Inference about biological mechanism on the basis of epidemiological data -- Ion channels and multiple sclerosis -- Supplementary variables for causal estimation -- Time-varying confounding : some practical considerations in a likelihood framework -- Natural experiments as a means of testing causal inferences -- Nonreactive and purely reactive doses in observational studies -- Evaluation of potential mediators in randomized trials of complex interventions (psychotherapies) -- Causal inference in clinical trials -- Granger causality and causal inference in time series analysis -- Dynamic molecular networks and mechanisms iIn the biosciences : a statistical framework. |
| Record Nr. | UNINA-9910820793703321 |
|
Berzuini Carlo
|
||
| Chichester, West Sussex, U.K., : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
| The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan |
| Autore | McLachlan Geoffrey J. <1946-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2008 |
| Descrizione fisica | 1 online resource (399 p.) |
| Disciplina |
519.5
519.5/44 519.544 |
| Altri autori (Persone) | KrishnanT <1938-> (Thriyambakam) |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Expectation-maximization algorithms
Estimation theory Missing observations (Statistics) |
| ISBN |
1-281-28447-5
9786611284473 0-470-19161-9 0-470-19160-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
The EM Algorithm and Extensions; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; LIST OF EXAMPLES; 1 GENERAL INTRODUCTION; 1.1 Introduction; 1.2 Maximum Likelihood Estimation; 1.3 Newton-Type Methods; 1.3.1 Introduction; 1.3.2 Newton-Raphson Method; 1.3.3 Quasi-Newton Methods; 1.3.4 Modified Newton Methods; 1.4 Introductory Examples; 1.4.1 Introduction; 1.4.2 Example 1.1: A Multinomial Example; 1.4.3 Example 1.2: Estimation of Mixing Proportions; 1.5 Formulation of the EM Algorithm; 1.5.1 EM Algorithm; 1.5.2 Example 1.3: Censored Exponentially Distributed Survival Times
1.5.3 E- and M-Steps for the Regular Exponential Family1.5.4 Example 1.4: Censored Exponentially Distributed Survival Times (Example 1.3 Continued); 1.5.5 Generalized EM Algorithm; 1.5.6 GEM Algorithm Based on One Newton-Raphson Step; 1.5.7 EM Gradient Algorithm; 1.5.8 EM Mapping; 1.6 EM Algorithm for MAP and MPL Estimation; 1.6.1 Maximum a Posteriori Estimation; 1.6.2 Example 1.5: A Multinomial Example (Example 1.1 Continued); 1.6.3 Maximum Penalized Estimation; 1.7 Brief Summary of the Properties of the EM Algorithm; 1.8 History of the EM Algorithm; 1.8.1 Early EM History 1.8.2 Work Before Dempster, Laird, and Rubin (1977)1.8.3 EM Examples and Applications Since Dempster, Laird, and Rubin (1977); 1.8.4 Two Interpretations of EM; 1.8.5 Developments in EM Theory, Methodology, and Applications; 1.9 Overview of the Book; 1.10 Notations; 2 EXAMPLES OF THE EM ALGORITHM; 2.1 Introduction; 2.2 Multivariate Data with Missing Values; 2.2.1 Example 2.1: Bivariate Normal Data with Missing Values; 2.2.2 Numerical Illustration; 2.2.3 Multivariate Data: Buck's Method; 2.3 Least Squares with Missing Data; 2.3.1 Healy-Westmacott Procedure 2.3.2 Example 2.2: Linear Regression with Missing Dependent Values2.3.3 Example 2.3: Missing Values in a Latin Square Design; 2.3.4 Healy-Westmacott Procedure as an EM Algorithm; 2.4 Example 2.4: Multinomial with Complex Cell Structure; 2.5 Example 2.5: Analysis of PET and SPECT Data; 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.); 2.6.1 ML Estimation of Multivariate t-Distribution; 2.6.2 Numerical Example: Stack Loss Data; 2.7 Finite Normal Mixtures; 2.7.1 Example 2.7: Univariate Component Densities; 2.7.2 Example 2.8: Multivariate Component Densities 2.7.3 Numerical Example: Red Blood Cell Volume Data2.8 Example 2.9: Grouped and Truncated Data; 2.8.1 Introduction; 2.8.2 Specification of Complete Data; 2.8.3 E-Step; 2.8.4 M-Step; 2.8.5 Confirmation of Incomplete-Data Score Statistic; 2.8.6 M-Step for Grouped Normal Data; 2.8.7 Numerical Example: Grouped Log Normal Data; 2.9 Example 2.10: A Hidden Markov AR(1) model; 3 BASIC THEORY OF THE EM ALGORITHM; 3.1 Introduction; 3.2 Monotonicity of the EM Algorithm; 3.3 Monotonicity of a Generalized EM Algorithm; 3.4 Convergence of an EM Sequence to a Stationary Value; 3.4.1 Introduction 3.4.2 Regularity Conditions of Wu (1983) |
| Record Nr. | UNINA-9910145008603321 |
|
McLachlan Geoffrey J. <1946->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
| The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan |
| Autore | McLachlan Geoffrey J. <1946-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2008 |
| Descrizione fisica | 1 online resource (399 p.) |
| Disciplina |
519.5
519.5/44 519.544 |
| Altri autori (Persone) | KrishnanT <1938-> (Thriyambakam) |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Expectation-maximization algorithms
Estimation theory Missing observations (Statistics) |
| ISBN |
1-281-28447-5
9786611284473 0-470-19161-9 0-470-19160-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
The EM Algorithm and Extensions; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; LIST OF EXAMPLES; 1 GENERAL INTRODUCTION; 1.1 Introduction; 1.2 Maximum Likelihood Estimation; 1.3 Newton-Type Methods; 1.3.1 Introduction; 1.3.2 Newton-Raphson Method; 1.3.3 Quasi-Newton Methods; 1.3.4 Modified Newton Methods; 1.4 Introductory Examples; 1.4.1 Introduction; 1.4.2 Example 1.1: A Multinomial Example; 1.4.3 Example 1.2: Estimation of Mixing Proportions; 1.5 Formulation of the EM Algorithm; 1.5.1 EM Algorithm; 1.5.2 Example 1.3: Censored Exponentially Distributed Survival Times
1.5.3 E- and M-Steps for the Regular Exponential Family1.5.4 Example 1.4: Censored Exponentially Distributed Survival Times (Example 1.3 Continued); 1.5.5 Generalized EM Algorithm; 1.5.6 GEM Algorithm Based on One Newton-Raphson Step; 1.5.7 EM Gradient Algorithm; 1.5.8 EM Mapping; 1.6 EM Algorithm for MAP and MPL Estimation; 1.6.1 Maximum a Posteriori Estimation; 1.6.2 Example 1.5: A Multinomial Example (Example 1.1 Continued); 1.6.3 Maximum Penalized Estimation; 1.7 Brief Summary of the Properties of the EM Algorithm; 1.8 History of the EM Algorithm; 1.8.1 Early EM History 1.8.2 Work Before Dempster, Laird, and Rubin (1977)1.8.3 EM Examples and Applications Since Dempster, Laird, and Rubin (1977); 1.8.4 Two Interpretations of EM; 1.8.5 Developments in EM Theory, Methodology, and Applications; 1.9 Overview of the Book; 1.10 Notations; 2 EXAMPLES OF THE EM ALGORITHM; 2.1 Introduction; 2.2 Multivariate Data with Missing Values; 2.2.1 Example 2.1: Bivariate Normal Data with Missing Values; 2.2.2 Numerical Illustration; 2.2.3 Multivariate Data: Buck's Method; 2.3 Least Squares with Missing Data; 2.3.1 Healy-Westmacott Procedure 2.3.2 Example 2.2: Linear Regression with Missing Dependent Values2.3.3 Example 2.3: Missing Values in a Latin Square Design; 2.3.4 Healy-Westmacott Procedure as an EM Algorithm; 2.4 Example 2.4: Multinomial with Complex Cell Structure; 2.5 Example 2.5: Analysis of PET and SPECT Data; 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.); 2.6.1 ML Estimation of Multivariate t-Distribution; 2.6.2 Numerical Example: Stack Loss Data; 2.7 Finite Normal Mixtures; 2.7.1 Example 2.7: Univariate Component Densities; 2.7.2 Example 2.8: Multivariate Component Densities 2.7.3 Numerical Example: Red Blood Cell Volume Data2.8 Example 2.9: Grouped and Truncated Data; 2.8.1 Introduction; 2.8.2 Specification of Complete Data; 2.8.3 E-Step; 2.8.4 M-Step; 2.8.5 Confirmation of Incomplete-Data Score Statistic; 2.8.6 M-Step for Grouped Normal Data; 2.8.7 Numerical Example: Grouped Log Normal Data; 2.9 Example 2.10: A Hidden Markov AR(1) model; 3 BASIC THEORY OF THE EM ALGORITHM; 3.1 Introduction; 3.2 Monotonicity of the EM Algorithm; 3.3 Monotonicity of a Generalized EM Algorithm; 3.4 Convergence of an EM Sequence to a Stationary Value; 3.4.1 Introduction 3.4.2 Regularity Conditions of Wu (1983) |
| Record Nr. | UNINA-9910831039703321 |
|
McLachlan Geoffrey J. <1946->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan
| The EM algorithm and extensions [[electronic resource] /] / Geoffrey J. McLachlan, Thriyambakam Krishnan |
| Autore | McLachlan Geoffrey J. <1946-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2008 |
| Descrizione fisica | 1 online resource (399 p.) |
| Disciplina |
519.5
519.5/44 519.544 |
| Altri autori (Persone) | KrishnanT <1938-> (Thriyambakam) |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Expectation-maximization algorithms
Estimation theory Missing observations (Statistics) |
| ISBN |
1-281-28447-5
9786611284473 0-470-19161-9 0-470-19160-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
The EM Algorithm and Extensions; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; LIST OF EXAMPLES; 1 GENERAL INTRODUCTION; 1.1 Introduction; 1.2 Maximum Likelihood Estimation; 1.3 Newton-Type Methods; 1.3.1 Introduction; 1.3.2 Newton-Raphson Method; 1.3.3 Quasi-Newton Methods; 1.3.4 Modified Newton Methods; 1.4 Introductory Examples; 1.4.1 Introduction; 1.4.2 Example 1.1: A Multinomial Example; 1.4.3 Example 1.2: Estimation of Mixing Proportions; 1.5 Formulation of the EM Algorithm; 1.5.1 EM Algorithm; 1.5.2 Example 1.3: Censored Exponentially Distributed Survival Times
1.5.3 E- and M-Steps for the Regular Exponential Family1.5.4 Example 1.4: Censored Exponentially Distributed Survival Times (Example 1.3 Continued); 1.5.5 Generalized EM Algorithm; 1.5.6 GEM Algorithm Based on One Newton-Raphson Step; 1.5.7 EM Gradient Algorithm; 1.5.8 EM Mapping; 1.6 EM Algorithm for MAP and MPL Estimation; 1.6.1 Maximum a Posteriori Estimation; 1.6.2 Example 1.5: A Multinomial Example (Example 1.1 Continued); 1.6.3 Maximum Penalized Estimation; 1.7 Brief Summary of the Properties of the EM Algorithm; 1.8 History of the EM Algorithm; 1.8.1 Early EM History 1.8.2 Work Before Dempster, Laird, and Rubin (1977)1.8.3 EM Examples and Applications Since Dempster, Laird, and Rubin (1977); 1.8.4 Two Interpretations of EM; 1.8.5 Developments in EM Theory, Methodology, and Applications; 1.9 Overview of the Book; 1.10 Notations; 2 EXAMPLES OF THE EM ALGORITHM; 2.1 Introduction; 2.2 Multivariate Data with Missing Values; 2.2.1 Example 2.1: Bivariate Normal Data with Missing Values; 2.2.2 Numerical Illustration; 2.2.3 Multivariate Data: Buck's Method; 2.3 Least Squares with Missing Data; 2.3.1 Healy-Westmacott Procedure 2.3.2 Example 2.2: Linear Regression with Missing Dependent Values2.3.3 Example 2.3: Missing Values in a Latin Square Design; 2.3.4 Healy-Westmacott Procedure as an EM Algorithm; 2.4 Example 2.4: Multinomial with Complex Cell Structure; 2.5 Example 2.5: Analysis of PET and SPECT Data; 2.6 Example 2.6: Multivariate t-Distribution (Known D.F.); 2.6.1 ML Estimation of Multivariate t-Distribution; 2.6.2 Numerical Example: Stack Loss Data; 2.7 Finite Normal Mixtures; 2.7.1 Example 2.7: Univariate Component Densities; 2.7.2 Example 2.8: Multivariate Component Densities 2.7.3 Numerical Example: Red Blood Cell Volume Data2.8 Example 2.9: Grouped and Truncated Data; 2.8.1 Introduction; 2.8.2 Specification of Complete Data; 2.8.3 E-Step; 2.8.4 M-Step; 2.8.5 Confirmation of Incomplete-Data Score Statistic; 2.8.6 M-Step for Grouped Normal Data; 2.8.7 Numerical Example: Grouped Log Normal Data; 2.9 Example 2.10: A Hidden Markov AR(1) model; 3 BASIC THEORY OF THE EM ALGORITHM; 3.1 Introduction; 3.2 Monotonicity of the EM Algorithm; 3.3 Monotonicity of a Generalized EM Algorithm; 3.4 Convergence of an EM Sequence to a Stationary Value; 3.4.1 Introduction 3.4.2 Regularity Conditions of Wu (1983) |
| Record Nr. | UNINA-9910841304303321 |
|
McLachlan Geoffrey J. <1946->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström
| Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström |
| Autore | Särndal Carl-Erik <1937-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (214 p.) |
| Disciplina |
001.433
519.5/44 519.544 |
| Altri autori (Persone) | LundströmSixten |
| Collana | Wiley Series in Survey Methodology |
| Soggetto topico |
Estimation theory
Sampling (Statistics) Nonresponse (Statistics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-27623-1
9786610276233 0-470-01135-1 0-470-01134-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Estimation in Surveys with Nonresponse; Contents; Preface; Chapter 1 Introduction; Chapter 2 The Survey and Its Imperfections; 2.1 The survey objective; 2.2 Sources of error in a survey; Chapter 3 General Principles to Assist Estimation; 3.1 Introduction; 3.2 The importance of auxiliary information; 3.3 Desirable features of an auxiliary vector; Chapter 4 The Use of Auxiliary Information under Ideal Conditions; 4.1 Introduction; 4.2 The Horvitz-Thompson estimator; 4.3 The generalized regression estimator; 4.4 Variance and variance estimation
4.5 Examples of the generalized regression estimatorChapter 5 Introduction to Estimation in the Presence of Nonresponse; 5.1 General background; 5.2 Errors caused by sampling and nonresponse; Appendix: Variance and mean squared error under nonresponse; Chapter 6 Weighting of Data in the Presence of Nonresponse; 6.1 Traditional approaches to weighting; 6.2 Auxiliary vectors and auxiliary information; 6.3 The calibration approach: some terminology; 6.4 Point estimation under the calibration approach; 6.5 Calibration estimators for domains; 6.6 Comments on the calibration approach 6.7 Alternative sets of calibrated weights6.8 Properties of the calibrated weights; Chapter 7 Examples of Calibration Estimators; 7.1 Examples of familiar estimators for data with nonresponse; 7.2 The simplest auxiliary vector; 7.3 One-way classi.cation; 7.4 A single quantitative auxiliary variable; 7.5 One-way classi.cation combined with a quantitative variable; 7.6 Two-way classi.cation; 7.7 A Monte Carlo simulation study; Chapter 8 The Combined Use of Sample Information and Population Information; 8.1 Options for the combined use of information 8.2 An example of calibration with information at both levels8.3 A Monte Carlo simulation study of alternative calibration procedures; 8.4 Two-step procedures in practice; Chapter 9 Analysing the Bias due to Nonresponse; 9.1 Simple estimators and their nonresponse bias; 9.2 Finding an ef.cient grouping; 9.3 Further illustrations of the nonresponse; 9.4 A general expression for the bias of the calibration estimator; 9.5 Conditions for near-unbiasedness; 9.6 A review of concepts, terms and ideas; Appendix: Proof of Proposition 9.1; Chapter 10 Selecting the Most Relevant Auxiliary Information 10.1 Discussion10.2 Guidelines for the construction of an auxiliary vector; 10.3 The prospects for near-zero bias with traditional estimators; 10.4 Further avenues towards a zero bias; 10.5 A further tool for reducing the bias; 10.6 The search for a powerful auxiliary vector; 10.7 Empirical illustrations of the indicators; 10.8 Literature review; Chapter 11 Variance and Variance Estimation; 11.1 Variance estimation for the calibration estimator; 11.2 An estimator for ideal conditions; 11.3 A useful relationship; 11.4 Variance estimation for the two-step A and two-step B procedures 11.5 A simulation study of the variance estimation technique |
| Record Nr. | UNINA-9910143689003321 |
|
Särndal Carl-Erik <1937->
|
||
| Hoboken, NJ, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström
| Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström |
| Autore | Särndal Carl-Erik <1937-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (214 p.) |
| Disciplina |
001.433
519.5/44 519.544 |
| Altri autori (Persone) | LundströmSixten |
| Collana | Wiley Series in Survey Methodology |
| Soggetto topico |
Estimation theory
Sampling (Statistics) Nonresponse (Statistics) |
| ISBN |
1-280-27623-1
9786610276233 0-470-01135-1 0-470-01134-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Estimation in Surveys with Nonresponse; Contents; Preface; Chapter 1 Introduction; Chapter 2 The Survey and Its Imperfections; 2.1 The survey objective; 2.2 Sources of error in a survey; Chapter 3 General Principles to Assist Estimation; 3.1 Introduction; 3.2 The importance of auxiliary information; 3.3 Desirable features of an auxiliary vector; Chapter 4 The Use of Auxiliary Information under Ideal Conditions; 4.1 Introduction; 4.2 The Horvitz-Thompson estimator; 4.3 The generalized regression estimator; 4.4 Variance and variance estimation
4.5 Examples of the generalized regression estimatorChapter 5 Introduction to Estimation in the Presence of Nonresponse; 5.1 General background; 5.2 Errors caused by sampling and nonresponse; Appendix: Variance and mean squared error under nonresponse; Chapter 6 Weighting of Data in the Presence of Nonresponse; 6.1 Traditional approaches to weighting; 6.2 Auxiliary vectors and auxiliary information; 6.3 The calibration approach: some terminology; 6.4 Point estimation under the calibration approach; 6.5 Calibration estimators for domains; 6.6 Comments on the calibration approach 6.7 Alternative sets of calibrated weights6.8 Properties of the calibrated weights; Chapter 7 Examples of Calibration Estimators; 7.1 Examples of familiar estimators for data with nonresponse; 7.2 The simplest auxiliary vector; 7.3 One-way classi.cation; 7.4 A single quantitative auxiliary variable; 7.5 One-way classi.cation combined with a quantitative variable; 7.6 Two-way classi.cation; 7.7 A Monte Carlo simulation study; Chapter 8 The Combined Use of Sample Information and Population Information; 8.1 Options for the combined use of information 8.2 An example of calibration with information at both levels8.3 A Monte Carlo simulation study of alternative calibration procedures; 8.4 Two-step procedures in practice; Chapter 9 Analysing the Bias due to Nonresponse; 9.1 Simple estimators and their nonresponse bias; 9.2 Finding an ef.cient grouping; 9.3 Further illustrations of the nonresponse; 9.4 A general expression for the bias of the calibration estimator; 9.5 Conditions for near-unbiasedness; 9.6 A review of concepts, terms and ideas; Appendix: Proof of Proposition 9.1; Chapter 10 Selecting the Most Relevant Auxiliary Information 10.1 Discussion10.2 Guidelines for the construction of an auxiliary vector; 10.3 The prospects for near-zero bias with traditional estimators; 10.4 Further avenues towards a zero bias; 10.5 A further tool for reducing the bias; 10.6 The search for a powerful auxiliary vector; 10.7 Empirical illustrations of the indicators; 10.8 Literature review; Chapter 11 Variance and Variance Estimation; 11.1 Variance estimation for the calibration estimator; 11.2 An estimator for ideal conditions; 11.3 A useful relationship; 11.4 Variance estimation for the two-step A and two-step B procedures 11.5 A simulation study of the variance estimation technique |
| Record Nr. | UNINA-9910830681903321 |
|
Särndal Carl-Erik <1937->
|
||
| Hoboken, NJ, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström
| Estimation in surveys with nonresponse [[electronic resource] /] / Carl-Erik Särndal, Sixten Lundström |
| Autore | Särndal Carl-Erik <1937-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (214 p.) |
| Disciplina |
001.433
519.5/44 519.544 |
| Altri autori (Persone) | LundströmSixten |
| Collana | Wiley Series in Survey Methodology |
| Soggetto topico |
Estimation theory
Sampling (Statistics) Nonresponse (Statistics) |
| ISBN |
1-280-27623-1
9786610276233 0-470-01135-1 0-470-01134-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Estimation in Surveys with Nonresponse; Contents; Preface; Chapter 1 Introduction; Chapter 2 The Survey and Its Imperfections; 2.1 The survey objective; 2.2 Sources of error in a survey; Chapter 3 General Principles to Assist Estimation; 3.1 Introduction; 3.2 The importance of auxiliary information; 3.3 Desirable features of an auxiliary vector; Chapter 4 The Use of Auxiliary Information under Ideal Conditions; 4.1 Introduction; 4.2 The Horvitz-Thompson estimator; 4.3 The generalized regression estimator; 4.4 Variance and variance estimation
4.5 Examples of the generalized regression estimatorChapter 5 Introduction to Estimation in the Presence of Nonresponse; 5.1 General background; 5.2 Errors caused by sampling and nonresponse; Appendix: Variance and mean squared error under nonresponse; Chapter 6 Weighting of Data in the Presence of Nonresponse; 6.1 Traditional approaches to weighting; 6.2 Auxiliary vectors and auxiliary information; 6.3 The calibration approach: some terminology; 6.4 Point estimation under the calibration approach; 6.5 Calibration estimators for domains; 6.6 Comments on the calibration approach 6.7 Alternative sets of calibrated weights6.8 Properties of the calibrated weights; Chapter 7 Examples of Calibration Estimators; 7.1 Examples of familiar estimators for data with nonresponse; 7.2 The simplest auxiliary vector; 7.3 One-way classi.cation; 7.4 A single quantitative auxiliary variable; 7.5 One-way classi.cation combined with a quantitative variable; 7.6 Two-way classi.cation; 7.7 A Monte Carlo simulation study; Chapter 8 The Combined Use of Sample Information and Population Information; 8.1 Options for the combined use of information 8.2 An example of calibration with information at both levels8.3 A Monte Carlo simulation study of alternative calibration procedures; 8.4 Two-step procedures in practice; Chapter 9 Analysing the Bias due to Nonresponse; 9.1 Simple estimators and their nonresponse bias; 9.2 Finding an ef.cient grouping; 9.3 Further illustrations of the nonresponse; 9.4 A general expression for the bias of the calibration estimator; 9.5 Conditions for near-unbiasedness; 9.6 A review of concepts, terms and ideas; Appendix: Proof of Proposition 9.1; Chapter 10 Selecting the Most Relevant Auxiliary Information 10.1 Discussion10.2 Guidelines for the construction of an auxiliary vector; 10.3 The prospects for near-zero bias with traditional estimators; 10.4 Further avenues towards a zero bias; 10.5 A further tool for reducing the bias; 10.6 The search for a powerful auxiliary vector; 10.7 Empirical illustrations of the indicators; 10.8 Literature review; Chapter 11 Variance and Variance Estimation; 11.1 Variance estimation for the calibration estimator; 11.2 An estimator for ideal conditions; 11.3 A useful relationship; 11.4 Variance estimation for the two-step A and two-step B procedures 11.5 A simulation study of the variance estimation technique |
| Record Nr. | UNINA-9910841066103321 |
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Särndal Carl-Erik <1937->
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| Hoboken, NJ, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Function estimates : proceedings of a conference held July 28-August 3, 1985 / / J.S. Marron, editor
| Function estimates : proceedings of a conference held July 28-August 3, 1985 / / J.S. Marron, editor |
| Pubbl/distr/stampa | Providence, Rhode Island : , : American Mathematical Society, , [1986] |
| Descrizione fisica | 1 online resource (189 p.) |
| Disciplina | 519.5/44 |
| Collana | Contemporary mathematics / American Mathematical Society |
| Soggetto topico | Estimation theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-8218-7649-X
0-8218-5062-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Contents""; ""Preface""; ""Logspline density estimation""; ""Statistical encounters with B-splines""; ""Estimation of a transfer function in a nongaussian context""; ""Evaluating the performance of an inversion algorithm""; ""Harmonic splines in geomagnetism""; ""Problems in estimating the anomalous gravity potential of the earth from discrete data""; ""What regression model should be chosen when the statistician misspecifies the error distribution?""; ""Approximation theory of method of regularization estimators: applications""
""Partial spline modelling of the tropopause and other discontinuities""""Choice of smoothing parameter in deconvolution problems""; ""Regression approximation using projections and isotropic kernels""; ""Will the art of smoothing ever become a science?"" |
| Record Nr. | UNINA-9910480001003321 |
| Providence, Rhode Island : , : American Mathematical Society, , [1986] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Function estimates : proceedings of a conference held July 28-August 3, 1985 / / J.S. Marron, editor
| Function estimates : proceedings of a conference held July 28-August 3, 1985 / / J.S. Marron, editor |
| Pubbl/distr/stampa | Providence, Rhode Island : , : American Mathematical Society, , [1986] |
| Descrizione fisica | 1 online resource (189 p.) |
| Disciplina | 519.5/44 |
| Collana | Contemporary mathematics |
| Soggetto topico | Estimation theory |
| ISBN |
0-8218-7649-X
0-8218-5062-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contents -- Preface -- Logspline density estimation -- Statistical encounters with B-splines -- Estimation of a transfer function in a non-gaussian context -- Evaluating the performance of an inversion algorithm -- Harmonic splines in geomagnetism -- Problems in estimating the anomalous gravity potential of the earth from discrete data -- What regression model should be chosen when the statistician misspecifies the error distribution? -- Approximation theory of method of regularization estimators: applications -- Partial spline modelling of the tropopause and other discontinuities -- Choice of smoothing parameter in deconvolution problems -- Regression approximation using projections and isotropic kernels -- Will the art of smoothing ever become a science. |
| Record Nr. | UNINA-9910788785303321 |
| Providence, Rhode Island : , : American Mathematical Society, , [1986] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
