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Large sample techniques for statistics / / Jiming Jiang
| Large sample techniques for statistics / / Jiming Jiang |
| Autore | Jiang Jiming |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (689 pages) |
| Disciplina | 519.52 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Mathematical statistics
Sampling (Statistics) Mostreig (Estadística) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030916954
9783030916947 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 The ε-δ Arguments -- 1.1 Introduction -- 1.2 Getting used to the ε-δ arguments -- 1.3 More examples -- 1.4 Case study: Consistency of MLE in the i.i.d. case -- 1.5 Some useful results -- 1.5.1 Infinite sequence -- 1.5.2 Infinite series -- 1.5.3 Topology -- 1.5.4 Continuity, differentiation, and integration -- 1.6 Exercises -- 2 Modes of Convergence -- 2.1 Introduction -- 2.2 Convergence in probability -- 2.3 Almost sure convergence -- 2.4 Convergence in distribution -- 2.5 Lp convergence and related topics -- 2.6 Case study: χ2-test -- 2.7 Summary and additional results -- 2.8 Exercises -- 3 Big O, Small o, and the Unspecified c -- 3.1 Introduction -- 3.2 Big O and small o for sequences and functions -- 3.3 Big O and small o for vectors and matrices -- 3.4 Big O and small o for random quantities -- 3.5 The unspecified c and other similar methods -- 3.6 Case study: The baseball problem -- 3.7 Case study: Likelihood ratio for a clustering problem -- 3.8 Exercises -- 4 Asymptotic Expansions -- 4.1 Introduction -- 4.2 Taylor expansion -- 4.3 Edgeworth expansion -- method of formal derivation -- 4.4 Other related expansions -- 4.4.1 Fourier series expansion -- 4.4.2 Cornish-Fisher expansion -- 4.4.3 Two time series expansions -- 4.5 Some elementary expansions -- 4.6 Laplace approximation -- 4.7 Case study: Asymptotic distribution of the MLE -- 4.8 Case study: The Prasad-Rao method -- 4.9 Exercises -- 5 Inequalities -- 5.1 Introduction -- 5.2 Numerical inequalities -- 5.2.1 The convex function inequality -- 5.2.2 Hölder's and related inequalities -- 5.2.3 Monotone functions and related inequalities -- 5.3 Matrix inequalities -- 5.3.1 Nonnegative definite matrices -- 5.3.2 Characteristics of matrices -- 5.4 Integral/moment inequalities -- 5.5 Probability inequalities.
5.6 Case study: Some problems on existence of moments -- 5.7 Case study: A variance inequality -- 5.8 Exercises -- 6 Sums of Independent Random Variables -- 6.1 Introduction -- 6.2 The weak law of large numbers -- 6.3 The strong law of large numbers -- 6.4 The central limit theorem -- 6.5 The law of the iterated logarithm -- 6.6 Further results -- 6.6.1 Invariance principles in CLT and LIL -- 6.6.2 Large deviations -- 6.7 Case study: The least squares estimators -- 6.8 Exercises -- 7 Empirical Processes -- 7.1 Introduction -- 7.2 Glivenko-Cantelli theorem and statistical functionals -- 7.3 Weak convergence of empirical processes -- 7.4 LIL and strong approximation -- 7.5 Bounds and large deviations -- 7.6 Non-i.i.d. observations -- 7.7 Empirical processes indexed by functions -- 7.8 Case study: Estimation of ROC curve and ODC -- 7.9 Exercises -- 8 Martingales -- 8.1 Introduction -- 8.2 Examples and simple properties -- 8.3 Two important theorems of martingales -- 8.3.1 The optional stopping theorem -- 8.3.2 The martingale convergence theorem -- 8.4 Martingale laws of large numbers -- 8.4.1 A weak law of large numbers -- 8.4.2 Some strong laws of large numbers -- 8.5 A martingale central limit theorem and related topic -- 8.6 Convergence rate in SLLN and LIL -- 8.7 Invariance principles for martingales -- 8.8 Case study: CLTs for quadratic forms -- 8.9 Case study: Martingale approximation -- 8.10 Exercises -- 9 Time and Spatial Series -- 9.1 Introduction -- 9.2 Autocovariances and autocorrelations -- 9.3 The information criteria -- 9.4 ARMA model identification -- 9.5 Strong limit theorems for i.i.d. spatial series -- 9.6 Two-parameter martingale differences -- 9.7 Sample ACV and ACR for spatial series -- 9.8 Case study: Spatial AR models -- 9.9 Exercises -- 10 Stochastic Processes -- 10.1 Introduction -- 10.2 Markov chains -- 10.3 Poisson processes. 10.4 Renewal theory -- 10.5 Brownian motion -- 10.6 Stochastic integrals and diffusions -- 10.7 Case study: GARCH models and financial SDE -- 10.8 Exercises -- 11 Nonparametric Statistics -- 11.1 Introduction -- 11.2 Some classical nonparametric tests -- 11.3 Asymptotic relative efficiency -- 11.4 Goodness-of-fit tests -- 11.5 U-statistics -- 11.6 Density estimation -- 11.7 Exercises -- 12 Mixed Effects Models -- 12.1 Introduction -- 12.2 REML: Restricted maximum likelihood -- 12.3 Linear mixed model diagnostics -- 12.4 Inference about GLMM -- 12.5 Mixed model selection -- 12.6 Exercises -- 13 Small-Area Estimation -- 13.1 Introduction -- 13.2 Empirical best prediction with binary data -- 13.3 The Fay-Herriot model -- 13.4 Nonparametric small-area estimation -- 13.5 Model selection for small-area estimation -- 13.6 Exercises -- 14 Jackknife and Bootstrap -- 14.1 Introduction -- 14.2 The jackknife -- 14.3 Jackknifing the MSPE of EBP -- 14.4 The bootstrap -- 14.5 Bootstrapping time series -- 14.6 Bootstrapping mixed models -- 14.7 Exercises -- 15 Markov-Chain Monte Carlo -- 15.1 Introduction -- 15.2 The Gibbs sampler -- 15.3 The Metropolis-Hastings algorithm -- 15.4 Monte Carlo EM algorithm -- 15.5 Convergence rates of Gibbs samplers -- 15.6 Exercises -- 16 Random Matrix Theory -- 16.1 Introduction -- 16.2 Fundamental theorems of RMT -- 16.3 Large covariance matrices -- 16.4 High-dimensional linear models -- 16.5 Genome-wide association study -- 16.6 Application to time series -- 16.7 Exercises -- Appendix A -- A.1 Matrix algebra -- A.1.1 Numbers associated with a matrix -- A.1.2 Inverse of a matrix -- A.1.3 Kronecker products -- A.1.4 Matrix differentiation -- A.1.5 Projection -- A.1.6 Decompositions of matrices and eigenvalues -- A.2 Measure and probability -- A.2.1 Measures -- A.2.2 Measurable functions -- A.2.3 Integration. A.2.4 Distributions and random variables -- A.2.5 Conditional expectations -- A.2.6 Conditional distributions -- A.3 Some results in statistics -- A.3.1 The multivariate normal distribution -- A.3.2 Maximum likelihood -- A.3.3 Exponential family and generalized linear models -- A.3.4 Bayesian inference -- A.3.5 Stationary processes -- A.4 List of notation and abbreviations -- References -- Index. |
| Record Nr. | UNISA-996472039303316 |
Jiang Jiming
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Large sample techniques for statistics / / Jiming Jiang
| Large sample techniques for statistics / / Jiming Jiang |
| Autore | Jiang Jiming |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (689 pages) |
| Disciplina | 519.52 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Mathematical statistics
Sampling (Statistics) Mostreig (Estadística) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030916954
9783030916947 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 The ε-δ Arguments -- 1.1 Introduction -- 1.2 Getting used to the ε-δ arguments -- 1.3 More examples -- 1.4 Case study: Consistency of MLE in the i.i.d. case -- 1.5 Some useful results -- 1.5.1 Infinite sequence -- 1.5.2 Infinite series -- 1.5.3 Topology -- 1.5.4 Continuity, differentiation, and integration -- 1.6 Exercises -- 2 Modes of Convergence -- 2.1 Introduction -- 2.2 Convergence in probability -- 2.3 Almost sure convergence -- 2.4 Convergence in distribution -- 2.5 Lp convergence and related topics -- 2.6 Case study: χ2-test -- 2.7 Summary and additional results -- 2.8 Exercises -- 3 Big O, Small o, and the Unspecified c -- 3.1 Introduction -- 3.2 Big O and small o for sequences and functions -- 3.3 Big O and small o for vectors and matrices -- 3.4 Big O and small o for random quantities -- 3.5 The unspecified c and other similar methods -- 3.6 Case study: The baseball problem -- 3.7 Case study: Likelihood ratio for a clustering problem -- 3.8 Exercises -- 4 Asymptotic Expansions -- 4.1 Introduction -- 4.2 Taylor expansion -- 4.3 Edgeworth expansion -- method of formal derivation -- 4.4 Other related expansions -- 4.4.1 Fourier series expansion -- 4.4.2 Cornish-Fisher expansion -- 4.4.3 Two time series expansions -- 4.5 Some elementary expansions -- 4.6 Laplace approximation -- 4.7 Case study: Asymptotic distribution of the MLE -- 4.8 Case study: The Prasad-Rao method -- 4.9 Exercises -- 5 Inequalities -- 5.1 Introduction -- 5.2 Numerical inequalities -- 5.2.1 The convex function inequality -- 5.2.2 Hölder's and related inequalities -- 5.2.3 Monotone functions and related inequalities -- 5.3 Matrix inequalities -- 5.3.1 Nonnegative definite matrices -- 5.3.2 Characteristics of matrices -- 5.4 Integral/moment inequalities -- 5.5 Probability inequalities.
5.6 Case study: Some problems on existence of moments -- 5.7 Case study: A variance inequality -- 5.8 Exercises -- 6 Sums of Independent Random Variables -- 6.1 Introduction -- 6.2 The weak law of large numbers -- 6.3 The strong law of large numbers -- 6.4 The central limit theorem -- 6.5 The law of the iterated logarithm -- 6.6 Further results -- 6.6.1 Invariance principles in CLT and LIL -- 6.6.2 Large deviations -- 6.7 Case study: The least squares estimators -- 6.8 Exercises -- 7 Empirical Processes -- 7.1 Introduction -- 7.2 Glivenko-Cantelli theorem and statistical functionals -- 7.3 Weak convergence of empirical processes -- 7.4 LIL and strong approximation -- 7.5 Bounds and large deviations -- 7.6 Non-i.i.d. observations -- 7.7 Empirical processes indexed by functions -- 7.8 Case study: Estimation of ROC curve and ODC -- 7.9 Exercises -- 8 Martingales -- 8.1 Introduction -- 8.2 Examples and simple properties -- 8.3 Two important theorems of martingales -- 8.3.1 The optional stopping theorem -- 8.3.2 The martingale convergence theorem -- 8.4 Martingale laws of large numbers -- 8.4.1 A weak law of large numbers -- 8.4.2 Some strong laws of large numbers -- 8.5 A martingale central limit theorem and related topic -- 8.6 Convergence rate in SLLN and LIL -- 8.7 Invariance principles for martingales -- 8.8 Case study: CLTs for quadratic forms -- 8.9 Case study: Martingale approximation -- 8.10 Exercises -- 9 Time and Spatial Series -- 9.1 Introduction -- 9.2 Autocovariances and autocorrelations -- 9.3 The information criteria -- 9.4 ARMA model identification -- 9.5 Strong limit theorems for i.i.d. spatial series -- 9.6 Two-parameter martingale differences -- 9.7 Sample ACV and ACR for spatial series -- 9.8 Case study: Spatial AR models -- 9.9 Exercises -- 10 Stochastic Processes -- 10.1 Introduction -- 10.2 Markov chains -- 10.3 Poisson processes. 10.4 Renewal theory -- 10.5 Brownian motion -- 10.6 Stochastic integrals and diffusions -- 10.7 Case study: GARCH models and financial SDE -- 10.8 Exercises -- 11 Nonparametric Statistics -- 11.1 Introduction -- 11.2 Some classical nonparametric tests -- 11.3 Asymptotic relative efficiency -- 11.4 Goodness-of-fit tests -- 11.5 U-statistics -- 11.6 Density estimation -- 11.7 Exercises -- 12 Mixed Effects Models -- 12.1 Introduction -- 12.2 REML: Restricted maximum likelihood -- 12.3 Linear mixed model diagnostics -- 12.4 Inference about GLMM -- 12.5 Mixed model selection -- 12.6 Exercises -- 13 Small-Area Estimation -- 13.1 Introduction -- 13.2 Empirical best prediction with binary data -- 13.3 The Fay-Herriot model -- 13.4 Nonparametric small-area estimation -- 13.5 Model selection for small-area estimation -- 13.6 Exercises -- 14 Jackknife and Bootstrap -- 14.1 Introduction -- 14.2 The jackknife -- 14.3 Jackknifing the MSPE of EBP -- 14.4 The bootstrap -- 14.5 Bootstrapping time series -- 14.6 Bootstrapping mixed models -- 14.7 Exercises -- 15 Markov-Chain Monte Carlo -- 15.1 Introduction -- 15.2 The Gibbs sampler -- 15.3 The Metropolis-Hastings algorithm -- 15.4 Monte Carlo EM algorithm -- 15.5 Convergence rates of Gibbs samplers -- 15.6 Exercises -- 16 Random Matrix Theory -- 16.1 Introduction -- 16.2 Fundamental theorems of RMT -- 16.3 Large covariance matrices -- 16.4 High-dimensional linear models -- 16.5 Genome-wide association study -- 16.6 Application to time series -- 16.7 Exercises -- Appendix A -- A.1 Matrix algebra -- A.1.1 Numbers associated with a matrix -- A.1.2 Inverse of a matrix -- A.1.3 Kronecker products -- A.1.4 Matrix differentiation -- A.1.5 Projection -- A.1.6 Decompositions of matrices and eigenvalues -- A.2 Measure and probability -- A.2.1 Measures -- A.2.2 Measurable functions -- A.2.3 Integration. A.2.4 Distributions and random variables -- A.2.5 Conditional expectations -- A.2.6 Conditional distributions -- A.3 Some results in statistics -- A.3.1 The multivariate normal distribution -- A.3.2 Maximum likelihood -- A.3.3 Exponential family and generalized linear models -- A.3.4 Bayesian inference -- A.3.5 Stationary processes -- A.4 List of notation and abbreviations -- References -- Index. |
| Record Nr. | UNINA-9910559398903321 |
|
Jiang Jiming
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Latent class analysis of survey error [[electronic resource] /] / Paul P. Biemer
| Latent class analysis of survey error [[electronic resource] /] / Paul P. Biemer |
| Autore | Biemer Paul P |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (412 p.) |
| Disciplina |
511/.43
519.535 |
| Collana | Wiley series in survey methodology |
| Soggetto topico |
Error analysis (Mathematics)
Sampling (Statistics) Estimation theory |
| ISBN |
1-118-09957-5
1-282-88443-3 9786612884436 0-470-89115-7 0-470-89114-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Latent Class Analysis of Survey Error; Contents; Preface; Abbreviations; CHAPTER 1: Survey Error Evaluation; CHAPTER 2: A General Model for Measurement Error; CHAPTER 3: Response Probability Models for Two Measurements; CHAPTER 4: Latent Class Models for Evaluating Classification Errors; CHAPTER 5: Further Aspects of Latent Class Modeling; CHAPTER 6: Latent Class Models for Special Applications; CHAPTER 7: Latent Class Models for Panel Data; CHAPTER 8: Survey Error Evaluation: Past, Present, and Future; APPENDIX A: Two-Stage Sampling Formulas; APPENDIX B: Loglinear Modeling Essentials
ReferencesIndex |
| Record Nr. | UNINA-9910140870803321 |
|
Biemer Paul P
|
||
| Hoboken, N.J., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Latent class analysis of survey error [[electronic resource] /] / Paul P. Biemer
| Latent class analysis of survey error [[electronic resource] /] / Paul P. Biemer |
| Autore | Biemer Paul P |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (412 p.) |
| Disciplina |
511/.43
519.535 |
| Collana | Wiley series in survey methodology |
| Soggetto topico |
Error analysis (Mathematics)
Sampling (Statistics) Estimation theory |
| ISBN |
1-118-09957-5
1-282-88443-3 9786612884436 0-470-89115-7 0-470-89114-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Latent Class Analysis of Survey Error; Contents; Preface; Abbreviations; CHAPTER 1: Survey Error Evaluation; CHAPTER 2: A General Model for Measurement Error; CHAPTER 3: Response Probability Models for Two Measurements; CHAPTER 4: Latent Class Models for Evaluating Classification Errors; CHAPTER 5: Further Aspects of Latent Class Modeling; CHAPTER 6: Latent Class Models for Special Applications; CHAPTER 7: Latent Class Models for Panel Data; CHAPTER 8: Survey Error Evaluation: Past, Present, and Future; APPENDIX A: Two-Stage Sampling Formulas; APPENDIX B: Loglinear Modeling Essentials
ReferencesIndex |
| Record Nr. | UNINA-9910827493303321 |
|
Biemer Paul P
|
||
| Hoboken, N.J., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Local Census Quality Check Act [[electronic resource] ] : report together with minority views (to accompany H.R. 472) (including cost estimate of the Congressional Budget Office)
| Local Census Quality Check Act [[electronic resource] ] : report together with minority views (to accompany H.R. 472) (including cost estimate of the Congressional Budget Office) |
| Pubbl/distr/stampa | [Washington, D.C.] : , : [U.S. G.P.O.], , [1999] |
| Descrizione fisica | 1 online resource (16 pages) |
| Collana | Report / 106th Congress, 1st session, House of Representatives |
| Soggetto topico |
Sampling (Statistics)
Census undercounts - United States |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Local Census Quality Check Act |
| Record Nr. | UNINA-9910702299303321 |
| [Washington, D.C.] : , : [U.S. G.P.O.], , [1999] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Make Your Data Speak : Creating Actionable Data through Excel For Non-Technical Professionals / / by Alex Kolokolov
| Make Your Data Speak : Creating Actionable Data through Excel For Non-Technical Professionals / / by Alex Kolokolov |
| Autore | Kolokolov Alex |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (269 pages) |
| Disciplina | 801 |
| Soggetto topico |
Data structures (Computer science)
Database management Big data Sampling (Statistics) |
| ISBN | 1-4842-8942-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Data preparation -- Chapter 2. Dashboard assembling -- Chapter 3. Anatomy of diagrams -- Chapter 4. Final Design -- Chapter 5. Corporate identity -- Chapter 6. Data visualization rules -- Conclusion. |
| Record Nr. | UNINA-9910672441803321 |
|
Kolokolov Alex
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Maximum likelihood estimation for sample surveys / / R.L. Chambers. [et al.]
| Maximum likelihood estimation for sample surveys / / R.L. Chambers. [et al.] |
| Autore | Chambers R. L (Ray L.) |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (374 p.) |
| Disciplina |
001.4
001.4/33 001.433 |
| Collana | Monographs on statistics and applied probability |
| Soggetto topico |
Sampling (Statistics)
Surveys - Statistical methods |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-14472-5
1-4200-1135-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Dedication; Contents; Preface; 1. Introduction; 2. Maximum likelihood theory for sample surveys; 3. Alternative likelihood-based methods for sample survey data; 4. Populations with independent units; 5. Regression models; 6. Clustered populations; 7. Informative nonresponse; 8. Maximum likelihood in other complicated situations; Notation |
| Record Nr. | UNINA-9910452004903321 |
|
Chambers R. L (Ray L.)
|
||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Maximum likelihood estimation for sample surveys / / R.L. Chambers. [et al.]
| Maximum likelihood estimation for sample surveys / / R.L. Chambers. [et al.] |
| Autore | Chambers R. L (Ray L.) |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (374 p.) |
| Disciplina |
001.4
001.4/33 001.433 |
| Collana | Monographs on statistics and applied probability |
| Soggetto topico |
Sampling (Statistics)
Surveys - Statistical methods |
| ISBN |
0-429-14472-5
1-4200-1135-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Dedication; Contents; Preface; 1. Introduction; 2. Maximum likelihood theory for sample surveys; 3. Alternative likelihood-based methods for sample survey data; 4. Populations with independent units; 5. Regression models; 6. Clustered populations; 7. Informative nonresponse; 8. Maximum likelihood in other complicated situations; Notation |
| Record Nr. | UNINA-9910779177103321 |
|
Chambers R. L (Ray L.)
|
||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Maximum likelihood estimation for sample surveys / / R.L. Chambers. [et al.]
| Maximum likelihood estimation for sample surveys / / R.L. Chambers. [et al.] |
| Autore | Chambers R. L (Ray L.) |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (374 p.) |
| Disciplina |
001.4
001.4/33 001.433 |
| Collana | Monographs on statistics and applied probability |
| Soggetto topico |
Sampling (Statistics)
Surveys - Statistical methods |
| ISBN |
0-429-14472-5
1-4200-1135-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Dedication; Contents; Preface; 1. Introduction; 2. Maximum likelihood theory for sample surveys; 3. Alternative likelihood-based methods for sample survey data; 4. Populations with independent units; 5. Regression models; 6. Clustered populations; 7. Informative nonresponse; 8. Maximum likelihood in other complicated situations; Notation |
| Record Nr. | UNINA-9910808084403321 |
|
Chambers R. L (Ray L.)
|
||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Meta-analysis : a structural equation modeling approach / / Mike W. L. Cheung
| Meta-analysis : a structural equation modeling approach / / Mike W. L. Cheung |
| Autore | Cheung Mike W. L. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, England ; ; West Sussex, England : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (403 p.) |
| Disciplina | 001.4/22 |
| Soggetto topico |
Statistics
Meta-analysis Research - Statistical methods Sampling (Statistics) |
| ISBN |
1-118-95782-2
1-118-95783-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Cover ""; ""Title Page ""; ""Copyright ""; ""Contents ""; ""Preface ""; ""Acknowledgments ""; ""List of abbreviations ""; ""List of figures ""; ""List of tables ""; ""Chapter 1 Introduction ""; ""1.1 What is meta-analysis? ""; ""1.2 What is structural equation modeling? ""
""1.3 Reasons for writing a book on meta-analysis and structural equation modeling """"1.3.1 Benefits to users of structural equation modeling and meta-analysis ""; ""1.4 Outline of the following chapters ""; ""1.4.1 Computer examples and data sets used in this book "" ""1.5 Concluding remarks and further readings """"References ""; ""Chapter 2 Brief review of structural equation modeling ""; ""2.1 Introduction ""; ""2.2 Model specification ""; ""2.2.1 Equations ""; ""2.2.2 Path diagram ""; ""2.2.3 Matrix representation ""; ""2.3 Common structural equation models "" ""2.3.1 Path analysis """"2.3.2 Confirmatory factor analysis ""; ""2.3.3 Structural equation model ""; ""2.3.4 Latent growth model ""; ""2.3.5 Multiple-group analysis ""; ""2.4 Estimation methods, test statistics, and goodness-of-fit indices ""; ""2.4.1 Maximum likelihood estimation "" ""2.4.2 Weighted least squares """"2.4.3 Multiple-group analysis ""; ""2.4.4 Likelihood ratio test and Wald test ""; ""2.4.5 Confidence intervals on parameter estimates ""; ""2.4.6 Test statistics versus goodness-of-fit indices ""; ""2.5 Extensions on structural equation modeling "" ""2.5.1 Phantom variables "" |
| Record Nr. | UNINA-9910131296503321 |
|
Cheung Mike W. L.
|
||
| Chichester, England ; ; West Sussex, England : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
