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Agent-based modelling of socio-technical systems / / Koen H. Dam, Igor Nikolic, Zofia Lukszo, editors
| Agent-based modelling of socio-technical systems / / Koen H. Dam, Igor Nikolic, Zofia Lukszo, editors |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Dordrecht, : Springer, 2013 |
| Descrizione fisica | 1 online resource (284 p.) |
| Disciplina | 001.4/22 |
| Altri autori (Persone) |
DamKoen H
NikolicIgor LukszoZofia |
| Collana | Agent-based social systems |
| Soggetto topico |
Social systems - Computer simulation
Technology - Social aspects |
| ISBN |
1-283-69809-9
94-007-4933-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. I. Theory and practice -- pt. II. Case studies. |
| Record Nr. | UNINA-9910437593503321 |
| Dordrecht, : Springer, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of survey data [[electronic resource] /] / edited by R.L. Chambers and C.J. Skinner
| Analysis of survey data [[electronic resource] /] / edited by R.L. Chambers and C.J. Skinner |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 |
| Descrizione fisica | 1 online resource (399 p.) |
| Disciplina |
001.4/22
001.433 |
| Altri autori (Persone) |
ChambersR. L (Ray L.)
SkinnerC. J |
| Collana | Wiley series in survey methodology |
| Soggetto topico | Mathematical statistics - Methodology |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-27189-2
9786610271894 0-470-32742-1 0-470-86439-7 0-470-86720-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Survey Data; Contents; Preface; List of Contributors; Chapter 1 Introduction; 1.1. The analysis of survey data; 1.2. Framework, terminology and specification of parameters; 1.3. Statistical inference; 1.4. Relation to Skinner, Holt and Smith (1989); 1.5. Outline of this book; PART A APPROACHES TO INFERENCE; Chapter 2 Introduction to Part A; 2.1. Introduction; 2.2. Full information likelihood; 2.3. Sample likelihood; 2.4. Pseudo-likelihood; 2.5. Pseudo-likelihood applied to analytic inference; 2.6. Bayesian inference for sample surveys
2.7. Application of the likelihood principle in descriptive inferenceChapter 3 Design-based and Model-based Methods for Estimating Model Parameters; 3.1. Choice of methods; 3.2. Design-based and model-based linear estimators; 3.2.1. Parameters of interest; 3.2.2. Linear estimators; 3.2.3. Properties of b and b; 3.3. Design-based and total variances of linear estimators; 3.3.1. Design-based and total variance of b; 3.3.2. Design-based mean squared error of b and its model expectation; 3.4. More complex estimators; 3.4.1. Taylor linearisation of non-linear statistics; 3.4.2. Ratio estimation 3.4.3. Non-linear statistics - explicitly defined statistics3.4.4. Non-linear statistics - defined implicitly by score statistics; 3.4.5. Total variance matrix of b for non-negligible sampling fractions; 3.5. Conditional model-based properties; 3.5.1. Conditional model-based properties of b; 3.5.2. Conditional model-based expectations; 3.5.3. Conditional model-based variance for b and the use of estimating functions; 3.6. Properties of methods when the assumed model is invalid; 3.6.1. Critical model assumptions; 3.6.2. Model-based properties of b; 3.6.3. Model-based properties of b 3.6.4. Summary3.7. Conclusion; Chapter 4 The Bayesian Approach to Sample Survey Inference; 4.1. Introduction; 4.2. Modeling the selection mechanism; Chapter 5 Interpreting a Sample as Evidence about a Finite Population; 5.1. Introduction; 5.2. The evidence in a sample from a finite population; 5.2.1. Evidence about a probability; 5.2.2. Evidence about a population proportion; 5.2.3. The likelihood function for a population proportion or total; 5.2.4. The probability of misleading evidence; 5.2.5. Evidence about the average count in a finite population 5.2.6. Evidence about a population mean under a regression model5.3. Defining the likelihood function for a finite population; PART B CATEGORICAL RESPONSE DATA; Chapter 6 Introduction to Part B; 6.1. Introduction; 6.2. Analysis of tabular data; 6.2.1. One-way classification; 6.2.2. Multi-way classifications and log-linear models; 6.2.3. Logistic models for domain proportions; 6.3. Analysis of unit-level data; 6.3.1. Logistic regression; 6.3.2. Some issues in weighting; Chapter 7 Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update; 7.1. Introduction 7.2. Fitting and testing log-linear models |
| Record Nr. | UNINA-9910143508403321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of survey data [[electronic resource] /] / edited by R.L. Chambers and C.J. Skinner
| Analysis of survey data [[electronic resource] /] / edited by R.L. Chambers and C.J. Skinner |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 |
| Descrizione fisica | 1 online resource (399 p.) |
| Disciplina |
001.4/22
001.433 |
| Altri autori (Persone) |
ChambersR. L (Ray L.)
SkinnerC. J |
| Collana | Wiley series in survey methodology |
| Soggetto topico | Mathematical statistics - Methodology |
| ISBN |
1-280-27189-2
9786610271894 0-470-32742-1 0-470-86439-7 0-470-86720-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Survey Data; Contents; Preface; List of Contributors; Chapter 1 Introduction; 1.1. The analysis of survey data; 1.2. Framework, terminology and specification of parameters; 1.3. Statistical inference; 1.4. Relation to Skinner, Holt and Smith (1989); 1.5. Outline of this book; PART A APPROACHES TO INFERENCE; Chapter 2 Introduction to Part A; 2.1. Introduction; 2.2. Full information likelihood; 2.3. Sample likelihood; 2.4. Pseudo-likelihood; 2.5. Pseudo-likelihood applied to analytic inference; 2.6. Bayesian inference for sample surveys
2.7. Application of the likelihood principle in descriptive inferenceChapter 3 Design-based and Model-based Methods for Estimating Model Parameters; 3.1. Choice of methods; 3.2. Design-based and model-based linear estimators; 3.2.1. Parameters of interest; 3.2.2. Linear estimators; 3.2.3. Properties of b and b; 3.3. Design-based and total variances of linear estimators; 3.3.1. Design-based and total variance of b; 3.3.2. Design-based mean squared error of b and its model expectation; 3.4. More complex estimators; 3.4.1. Taylor linearisation of non-linear statistics; 3.4.2. Ratio estimation 3.4.3. Non-linear statistics - explicitly defined statistics3.4.4. Non-linear statistics - defined implicitly by score statistics; 3.4.5. Total variance matrix of b for non-negligible sampling fractions; 3.5. Conditional model-based properties; 3.5.1. Conditional model-based properties of b; 3.5.2. Conditional model-based expectations; 3.5.3. Conditional model-based variance for b and the use of estimating functions; 3.6. Properties of methods when the assumed model is invalid; 3.6.1. Critical model assumptions; 3.6.2. Model-based properties of b; 3.6.3. Model-based properties of b 3.6.4. Summary3.7. Conclusion; Chapter 4 The Bayesian Approach to Sample Survey Inference; 4.1. Introduction; 4.2. Modeling the selection mechanism; Chapter 5 Interpreting a Sample as Evidence about a Finite Population; 5.1. Introduction; 5.2. The evidence in a sample from a finite population; 5.2.1. Evidence about a probability; 5.2.2. Evidence about a population proportion; 5.2.3. The likelihood function for a population proportion or total; 5.2.4. The probability of misleading evidence; 5.2.5. Evidence about the average count in a finite population 5.2.6. Evidence about a population mean under a regression model5.3. Defining the likelihood function for a finite population; PART B CATEGORICAL RESPONSE DATA; Chapter 6 Introduction to Part B; 6.1. Introduction; 6.2. Analysis of tabular data; 6.2.1. One-way classification; 6.2.2. Multi-way classifications and log-linear models; 6.2.3. Logistic models for domain proportions; 6.3. Analysis of unit-level data; 6.3.1. Logistic regression; 6.3.2. Some issues in weighting; Chapter 7 Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update; 7.1. Introduction 7.2. Fitting and testing log-linear models |
| Record Nr. | UNINA-9910830716603321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of survey data / / edited by R.L. Chambers and C.J. Skinner
| Analysis of survey data / / edited by R.L. Chambers and C.J. Skinner |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 |
| Descrizione fisica | 1 online resource (399 p.) |
| Disciplina | 001.4/22 |
| Altri autori (Persone) |
ChambersR. L (Ray L.)
SkinnerC. J |
| Collana | Wiley series in survey methodology |
| Soggetto topico | Mathematical statistics - Methodology |
| ISBN |
1-280-27189-2
9786610271894 0-470-32742-1 0-470-86439-7 0-470-86720-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Survey Data; Contents; Preface; List of Contributors; Chapter 1 Introduction; 1.1. The analysis of survey data; 1.2. Framework, terminology and specification of parameters; 1.3. Statistical inference; 1.4. Relation to Skinner, Holt and Smith (1989); 1.5. Outline of this book; PART A APPROACHES TO INFERENCE; Chapter 2 Introduction to Part A; 2.1. Introduction; 2.2. Full information likelihood; 2.3. Sample likelihood; 2.4. Pseudo-likelihood; 2.5. Pseudo-likelihood applied to analytic inference; 2.6. Bayesian inference for sample surveys
2.7. Application of the likelihood principle in descriptive inferenceChapter 3 Design-based and Model-based Methods for Estimating Model Parameters; 3.1. Choice of methods; 3.2. Design-based and model-based linear estimators; 3.2.1. Parameters of interest; 3.2.2. Linear estimators; 3.2.3. Properties of b and b; 3.3. Design-based and total variances of linear estimators; 3.3.1. Design-based and total variance of b; 3.3.2. Design-based mean squared error of b and its model expectation; 3.4. More complex estimators; 3.4.1. Taylor linearisation of non-linear statistics; 3.4.2. Ratio estimation 3.4.3. Non-linear statistics - explicitly defined statistics3.4.4. Non-linear statistics - defined implicitly by score statistics; 3.4.5. Total variance matrix of b for non-negligible sampling fractions; 3.5. Conditional model-based properties; 3.5.1. Conditional model-based properties of b; 3.5.2. Conditional model-based expectations; 3.5.3. Conditional model-based variance for b and the use of estimating functions; 3.6. Properties of methods when the assumed model is invalid; 3.6.1. Critical model assumptions; 3.6.2. Model-based properties of b; 3.6.3. Model-based properties of b 3.6.4. Summary3.7. Conclusion; Chapter 4 The Bayesian Approach to Sample Survey Inference; 4.1. Introduction; 4.2. Modeling the selection mechanism; Chapter 5 Interpreting a Sample as Evidence about a Finite Population; 5.1. Introduction; 5.2. The evidence in a sample from a finite population; 5.2.1. Evidence about a probability; 5.2.2. Evidence about a population proportion; 5.2.3. The likelihood function for a population proportion or total; 5.2.4. The probability of misleading evidence; 5.2.5. Evidence about the average count in a finite population 5.2.6. Evidence about a population mean under a regression model5.3. Defining the likelihood function for a finite population; PART B CATEGORICAL RESPONSE DATA; Chapter 6 Introduction to Part B; 6.1. Introduction; 6.2. Analysis of tabular data; 6.2.1. One-way classification; 6.2.2. Multi-way classifications and log-linear models; 6.2.3. Logistic models for domain proportions; 6.3. Analysis of unit-level data; 6.3.1. Logistic regression; 6.3.2. Some issues in weighting; Chapter 7 Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update; 7.1. Introduction 7.2. Fitting and testing log-linear models |
| Record Nr. | UNINA-9910877666803321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Beyond basic statistics : tips, tricks, and techniques every data analyst should know / / Kristin H. Jarman
| Beyond basic statistics : tips, tricks, and techniques every data analyst should know / / Kristin H. Jarman |
| Autore | Jarman Kristin H. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (203 p.) |
| Disciplina | 001.4/22 |
| Soggetto topico | Mathematical statistics |
| ISBN |
1-118-85612-0
1-118-85617-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Copyright Page; Contents; Preface; Chapter 1 Introduction: It Seemed Like the Right Thing To Do at the Time; WHEN GOOD STATISTICS GO BAD: COMMON MISTAKES AND THE IMPACT THEY HAVE; STATISTICS 101: CONCEPTS YOU SHOULD KNOW BEFORE READING THIS BOOK; Probability Theory; Statistics; TIPS, TRICKS, AND TECHNIQUES: A ROAD MAP OF WHAT FOLLOWS; BIBLIOGRAPHY; Chapter 2 The Type A Diet: Sampling Strategies to Eliminate Confounding and Reduce Your Waistline; THE BASICS OF PLANNING A STUDY; MY STATISTICAL ANALYSIS IS BRILLIANT. WHY ARE MY CONCLUSIONS SO WRONG?; Answering the Wrong Question
Putting Too Much Confidence in Convenience Data Confusing Association and Causation; REPLICATION, RANDOMIZATION, AND BLOCKING: THE BUILDING BLOCKS A GOOD STUDY; EXPLORATORY RESEARCH: GETTING YOUR STUDIES INTO FOCUS; DESCRIPTIVE AND EXPLANATORY RESEARCH: ANSWERING THE TARGETED QUESTIONS; Controlled Experiments: The Art of Manipulation; Observational Studies: Scientifically Approved Voyeurism; SO MANY STRATEGIES, SO LITTLE TIME; BIBLIOGRAPHY; Chapter 3 Conservatives, Liberals, and Other Political Pawns: How to Gain Power and Influence with Sample Size Calculations STEP 1. KEEP YOUR FINGER ON THE PULSE OF THE POPULACE STEP 2. AVOID AMBIGUOUS RESULTS AND OTHER POLITICAL POTHOLES; Identify the Data Analysis Technique You Will Be Performing; Know the Difference Between Practical Significance and Statistical Significance; Note Your Practical Limitations; STEP 3. LET SAMPLE-SIZE CALCULATIONS BE YOUR RIGHT-HAND MAN; Population Means and Probabilities: Sample-Size Calculations for a Confidence Interval; Power and Sample-Size Calculations for Hypothesis Tests; STEP 4. KEEP YOUR FRIENDS CLOSE AND YOUR ENEMIES CLOSER; BIBLIOGRAPHY Chapter 4 Bunco, Bricks, and Marked Cards: Chi-Squared Tests and How to Beat a Cheater WHAT HAPPENS IN VEGAS ; HOW STATISTICIANS REMAIN DISCRETE; CONTINGENCY TABLES, CHI-SQUARED TESTS, AND OTHER WINNING STRATEGIES FOR DISCRETE DATA ANALYSIS; Turning Lemons into Gold Bars: How to Convert Qualitative Data into Quantitative Random Variables; Plots and Tables: The Poor Man's Statistical Analysis; Contingency Tables: How to Break Down a Frequency Distribution and Expose Your Variables; The Chi-Squared Test: An All-You-Can-Eat Buffet for Discrete Data Analysis; HOW TO BEAT A CHEATER; BIBLIOGRAPHY Chapter 5 WHY IT PAYS TO BE A STABLE MASTER: SUMO WRESTLERS AND OTHER ROBUST STATISTICS DESCRIPTIVE STATISTICS: A REVIEW FOR THE JONOKUCHI; Three Things You Should Know About the Sample Mean; Three Things You Should Know about the Standard Deviation; THE JAPANESE SUMO INVASION: WHY IT PAYS TO BE ROBUST; Summarizing a Sample with Percentiles; Robust Center Location; Robust and Resistant Variation; Robust Confidence Intervals; WHEN ROBUST DOES IT BETTER; HARVESTING THE AMERICAN DREAM; BIBLIOGRAPHY Chapter 6 Five-Hour Marriages: Continuous Distributions, Tests for Normality, and Juicy Hollywood Scandals |
| Record Nr. | UNINA-9910140485803321 |
Jarman Kristin H.
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Beyond basic statistics : tips, tricks, and techniques every data analyst should know / / Kristin H. Jarman
| Beyond basic statistics : tips, tricks, and techniques every data analyst should know / / Kristin H. Jarman |
| Autore | Jarman Kristin H. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (203 p.) |
| Disciplina | 001.4/22 |
| Soggetto topico | Mathematical statistics |
| ISBN |
1-118-85612-0
1-118-85617-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Copyright Page; Contents; Preface; Chapter 1 Introduction: It Seemed Like the Right Thing To Do at the Time; WHEN GOOD STATISTICS GO BAD: COMMON MISTAKES AND THE IMPACT THEY HAVE; STATISTICS 101: CONCEPTS YOU SHOULD KNOW BEFORE READING THIS BOOK; Probability Theory; Statistics; TIPS, TRICKS, AND TECHNIQUES: A ROAD MAP OF WHAT FOLLOWS; BIBLIOGRAPHY; Chapter 2 The Type A Diet: Sampling Strategies to Eliminate Confounding and Reduce Your Waistline; THE BASICS OF PLANNING A STUDY; MY STATISTICAL ANALYSIS IS BRILLIANT. WHY ARE MY CONCLUSIONS SO WRONG?; Answering the Wrong Question
Putting Too Much Confidence in Convenience Data Confusing Association and Causation; REPLICATION, RANDOMIZATION, AND BLOCKING: THE BUILDING BLOCKS A GOOD STUDY; EXPLORATORY RESEARCH: GETTING YOUR STUDIES INTO FOCUS; DESCRIPTIVE AND EXPLANATORY RESEARCH: ANSWERING THE TARGETED QUESTIONS; Controlled Experiments: The Art of Manipulation; Observational Studies: Scientifically Approved Voyeurism; SO MANY STRATEGIES, SO LITTLE TIME; BIBLIOGRAPHY; Chapter 3 Conservatives, Liberals, and Other Political Pawns: How to Gain Power and Influence with Sample Size Calculations STEP 1. KEEP YOUR FINGER ON THE PULSE OF THE POPULACE STEP 2. AVOID AMBIGUOUS RESULTS AND OTHER POLITICAL POTHOLES; Identify the Data Analysis Technique You Will Be Performing; Know the Difference Between Practical Significance and Statistical Significance; Note Your Practical Limitations; STEP 3. LET SAMPLE-SIZE CALCULATIONS BE YOUR RIGHT-HAND MAN; Population Means and Probabilities: Sample-Size Calculations for a Confidence Interval; Power and Sample-Size Calculations for Hypothesis Tests; STEP 4. KEEP YOUR FRIENDS CLOSE AND YOUR ENEMIES CLOSER; BIBLIOGRAPHY Chapter 4 Bunco, Bricks, and Marked Cards: Chi-Squared Tests and How to Beat a Cheater WHAT HAPPENS IN VEGAS ; HOW STATISTICIANS REMAIN DISCRETE; CONTINGENCY TABLES, CHI-SQUARED TESTS, AND OTHER WINNING STRATEGIES FOR DISCRETE DATA ANALYSIS; Turning Lemons into Gold Bars: How to Convert Qualitative Data into Quantitative Random Variables; Plots and Tables: The Poor Man's Statistical Analysis; Contingency Tables: How to Break Down a Frequency Distribution and Expose Your Variables; The Chi-Squared Test: An All-You-Can-Eat Buffet for Discrete Data Analysis; HOW TO BEAT A CHEATER; BIBLIOGRAPHY Chapter 5 WHY IT PAYS TO BE A STABLE MASTER: SUMO WRESTLERS AND OTHER ROBUST STATISTICS DESCRIPTIVE STATISTICS: A REVIEW FOR THE JONOKUCHI; Three Things You Should Know About the Sample Mean; Three Things You Should Know about the Standard Deviation; THE JAPANESE SUMO INVASION: WHY IT PAYS TO BE ROBUST; Summarizing a Sample with Percentiles; Robust Center Location; Robust and Resistant Variation; Robust Confidence Intervals; WHEN ROBUST DOES IT BETTER; HARVESTING THE AMERICAN DREAM; BIBLIOGRAPHY Chapter 6 Five-Hour Marriages: Continuous Distributions, Tests for Normality, and Juicy Hollywood Scandals |
| Record Nr. | UNINA-9910818323203321 |
|
Jarman Kristin H.
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Explanation in causal inference : methods for mediation and interaction / / Tyler J. VanderWeele
| Explanation in causal inference : methods for mediation and interaction / / Tyler J. VanderWeele |
| Autore | VanderWeele Tyler |
| Pubbl/distr/stampa | New York : , : Oxford University Press, , 2015 |
| Descrizione fisica | 1 online resource (729 p.) |
| Disciplina | 001.4/22 |
| Soggetto topico |
Social sciences - Research
Social sciences - Methodology Causation |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-19-932588-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Explanation in Causal Inference; Copyright; Dedication; Contents; Preface; Part 1 Mediation Analysis; 1 Explanation and Mechanism; 1.1 Causal Inference and Explanation; 1.2 Forms of Explanation and Types of Mechanisms; 1.3 Motivations for Assessing Mediation, Interaction, and Interference; 1.4 Organization of this Book; 2 Mediation: Introduction and Regression-Based Approaches; 2.1 Classic Regression Approach to Mediation Analysis; 2.2 Counterfactual Approach to Mediation Analysis: Continuous Outcomes; 2.3 Assumptions about Confounding; 2.4 Binary and Count Outcomes
2.5 Binary Mediators2.6 Comparison of Approaches: Product-of-Coefficient and Difference Methods; 2.7 Description of the SAS Macro; 2.8 Description of the SPSS Macro; 2.9 Description of the Stata Macro; 2.10 Hypothetical Example with Output; 2.11 Empirical Example in Genetic Epidemiology; 2.12 When to Include an Exposure--Mediator Interaction; 2.13 Proportion Mediated; 2.14 Proportion Eliminated; 2.15 Study Design and Mediation Analysis; 2.16 Counterfactual Notation for Natural Direct and Indirect Effects; 2.17 An Alternative Regression-Based Estimation Approach Using Simulations 2.18 Code for the Simulation-Based Approach in R2.19 Discussion; 3 Sensitivity Analysis for Mediation; 3.1 Sensitivity Analysis for Unmeasured Confounding for Total Effects; 3.2 Sensitivity Analysis for Unmeasured Confounding for Controlled Direct Effects; 3.3 Sensitivity Analysis for Unmeasured Confounding for Natural Direct and Indirect Effects; 3.4 Sensitivity Analysis Using Two Trials; 3.5 Sensitivity Analysis for Direct and Indirect Effects in the Presence of Measurement Error; 3.6 Discussion; 4 Mediation Analysis with Survival Data 4.1 Earlier Literature on Mediation Analysis with Survival Models4.2 Mediation Analysis with an Accelerated Failure Time Model; 4.3 Mediation Analysis with a Proportional Hazards Model; 4.4 Mediation with an Additive Hazard Model; 4.5 A Weighting Approach to Direct and Indirect Effects with Survival Outcomes; 4.6 Sensitivity Analysis with Survival Data; 4.7 Discussion; 5 Multiple Mediators; 5.1 Regression-Based Approaches to Multiple Mediators; 5.2 A Weighting Approach to Multiple Mediators; 5.3 Controlled Direct Effects and Exposure-Induced Confounding 5.4 Effect Decomposition with Exposure-Induced Confounding5.5 Path-Specific Effects; 5.6 Sensitivity Analysis for Exposure-Induced Confounding; 5.7 Discussion; 6 Mediation Analysis with Time-Varying Exposures and Mediators; 6.1 Notation and Definitions; 6.2 Controlled Direct Effects with Time-Varying Exposures and Mediators; 6.3 Natural Direct and Indirect Effects and their Randomized Interventional Analogues with Time-Varying Exposures and Mediators; 6.4 Counterfactual Analysis of MacKinnon's Three-Wave Mediation Model; 6.5 Discussion; 7 Selected Topics in Mediation Analysis 7.1 Other Estimation Approaches |
| Record Nr. | UNINA-9910459790903321 |
|
VanderWeele Tyler
|
||
| New York : , : Oxford University Press, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Explanation in causal inference : methods for mediation and interaction / / Tyler J. VanderWeele
| Explanation in causal inference : methods for mediation and interaction / / Tyler J. VanderWeele |
| Autore | VanderWeele Tyler |
| Pubbl/distr/stampa | New York : , : Oxford University Press, , 2015 |
| Descrizione fisica | 1 online resource (729 p.) |
| Disciplina | 001.4/22 |
| Soggetto topico |
Social sciences - Research
Social sciences - Methodology Causation |
| ISBN | 0-19-932588-X |
| Classificazione | PSY031000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Explanation in Causal Inference; Copyright; Dedication; Contents; Preface; Part 1 Mediation Analysis; 1 Explanation and Mechanism; 1.1 Causal Inference and Explanation; 1.2 Forms of Explanation and Types of Mechanisms; 1.3 Motivations for Assessing Mediation, Interaction, and Interference; 1.4 Organization of this Book; 2 Mediation: Introduction and Regression-Based Approaches; 2.1 Classic Regression Approach to Mediation Analysis; 2.2 Counterfactual Approach to Mediation Analysis: Continuous Outcomes; 2.3 Assumptions about Confounding; 2.4 Binary and Count Outcomes
2.5 Binary Mediators2.6 Comparison of Approaches: Product-of-Coefficient and Difference Methods; 2.7 Description of the SAS Macro; 2.8 Description of the SPSS Macro; 2.9 Description of the Stata Macro; 2.10 Hypothetical Example with Output; 2.11 Empirical Example in Genetic Epidemiology; 2.12 When to Include an Exposure--Mediator Interaction; 2.13 Proportion Mediated; 2.14 Proportion Eliminated; 2.15 Study Design and Mediation Analysis; 2.16 Counterfactual Notation for Natural Direct and Indirect Effects; 2.17 An Alternative Regression-Based Estimation Approach Using Simulations 2.18 Code for the Simulation-Based Approach in R2.19 Discussion; 3 Sensitivity Analysis for Mediation; 3.1 Sensitivity Analysis for Unmeasured Confounding for Total Effects; 3.2 Sensitivity Analysis for Unmeasured Confounding for Controlled Direct Effects; 3.3 Sensitivity Analysis for Unmeasured Confounding for Natural Direct and Indirect Effects; 3.4 Sensitivity Analysis Using Two Trials; 3.5 Sensitivity Analysis for Direct and Indirect Effects in the Presence of Measurement Error; 3.6 Discussion; 4 Mediation Analysis with Survival Data 4.1 Earlier Literature on Mediation Analysis with Survival Models4.2 Mediation Analysis with an Accelerated Failure Time Model; 4.3 Mediation Analysis with a Proportional Hazards Model; 4.4 Mediation with an Additive Hazard Model; 4.5 A Weighting Approach to Direct and Indirect Effects with Survival Outcomes; 4.6 Sensitivity Analysis with Survival Data; 4.7 Discussion; 5 Multiple Mediators; 5.1 Regression-Based Approaches to Multiple Mediators; 5.2 A Weighting Approach to Multiple Mediators; 5.3 Controlled Direct Effects and Exposure-Induced Confounding 5.4 Effect Decomposition with Exposure-Induced Confounding5.5 Path-Specific Effects; 5.6 Sensitivity Analysis for Exposure-Induced Confounding; 5.7 Discussion; 6 Mediation Analysis with Time-Varying Exposures and Mediators; 6.1 Notation and Definitions; 6.2 Controlled Direct Effects with Time-Varying Exposures and Mediators; 6.3 Natural Direct and Indirect Effects and their Randomized Interventional Analogues with Time-Varying Exposures and Mediators; 6.4 Counterfactual Analysis of MacKinnon's Three-Wave Mediation Model; 6.5 Discussion; 7 Selected Topics in Mediation Analysis 7.1 Other Estimation Approaches |
| Record Nr. | UNINA-9910787222603321 |
|
VanderWeele Tyler
|
||
| New York : , : Oxford University Press, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Explanation in causal inference : methods for mediation and interaction / / Tyler J. VanderWeele
| Explanation in causal inference : methods for mediation and interaction / / Tyler J. VanderWeele |
| Autore | VanderWeele Tyler |
| Pubbl/distr/stampa | New York : , : Oxford University Press, , 2015 |
| Descrizione fisica | 1 online resource (729 p.) |
| Disciplina | 001.4/22 |
| Soggetto topico |
Social sciences - Research
Social sciences - Methodology Causation |
| ISBN | 0-19-932588-X |
| Classificazione | PSY031000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Explanation in Causal Inference; Copyright; Dedication; Contents; Preface; Part 1 Mediation Analysis; 1 Explanation and Mechanism; 1.1 Causal Inference and Explanation; 1.2 Forms of Explanation and Types of Mechanisms; 1.3 Motivations for Assessing Mediation, Interaction, and Interference; 1.4 Organization of this Book; 2 Mediation: Introduction and Regression-Based Approaches; 2.1 Classic Regression Approach to Mediation Analysis; 2.2 Counterfactual Approach to Mediation Analysis: Continuous Outcomes; 2.3 Assumptions about Confounding; 2.4 Binary and Count Outcomes
2.5 Binary Mediators2.6 Comparison of Approaches: Product-of-Coefficient and Difference Methods; 2.7 Description of the SAS Macro; 2.8 Description of the SPSS Macro; 2.9 Description of the Stata Macro; 2.10 Hypothetical Example with Output; 2.11 Empirical Example in Genetic Epidemiology; 2.12 When to Include an Exposure--Mediator Interaction; 2.13 Proportion Mediated; 2.14 Proportion Eliminated; 2.15 Study Design and Mediation Analysis; 2.16 Counterfactual Notation for Natural Direct and Indirect Effects; 2.17 An Alternative Regression-Based Estimation Approach Using Simulations 2.18 Code for the Simulation-Based Approach in R2.19 Discussion; 3 Sensitivity Analysis for Mediation; 3.1 Sensitivity Analysis for Unmeasured Confounding for Total Effects; 3.2 Sensitivity Analysis for Unmeasured Confounding for Controlled Direct Effects; 3.3 Sensitivity Analysis for Unmeasured Confounding for Natural Direct and Indirect Effects; 3.4 Sensitivity Analysis Using Two Trials; 3.5 Sensitivity Analysis for Direct and Indirect Effects in the Presence of Measurement Error; 3.6 Discussion; 4 Mediation Analysis with Survival Data 4.1 Earlier Literature on Mediation Analysis with Survival Models4.2 Mediation Analysis with an Accelerated Failure Time Model; 4.3 Mediation Analysis with a Proportional Hazards Model; 4.4 Mediation with an Additive Hazard Model; 4.5 A Weighting Approach to Direct and Indirect Effects with Survival Outcomes; 4.6 Sensitivity Analysis with Survival Data; 4.7 Discussion; 5 Multiple Mediators; 5.1 Regression-Based Approaches to Multiple Mediators; 5.2 A Weighting Approach to Multiple Mediators; 5.3 Controlled Direct Effects and Exposure-Induced Confounding 5.4 Effect Decomposition with Exposure-Induced Confounding5.5 Path-Specific Effects; 5.6 Sensitivity Analysis for Exposure-Induced Confounding; 5.7 Discussion; 6 Mediation Analysis with Time-Varying Exposures and Mediators; 6.1 Notation and Definitions; 6.2 Controlled Direct Effects with Time-Varying Exposures and Mediators; 6.3 Natural Direct and Indirect Effects and their Randomized Interventional Analogues with Time-Varying Exposures and Mediators; 6.4 Counterfactual Analysis of MacKinnon's Three-Wave Mediation Model; 6.5 Discussion; 7 Selected Topics in Mediation Analysis 7.1 Other Estimation Approaches |
| Record Nr. | UNINA-9910829114103321 |
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VanderWeele Tyler
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| New York : , : Oxford University Press, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Exploratory factor analysis [[electronic resource] /] / Leandre R. Fabrigar and Duane T. Wegener
| Exploratory factor analysis [[electronic resource] /] / Leandre R. Fabrigar and Duane T. Wegener |
| Autore | Fabrigar Leandre R |
| Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, c2012 |
| Descrizione fisica | 1 online resource (170 p.) |
| Disciplina |
001.4/22
001.422 |
| Altri autori (Persone) | WegenerDuane Theodore |
| Collana | Understanding statistics |
| Soggetto topico |
Factor analysis
Psychology - Mathematical models Social sciences - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-19-025584-6
1-283-62144-4 9786613933898 0-19-981351-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Contents; CHAPTER 1 Introductory Factor Analysis Concepts; The Goals of Factor Analysis; A Conceptual Introduction to the Common Factor Model; A Graphical Depiction of the Common Factor Model; A Simple Mathematical Introduction to the Common Factor Model; Chapter Summary and Book Overview; CHAPTER 2 Requirements for and Decisions in Choosing Exploratory Common Factor Analysis; Is EFA Suitable for the Research Question?; Are the Data Suitable for Factor Analysis?; Properties of the Measured Variables; Is an Exploratory or Confirmatory Approach Most Appropriate?
The Common Factor Model or Principal Component Model?Summary; CHAPTER 3 Requirements and Decisions for Implementing Exploratory Common Factor Analysis; Choosing a Method of Fitting the Common Factor Model; Determining the Appropriate Number of Common Factors; Rotating Factor Analysis Solutions; Concluding Comments; CHAPTER 4 Factor Analysis Assumptions; Assumptions Underlying the Common Factor Model; Assumptions Related to Model Fitting Procedures; Summary and Conclusions; CHAPTER 5 Implementing and Interpreting Exploratory Factor Analysis; Context for the Analysis: Pre-Analysis Decisions Example Research Question and MeasuresConducting the Analysis: Implementation of EFA; Concluding Comments; CHAPTER 6 Summary, Conclusions, and Recommendations; Understanding the Common Factor Model; Determining if Exploratory Factor Analysis is Appropriate; Decisions in Conducting Exploratory Factor Analysis; Assumptions Underlying the Common Factor Model and Fitting Procedures; Implementing and Interpreting EFA; Concluding Thoughts; Recommended Readings and Supplementary Programs; Index; A; B; C; D; E; F; G; H; I; K; L; M; N; O; P; Q; R; S; T; U; V |
| Record Nr. | UNINA-9910462070903321 |
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Fabrigar Leandre R
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| Oxford ; ; New York, : Oxford University Press, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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