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Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910144711403321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910829865903321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910840665103321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
Schreuder Hans T.
|
||
| Fort Collins, CO : , : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, , 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deterministic observation theory and applications / / Jean-Paul Gauthier, Ivan Kupka [[electronic resource]]
| Deterministic observation theory and applications / / Jean-Paul Gauthier, Ivan Kupka [[electronic resource]] |
| Autore | Gauthier Jean-Paul |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2001 |
| Descrizione fisica | 1 online resource (x, 226 pages) : digital, PDF file(s) |
| Disciplina | 003 |
| Soggetto topico |
Observers (Control theory)
Missing observations (Statistics) |
| ISBN |
1-107-12389-5
0-521-18386-3 0-511-17475-6 0-511-15477-1 1-280-43347-7 0-511-54664-5 9786610433476 0-511-30238-X 0-511-04405-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Systems under Consideration -- What Is Observability? -- The New Observability Theory Versus the Old Ones -- Observability and Observers -- Observability Concepts -- Infinitesimal and Uniform Infinitesimal Observability -- The Canonical Flag of Distributions -- The Phase-Variable Representation -- Differential Observability and Strong Differential Observability -- The Trivial Foliation -- Appendix: Weak Controllability -- The Case d[subscript y] [less than or equal] d[subscript u] -- Relation Between Observability and Infinitesimal Observability -- Normal Form for a Uniform Canonical Flag -- Characterization of Uniform Infinitesimal Observability -- Complements -- Proof of Theorem 3.2 -- The Case d[subscript y]] d[subscript u] -- Definitions and Notations -- Statement of Our Differential Observability Results -- Proof of the Observability Theorems -- Equivalence between Observability and Observability for Smooth Inputs -- The Approximation Theorem -- Complements -- Singular State-Output Mappings -- Assumptions and Definitions -- The Ascending Chain Property -- The Key Lemma -- The ACP(N) in the Controlled Case -- Globalization -- The Controllable Case -- Observers: The High-Gain Construction -- Definition of Observer Systems and Comments -- The High-Gain Construction -- Dynamic Output Stabilization and Applications -- Dynamic Output Stabilization -- The Case of a Uniform Canonical Flag -- The General Case of a Phase-Variable Representation -- Complements -- Applications -- Binary Distillation Columns -- Polymerization Reactors. |
| Altri titoli varianti | Deterministic Observation Theory & Applications |
| Record Nr. | UNINA-9910455317803321 |
|
Gauthier Jean-Paul
|
||
| Cambridge : , : Cambridge University Press, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deterministic observation theory and applications / / Jean-Paul Gauthier, Ivan Kupka [[electronic resource]]
| Deterministic observation theory and applications / / Jean-Paul Gauthier, Ivan Kupka [[electronic resource]] |
| Autore | Gauthier Jean-Paul |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2001 |
| Descrizione fisica | 1 online resource (x, 226 pages) : digital, PDF file(s) |
| Disciplina | 003 |
| Soggetto topico |
Observers (Control theory)
Missing observations (Statistics) |
| ISBN |
1-107-12389-5
0-521-18386-3 0-511-17475-6 0-511-15477-1 1-280-43347-7 0-511-54664-5 9786610433476 0-511-30238-X 0-511-04405-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Systems under Consideration -- What Is Observability? -- The New Observability Theory Versus the Old Ones -- Observability and Observers -- Observability Concepts -- Infinitesimal and Uniform Infinitesimal Observability -- The Canonical Flag of Distributions -- The Phase-Variable Representation -- Differential Observability and Strong Differential Observability -- The Trivial Foliation -- Appendix: Weak Controllability -- The Case d[subscript y] [less than or equal] d[subscript u] -- Relation Between Observability and Infinitesimal Observability -- Normal Form for a Uniform Canonical Flag -- Characterization of Uniform Infinitesimal Observability -- Complements -- Proof of Theorem 3.2 -- The Case d[subscript y]] d[subscript u] -- Definitions and Notations -- Statement of Our Differential Observability Results -- Proof of the Observability Theorems -- Equivalence between Observability and Observability for Smooth Inputs -- The Approximation Theorem -- Complements -- Singular State-Output Mappings -- Assumptions and Definitions -- The Ascending Chain Property -- The Key Lemma -- The ACP(N) in the Controlled Case -- Globalization -- The Controllable Case -- Observers: The High-Gain Construction -- Definition of Observer Systems and Comments -- The High-Gain Construction -- Dynamic Output Stabilization and Applications -- Dynamic Output Stabilization -- The Case of a Uniform Canonical Flag -- The General Case of a Phase-Variable Representation -- Complements -- Applications -- Binary Distillation Columns -- Polymerization Reactors. |
| Altri titoli varianti | Deterministic Observation Theory & Applications |
| Record Nr. | UNINA-9910779924903321 |
|
Gauthier Jean-Paul
|
||
| Cambridge : , : Cambridge University Press, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deterministic observation theory and applications / / Jean-Paul Gauthier, Ivan Kupka [[electronic resource]]
| Deterministic observation theory and applications / / Jean-Paul Gauthier, Ivan Kupka [[electronic resource]] |
| Autore | Gauthier Jean-Paul |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2001 |
| Descrizione fisica | 1 online resource (x, 226 pages) : digital, PDF file(s) |
| Disciplina | 003 |
| Soggetto topico |
Observers (Control theory)
Missing observations (Statistics) |
| ISBN |
1-107-12389-5
0-521-18386-3 0-511-17475-6 0-511-15477-1 1-280-43347-7 0-511-54664-5 9786610433476 0-511-30238-X 0-511-04405-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Systems under Consideration -- What Is Observability? -- The New Observability Theory Versus the Old Ones -- Observability and Observers -- Observability Concepts -- Infinitesimal and Uniform Infinitesimal Observability -- The Canonical Flag of Distributions -- The Phase-Variable Representation -- Differential Observability and Strong Differential Observability -- The Trivial Foliation -- Appendix: Weak Controllability -- The Case d[subscript y] [less than or equal] d[subscript u] -- Relation Between Observability and Infinitesimal Observability -- Normal Form for a Uniform Canonical Flag -- Characterization of Uniform Infinitesimal Observability -- Complements -- Proof of Theorem 3.2 -- The Case d[subscript y]] d[subscript u] -- Definitions and Notations -- Statement of Our Differential Observability Results -- Proof of the Observability Theorems -- Equivalence between Observability and Observability for Smooth Inputs -- The Approximation Theorem -- Complements -- Singular State-Output Mappings -- Assumptions and Definitions -- The Ascending Chain Property -- The Key Lemma -- The ACP(N) in the Controlled Case -- Globalization -- The Controllable Case -- Observers: The High-Gain Construction -- Definition of Observer Systems and Comments -- The High-Gain Construction -- Dynamic Output Stabilization and Applications -- Dynamic Output Stabilization -- The Case of a Uniform Canonical Flag -- The General Case of a Phase-Variable Representation -- Complements -- Applications -- Binary Distillation Columns -- Polymerization Reactors. |
| Altri titoli varianti | Deterministic Observation Theory & Applications |
| Record Nr. | UNINA-9910814121503321 |
|
Gauthier Jean-Paul
|
||
| Cambridge : , : Cambridge University Press, , 2001 | ||
| 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 |
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McLachlan Geoffrey J. <1946->
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| Hoboken, N.J., : Wiley-Interscience, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 | ||
| ||
