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Applications of linear and nonlinear models : fixed effects, random effects, and total least squares / / Joseph L. Awange, Erik W. Grafarend, Silvelyn Zwanzig
Applications of linear and nonlinear models : fixed effects, random effects, and total least squares / / Joseph L. Awange, Erik W. Grafarend, Silvelyn Zwanzig
Autore Awange Joseph L. <1969->
Edizione [2nd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (1127 pages)
Disciplina 550
Collana Springer geophysics
Soggetto topico Geophysics
Linear models (Statistics)
Mathematical models
Geofísica
Models lineals (Estadística)
Models matemàtics
Soggetto genere / forma Llibres electrònics
ISBN 3-030-94598-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Contents -- Preface to the First Edition -- Preface to the Second Edition -- Chapter 1 The First Problem of Algebraic Regression -- 1-1 Introduction -- 1-11 The Front Page Example -- 1-12 The Front Page Example: Matrix Algebra -- 1-13 The Front Page Example: MINOS, Horizontal Rank Partitioning -- 1-14 The Range R(f) and the Kernel N(f) -- 1-15 The Interpretation of MINOS -- 1-2 Minimum Norm Solution (MINOS) -- 1-21 A Discussion of the Metric of the Parameter Space X -- 1-22 An Alternative Choice of the Metric of the Parameter Space X -- 1-23 Gx-MINOS and Its Generalized Inverse -- 1-24 Eigenvalue Decomposition of Gx-MINOS: Canonical MINOS -- 1-3 Case Study -- 1-31 Fourier Series -- 1-32 Fourier-Legendre Series -- 1-33 Nyquist Frequency for Spherical Data -- 1-4 Special Nonlinear Models -- 1-41 Taylor Polynomials, Generalized Newton Iteration -- 1-42 Linearized Models with Datum Defect -- 1-5 Notes -- Chapter 2 The First Problem of Probabilistic Regression: The Bias Problem -- 2-1 Linear Uniformly Minimum Bias Estimator (LUMBE) -- 2-2 The Equivalence Theorem of Gx-MINOS and S-LUMBE -- 2-3 Example -- Chapter 3 The Second Problem of Algebraic Regression -- 3-1 Introduction -- 3-11 The Front Page Example -- 3-12 The Front Page Example in Matrix Algebra -- 3-13 Least Squares Solution of the Front Page Example by Means of Vertical Rank Partitioning -- 3-14 The RangeR(f) and the Kernel N(f), Interpretation of "LESS" by Three Partitionings -- 3-2 The Least Squares Solution: "LESS" -- 3-21 A Discussion of the Metric of the Parameter Space X -- 3-22 Alternative Choices of the Metric of the Observation Y -- 3-23 Gx-LESS and Its Generalized Inverse -- 3-24 Eigenvalue Decomposition of Gy-LESS: Canonical LESS -- 3-3 Case Study -- 3-31 Canonical Analysis of the Hat Matrix, Partial Redundancies, High Leverage Points.
3-32 Multilinear Algebra, "Join" and "Meet", the Hodge Star Operator -- 3-33 From A to B: Latent Restrictions, Grassmann Coordinates, Plücker Coordinates -- 3-34 From B to A: Latent Parametric Equations, Dual Grassmann Coordinates, Dual Plücker Coordinates -- 3-35 Break Points -- 3-4 Special Linear and Nonlinear Models: A Family of Means for Direct Observations -- 3-5 A Historical Note on C.F. Gauss and A.M. Legendre -- Chapter 4 The Second Problem of Probabilistic Regression -- 4-1 Introduction -- 4-11 The Front Page Example -- 4-12 Estimators of Type BLUUE and BIQUUE of the Front Page Example -- 4-13 BLUUE and BIQUUE of the Front Page Example, Sample Median, Median Absolute Deviation -- 4-14 Alternative Estimation Maximum Likelihood (MALE) -- 4-2 Setup of the Best Linear Uniformly Unbiased Estimator -- 4-21 The Best Linear Uniformly Unbiased Estimation ^ξ of ξ : Σy-BLUUE -- 4-22 The Equivalence Theorem of Gy-LESS and Σy-BLUUE -- 4-3 Setup of the Best Invariant Quadratic Uniformly Unbiased Estimator -- 4-31 Block Partitioning of the Dispersion Matrix and Linear Space Generated by Variance-Covariance Components -- 4-32 Invariant Quadratic Estimation of Variance-Covariance Components of Type IQE -- 4-33 Invariant Quadratic Uniformly Unbiased Estimations of Variance-Covariance Components of Type IQUUE -- 4-34 Invariant Quadratic Uniformly Unbiased Estimationsof One Variance Component (IQUUE) from Σy-BLUUE: HIQUUE -- 4-35 Invariant Quadratic Uniformly Unbiased Estimators of Variance Covariance Components of Helmert Type: HIQUUE Versus HIQE -- 4-36 Best Quadratic Uniformly Unbiased Estimations of One Variance Component: BIQUUE -- 4-37 Simultaneous Determination of First Moment and the Second Central Moment, Inhomogeneous Multilinear Estimation, the E - D Correspondence, Bayes Designwith Moment Estimations.
Chapter 5 The Third Problem of Algebraic Regression -- 5-1 Introduction -- 5-11 The Front Page Example -- 5-12 The Front Page Example in Matrix Algebra -- 5-13 Minimum Norm: Least Squares Solution of the Front Page Example by Means of Additive Rank Partitioning -- 5-14 Minimum Norm: Least Squares Solution of the Front Page Example by Means of Multiplicative Rank Partitioning -- 5-15 The Range R(f) and the Kernel N(f) Interpretation of "MINOLESS" by Three Partitionings -- 5-2 MINOLESS and Related Solutions Like Weighted Minimum Norm-Weighted Least Squares Solutions -- 5-21 The Minimum Norm-Least Squares Solution: "MINOLESS" -- 5-22 (Gx, Gy)-MINOS and Its Generalized Inverse -- 5-23 Eigenvalue Decomposition of (Gx, Gy)-MINOLESS -- 5-24 Notes -- 5-3 The Hybrid Approximation Solution: α-HAPS and Tykhonov-Phillips Regularization -- Chapter 6 The Third Problem of Probabilistic Regression -- 6-1 Setup of the Best Linear Minimum Bias Estimator of Type BLUMBE -- 6-11 Definitions, Lemmas and Theorems -- 6-12 The First Example: BLUMBE Versus BLE, BIQUUE Versus BIQE, Triangular Leveling Network -- 6-2 Setup of the Best Linear Estimators of Type hom BLE, hom S-BLE and hom a-BLE for Fixed Effects -- 6-3 Continuous Networks -- 6-31 Continuous Networks of Second Derivatives Type -- Chapter 7 Overdetermined System of Nonlinear Equations on Curved Manifolds -- 7-1 Introduction -- 7-2 Minimal Geodesic Distance: MINGEODISC -- 7-3 Special Models: From the Circular Normal Distribution to the Oblique Normal Distribution -- 7-31 A Historical Note of the von Mises Distribution -- 7-32 Oblique Map Projection -- 7-33 A Note on the Angular Metric -- 7-4 Case Study -- References -- Chapter 8 The Fourth Problem of Probabilistic Regression -- 8-1 The Random Effect Model -- 8-2 Examples.
Chapter 9 The Fifth Problem of Algebraic Regression: The System of Conditional Equations: Homogeneous and Inhomogeneous Equations: {By = Bi versus -c + By = Bi} -- 9-1 Gy-LESS of a System of a Inconsistent Homogeneous Conditional Equations -- 9-2 Solving a System of Inconsistent Inhomogeneous Conditional Equations -- 9-3 Examples -- Chapter 10 The Fifth Problem of Probabilistic Regression -- 10-1 Inhomogeneous General Linear Gauss-Markov Model (Fixed Effects and Random Effects) -- 10-2 Explicit Representations of Errors in the General Gauss-Markov Model with Mixed Effects -- 10-3 An Example for Collocation -- 10-4 Comments -- Chapter 11 The sixth problem of probabilistic regression -- 11-1 Introduction -- 11-2 The Errors-in-Variables Model and its Symmetry -- 11-3 Least Squares in Linear Errors-in-Variables Models -- 11-31 Naive Least Squares -- 11-32 Total Least Squares TLS -- 11-4 SIMEX and SYMEX -- 11-41 SIMEX -- 11-42 SYMEX -- 11-5 Datum Transformation -- 11-6 Nonlinear Errors-in-Variables Models -- Chapter 12 The Nonlinear Problem of the 3d Datum Transformation and the Procrustes Algorithm -- 12-1 The 3d Datum Transformation and the Procrustes Algorithm -- 12-2 The Variance: Covariance Matrix of the Error Matrix E -- 12-3 References -- Chapter 13 The Sixth Problem of Generalized Algebraic Regression -- 13-1 Variance-Covariance-Component Estimation in the Linear Model Ax + ε = y, y ∉ R(A) -- 13-2 Variance-Covariance-Component Estimation in the Linear Model Bε = By -c, By ∉ R(A) + c -- 13-3 Variance-Covariance-Component Estimation in theLinear Model Ax + ε + Bε = By -c, By ∉ R(A) + c -- 13-4 The Block Structure of Dispersion Matrix D{y} -- Chapter 14 Special Problems of Algebraic Regression and Stochastic Estimation -- 14-1 The Multivariate Gauss-Markov Model: A Special Problem of Probabilistic Regression -- 14-2 n-Way Classification Models.
14-21 A First Example: 1-Way Classification -- 14-22 A Second Example: 2-Way Classification Without Interaction -- 14-23 A Third Example: 2-Way Classification with Interaction -- 14-24 Higher Classifications with Interaction -- 14-3 Dynamical Systems -- Chapter 15 Systems of equations: Hybrid algebraic-numeric solutions -- 15-1 Algebraic, numeric, and hybrid algebraic-numeric -- 15-2 Algebraic solutions: Background -- 15-3 Nonlinear systems of equations: Algebraic methods -- 15-31 Nonlinear Gauss-Markov model: Algebraic solution -- 15-32 Adjustment of the combinatorial subsets -- 15-4 Examples -- 15-5 Hybrid algebraic-numeric methods -- 15-6 Notes -- Chapter 16 Integer Least Squares -- 16-1 Introductory remarks -- 16-2 Model for Positioning -- 16-3 Mixed Integer Linear Model -- 16-4 Integer Least Squares -- 16-41 Simple Rounding Solution -- 16-42 Main Steps -- 16-43 The Closest Vector Problem (CVP) -- 16-44 Reduction -- 16-45 Gram-Schmidt Method -- 16-46 The LLL Algorithm -- 16-47 Babai's Rounding Technique -- Chapter 17 Bayesian Inference -- 17-1 Introduction -- 17-2 Principle of Bayesian Analysis -- 17-21 Sequential Analysis -- 17-22 Hierarchical Bayes Models -- 17-23 Choice of Prior -- 17-24 Bayesian Inference -- 17-3 Univariate Linear Model -- 17-31 Model Assumptions -- 17-32 Normal-inverse-gamma Distribution -- 17-33 Noninformative Prior -- 17-34 Conjugate Prior -- 17-35 Regularized Estimators -- 17-4 Mixed Model -- 17-41 Prior Distribution -- 17-42 Posterior Distribution -- 17-5 Multivariate Linear Model -- 17-51 Normal-inverse-Wishart Distribution -- 17-52 Noninformative Prior -- 17-53 Informative Prior -- 17-6 Computer Intensive Methods -- 17-61 Independent Monte Carlo (MC) -- 17-62 Importance Sampling -- 17-63 Markov Chain Monte Carlo -- 17-64 Gibbs Sampling -- 17-65 Rejection Algorithm -- 17-66 Approximative Bayesian Computation (ABC).
Appendix A Tensor Algebra, Linear Algebra, Matrix Algebra, Multilinear Algebra.
Record Nr. UNISA-996495171203316
Awange Joseph L. <1969->  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Applications of linear and nonlinear models : fixed effects, random effects, and total least squares / / Joseph L. Awange, Erik W. Grafarend, Silvelyn Zwanzig
Applications of linear and nonlinear models : fixed effects, random effects, and total least squares / / Joseph L. Awange, Erik W. Grafarend, Silvelyn Zwanzig
Autore Awange Joseph L. <1969->
Edizione [2nd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (1127 pages)
Disciplina 550
Collana Springer geophysics
Soggetto topico Geophysics
Linear models (Statistics)
Mathematical models
Geofísica
Models lineals (Estadística)
Models matemàtics
Soggetto genere / forma Llibres electrònics
ISBN 3-030-94598-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Contents -- Preface to the First Edition -- Preface to the Second Edition -- Chapter 1 The First Problem of Algebraic Regression -- 1-1 Introduction -- 1-11 The Front Page Example -- 1-12 The Front Page Example: Matrix Algebra -- 1-13 The Front Page Example: MINOS, Horizontal Rank Partitioning -- 1-14 The Range R(f) and the Kernel N(f) -- 1-15 The Interpretation of MINOS -- 1-2 Minimum Norm Solution (MINOS) -- 1-21 A Discussion of the Metric of the Parameter Space X -- 1-22 An Alternative Choice of the Metric of the Parameter Space X -- 1-23 Gx-MINOS and Its Generalized Inverse -- 1-24 Eigenvalue Decomposition of Gx-MINOS: Canonical MINOS -- 1-3 Case Study -- 1-31 Fourier Series -- 1-32 Fourier-Legendre Series -- 1-33 Nyquist Frequency for Spherical Data -- 1-4 Special Nonlinear Models -- 1-41 Taylor Polynomials, Generalized Newton Iteration -- 1-42 Linearized Models with Datum Defect -- 1-5 Notes -- Chapter 2 The First Problem of Probabilistic Regression: The Bias Problem -- 2-1 Linear Uniformly Minimum Bias Estimator (LUMBE) -- 2-2 The Equivalence Theorem of Gx-MINOS and S-LUMBE -- 2-3 Example -- Chapter 3 The Second Problem of Algebraic Regression -- 3-1 Introduction -- 3-11 The Front Page Example -- 3-12 The Front Page Example in Matrix Algebra -- 3-13 Least Squares Solution of the Front Page Example by Means of Vertical Rank Partitioning -- 3-14 The RangeR(f) and the Kernel N(f), Interpretation of "LESS" by Three Partitionings -- 3-2 The Least Squares Solution: "LESS" -- 3-21 A Discussion of the Metric of the Parameter Space X -- 3-22 Alternative Choices of the Metric of the Observation Y -- 3-23 Gx-LESS and Its Generalized Inverse -- 3-24 Eigenvalue Decomposition of Gy-LESS: Canonical LESS -- 3-3 Case Study -- 3-31 Canonical Analysis of the Hat Matrix, Partial Redundancies, High Leverage Points.
3-32 Multilinear Algebra, "Join" and "Meet", the Hodge Star Operator -- 3-33 From A to B: Latent Restrictions, Grassmann Coordinates, Plücker Coordinates -- 3-34 From B to A: Latent Parametric Equations, Dual Grassmann Coordinates, Dual Plücker Coordinates -- 3-35 Break Points -- 3-4 Special Linear and Nonlinear Models: A Family of Means for Direct Observations -- 3-5 A Historical Note on C.F. Gauss and A.M. Legendre -- Chapter 4 The Second Problem of Probabilistic Regression -- 4-1 Introduction -- 4-11 The Front Page Example -- 4-12 Estimators of Type BLUUE and BIQUUE of the Front Page Example -- 4-13 BLUUE and BIQUUE of the Front Page Example, Sample Median, Median Absolute Deviation -- 4-14 Alternative Estimation Maximum Likelihood (MALE) -- 4-2 Setup of the Best Linear Uniformly Unbiased Estimator -- 4-21 The Best Linear Uniformly Unbiased Estimation ^ξ of ξ : Σy-BLUUE -- 4-22 The Equivalence Theorem of Gy-LESS and Σy-BLUUE -- 4-3 Setup of the Best Invariant Quadratic Uniformly Unbiased Estimator -- 4-31 Block Partitioning of the Dispersion Matrix and Linear Space Generated by Variance-Covariance Components -- 4-32 Invariant Quadratic Estimation of Variance-Covariance Components of Type IQE -- 4-33 Invariant Quadratic Uniformly Unbiased Estimations of Variance-Covariance Components of Type IQUUE -- 4-34 Invariant Quadratic Uniformly Unbiased Estimationsof One Variance Component (IQUUE) from Σy-BLUUE: HIQUUE -- 4-35 Invariant Quadratic Uniformly Unbiased Estimators of Variance Covariance Components of Helmert Type: HIQUUE Versus HIQE -- 4-36 Best Quadratic Uniformly Unbiased Estimations of One Variance Component: BIQUUE -- 4-37 Simultaneous Determination of First Moment and the Second Central Moment, Inhomogeneous Multilinear Estimation, the E - D Correspondence, Bayes Designwith Moment Estimations.
Chapter 5 The Third Problem of Algebraic Regression -- 5-1 Introduction -- 5-11 The Front Page Example -- 5-12 The Front Page Example in Matrix Algebra -- 5-13 Minimum Norm: Least Squares Solution of the Front Page Example by Means of Additive Rank Partitioning -- 5-14 Minimum Norm: Least Squares Solution of the Front Page Example by Means of Multiplicative Rank Partitioning -- 5-15 The Range R(f) and the Kernel N(f) Interpretation of "MINOLESS" by Three Partitionings -- 5-2 MINOLESS and Related Solutions Like Weighted Minimum Norm-Weighted Least Squares Solutions -- 5-21 The Minimum Norm-Least Squares Solution: "MINOLESS" -- 5-22 (Gx, Gy)-MINOS and Its Generalized Inverse -- 5-23 Eigenvalue Decomposition of (Gx, Gy)-MINOLESS -- 5-24 Notes -- 5-3 The Hybrid Approximation Solution: α-HAPS and Tykhonov-Phillips Regularization -- Chapter 6 The Third Problem of Probabilistic Regression -- 6-1 Setup of the Best Linear Minimum Bias Estimator of Type BLUMBE -- 6-11 Definitions, Lemmas and Theorems -- 6-12 The First Example: BLUMBE Versus BLE, BIQUUE Versus BIQE, Triangular Leveling Network -- 6-2 Setup of the Best Linear Estimators of Type hom BLE, hom S-BLE and hom a-BLE for Fixed Effects -- 6-3 Continuous Networks -- 6-31 Continuous Networks of Second Derivatives Type -- Chapter 7 Overdetermined System of Nonlinear Equations on Curved Manifolds -- 7-1 Introduction -- 7-2 Minimal Geodesic Distance: MINGEODISC -- 7-3 Special Models: From the Circular Normal Distribution to the Oblique Normal Distribution -- 7-31 A Historical Note of the von Mises Distribution -- 7-32 Oblique Map Projection -- 7-33 A Note on the Angular Metric -- 7-4 Case Study -- References -- Chapter 8 The Fourth Problem of Probabilistic Regression -- 8-1 The Random Effect Model -- 8-2 Examples.
Chapter 9 The Fifth Problem of Algebraic Regression: The System of Conditional Equations: Homogeneous and Inhomogeneous Equations: {By = Bi versus -c + By = Bi} -- 9-1 Gy-LESS of a System of a Inconsistent Homogeneous Conditional Equations -- 9-2 Solving a System of Inconsistent Inhomogeneous Conditional Equations -- 9-3 Examples -- Chapter 10 The Fifth Problem of Probabilistic Regression -- 10-1 Inhomogeneous General Linear Gauss-Markov Model (Fixed Effects and Random Effects) -- 10-2 Explicit Representations of Errors in the General Gauss-Markov Model with Mixed Effects -- 10-3 An Example for Collocation -- 10-4 Comments -- Chapter 11 The sixth problem of probabilistic regression -- 11-1 Introduction -- 11-2 The Errors-in-Variables Model and its Symmetry -- 11-3 Least Squares in Linear Errors-in-Variables Models -- 11-31 Naive Least Squares -- 11-32 Total Least Squares TLS -- 11-4 SIMEX and SYMEX -- 11-41 SIMEX -- 11-42 SYMEX -- 11-5 Datum Transformation -- 11-6 Nonlinear Errors-in-Variables Models -- Chapter 12 The Nonlinear Problem of the 3d Datum Transformation and the Procrustes Algorithm -- 12-1 The 3d Datum Transformation and the Procrustes Algorithm -- 12-2 The Variance: Covariance Matrix of the Error Matrix E -- 12-3 References -- Chapter 13 The Sixth Problem of Generalized Algebraic Regression -- 13-1 Variance-Covariance-Component Estimation in the Linear Model Ax + ε = y, y ∉ R(A) -- 13-2 Variance-Covariance-Component Estimation in the Linear Model Bε = By -c, By ∉ R(A) + c -- 13-3 Variance-Covariance-Component Estimation in theLinear Model Ax + ε + Bε = By -c, By ∉ R(A) + c -- 13-4 The Block Structure of Dispersion Matrix D{y} -- Chapter 14 Special Problems of Algebraic Regression and Stochastic Estimation -- 14-1 The Multivariate Gauss-Markov Model: A Special Problem of Probabilistic Regression -- 14-2 n-Way Classification Models.
14-21 A First Example: 1-Way Classification -- 14-22 A Second Example: 2-Way Classification Without Interaction -- 14-23 A Third Example: 2-Way Classification with Interaction -- 14-24 Higher Classifications with Interaction -- 14-3 Dynamical Systems -- Chapter 15 Systems of equations: Hybrid algebraic-numeric solutions -- 15-1 Algebraic, numeric, and hybrid algebraic-numeric -- 15-2 Algebraic solutions: Background -- 15-3 Nonlinear systems of equations: Algebraic methods -- 15-31 Nonlinear Gauss-Markov model: Algebraic solution -- 15-32 Adjustment of the combinatorial subsets -- 15-4 Examples -- 15-5 Hybrid algebraic-numeric methods -- 15-6 Notes -- Chapter 16 Integer Least Squares -- 16-1 Introductory remarks -- 16-2 Model for Positioning -- 16-3 Mixed Integer Linear Model -- 16-4 Integer Least Squares -- 16-41 Simple Rounding Solution -- 16-42 Main Steps -- 16-43 The Closest Vector Problem (CVP) -- 16-44 Reduction -- 16-45 Gram-Schmidt Method -- 16-46 The LLL Algorithm -- 16-47 Babai's Rounding Technique -- Chapter 17 Bayesian Inference -- 17-1 Introduction -- 17-2 Principle of Bayesian Analysis -- 17-21 Sequential Analysis -- 17-22 Hierarchical Bayes Models -- 17-23 Choice of Prior -- 17-24 Bayesian Inference -- 17-3 Univariate Linear Model -- 17-31 Model Assumptions -- 17-32 Normal-inverse-gamma Distribution -- 17-33 Noninformative Prior -- 17-34 Conjugate Prior -- 17-35 Regularized Estimators -- 17-4 Mixed Model -- 17-41 Prior Distribution -- 17-42 Posterior Distribution -- 17-5 Multivariate Linear Model -- 17-51 Normal-inverse-Wishart Distribution -- 17-52 Noninformative Prior -- 17-53 Informative Prior -- 17-6 Computer Intensive Methods -- 17-61 Independent Monte Carlo (MC) -- 17-62 Importance Sampling -- 17-63 Markov Chain Monte Carlo -- 17-64 Gibbs Sampling -- 17-65 Rejection Algorithm -- 17-66 Approximative Bayesian Computation (ABC).
Appendix A Tensor Algebra, Linear Algebra, Matrix Algebra, Multilinear Algebra.
Record Nr. UNINA-9910616381603321
Awange Joseph L. <1969->  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Autore Jiang Jiming
Edizione [Second edition.]
Pubbl/distr/stampa New York, New York ; ; London, England : , : Springer, , [2021]
Descrizione fisica 1 online resource (352 pages) : illustrations
Disciplina 519.5
Collana Springer Series in Statistics
Soggetto topico Mathematical statistics
Linear models (Statistics)
Estadística matemàtica
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 1-0716-1282-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- List of Notations -- 1 Linear Mixed Models: Part I -- 1.1 Introduction -- 1.1.1 Effect of Air Pollution Episodes on Children -- 1.1.2 Genome-Wide Association Study -- 1.1.3 Small Area Estimation of Income -- 1.2 Types of Linear Mixed Models -- 1.2.1 Gaussian Mixed Models -- 1.2.1.1 Mixed ANOVA Model -- 1.2.1.2 Longitudinal Model -- 1.2.1.3 Marginal Model -- 1.2.1.4 Hierarchical Models -- 1.2.2 Non-Gaussian Linear Mixed Models -- 1.2.2.1 Mixed ANOVA Model -- 1.2.2.2 Longitudinal Model -- 1.2.2.3 Marginal Model -- 1.3 Estimation in Gaussian Mixed Models -- 1.3.1 Maximum Likelihood -- 1.3.1.1 Point Estimation -- 1.3.1.2 Asymptotic Covariance Matrix -- 1.3.2 Restricted Maximum Likelihood (REML) -- 1.3.2.1 Point Estimation -- 1.3.2.2 Historical Note -- 1.3.2.3 Asymptotic Covariance Matrix -- 1.4 Estimation in Non-Gaussian Linear Mixed Models -- 1.4.1 Quasi-Likelihood Method -- 1.4.2 Partially Observed Information -- 1.4.3 Iterative Weighted Least Squares -- 1.4.3.1 Balanced Case -- 1.4.3.2 Unbalanced Case -- 1.4.4 Jackknife Method -- 1.4.5 High-Dimensional Misspecified Mixed Model Analysis -- 1.5 Other Methods of Estimation -- 1.5.1 Analysis of Variance Estimation -- 1.5.1.1 Balanced Data -- 1.5.1.2 Unbalanced Data -- 1.5.2 Minimum Norm Quadratic Unbiased Estimation -- 1.6 Notes on Computation and Software -- 1.6.1 Notes on Computation -- 1.6.1.1 Computation of the ML and REML Estimators -- 1.6.1.2 The EM Algorithm -- 1.6.2 Notes on Software -- 1.7 Real-Life Data Examples -- 1.7.1 Analysis of Birth Weights of Lambs -- 1.7.2 Analysis of Hip Replacements Data -- 1.7.3 Analyses of High-Dimensional GWAS Data -- 1.8 Further Results and Technical Notes -- 1.8.1 A Note on Finding the MLE -- 1.8.2 Note on Matrix X Not Being Full Rank -- 1.8.3 Asymptotic Behavior of ML and REML Estimators in Non-Gaussian Mixed ANOVA Models.
1.8.4 Truncated Estimator -- 1.8.5 POQUIM in General -- 1.9 Exercises -- 2 Linear Mixed Models: Part II -- 2.1 Tests in Linear Mixed Models -- 2.1.1 Tests in Gaussian Mixed Models -- 2.1.1.1 Exact Tests -- 2.1.1.2 Optimal Tests -- 2.1.1.3 Likelihood-Ratio Tests -- 2.1.2 Tests in Non-Gaussian Linear Mixed Models -- 2.1.2.1 Empirical Method of Moments -- 2.1.2.2 Partially Observed Information -- 2.1.2.3 Jackknife Method -- 2.1.2.4 Robust Versions of Classical Tests -- 2.2 Confidence Intervals in Linear Mixed Models -- 2.2.1 Confidence Intervals in Gaussian Mixed Models -- 2.2.1.1 Exact Confidence Intervals for Variance Components -- 2.2.1.2 Approximate Confidence Intervals for Variance Components -- 2.2.1.3 Simultaneous Confidence Intervals -- 2.2.1.4 Confidence Intervals for Fixed Effects -- 2.2.2 Confidence Intervals in Non-Gaussian Linear MixedModels -- 2.2.2.1 ANOVA Models -- 2.2.2.2 Longitudinal Models -- 2.3 Prediction -- 2.3.1 Best Prediction -- 2.3.2 Best Linear Unbiased Prediction -- 2.3.2.1 Empirical BLUP -- 2.3.3 Observed Best Prediction -- 2.3.4 Prediction of Future Observation -- 2.3.4.1 Distribution-Free Prediction Intervals -- 2.3.4.2 Standard Linear Mixed Models -- 2.3.4.3 Nonstandard Linear Mixed Models -- 2.3.4.4 A Simulated Example -- 2.3.5 Classified Mixed Model Prediction -- 2.3.5.1 CMMP of Mixed Effects -- 2.3.5.2 CMMP of Future Observation -- 2.3.5.3 CMMP When the Actual Match Does Not Exist -- 2.3.5.4 Empirical Demonstration -- 2.3.5.5 Incorporating Covariate Information in Matching -- 2.3.5.6 More Empirical Demonstration -- 2.3.5.7 Prediction Interval -- 2.4 Model Checking and Selection -- 2.4.1 Model Diagnostics -- 2.4.1.1 Diagnostic Plots -- 2.4.1.2 Goodness-of-Fit Tests -- 2.4.2 Information Criteria -- 2.4.2.1 Selection with Fixed Random Factors -- 2.4.2.2 Selection with Random Factors -- 2.4.3 The Fence Methods.
2.4.3.1 The Effective Sample Size -- 2.4.3.2 The Dimension of a Model -- 2.4.3.3 Unknown Distribution -- 2.4.3.4 Finite-Sample Performance and the Effect of a Constant -- 2.4.3.5 Criterion of Optimality -- 2.4.4 Shrinkage Mixed Model Selection -- 2.5 Bayesian Inference -- 2.5.1 Inference About Variance Components -- 2.5.2 Inference About Fixed and Random Effects -- 2.6 Real-Life Data Examples -- 2.6.1 Reliability of Environmental Sampling -- 2.6.2 Hospital Data -- 2.6.3 Baseball Example -- 2.6.4 Iowa Crops Data -- 2.6.5 Analysis of High-Speed Network Data -- 2.7 Further Results and Technical Notes -- 2.7.1 Robust Versions of Classical Tests -- 2.7.2 Existence of Moments of ML/REML Estimators -- 2.7.3 Existence of Moments of EBLUE and EBLUP -- 2.7.4 The Definition of Σn(θ) in Sect.2.4.1.2 -- 2.8 Exercises -- 3 Generalized Linear Mixed Models: Part I -- 3.1 Introduction -- 3.2 Generalized Linear Mixed Models -- 3.3 Real-Life Data Examples -- 3.3.1 Salamander Mating Experiments -- 3.3.2 A Log-Linear Mixed Model for Seizure Counts -- 3.3.3 Small Area Estimation of Mammography Rates -- 3.4 Likelihood Function Under GLMM -- 3.5 Approximate Inference -- 3.5.1 Laplace Approximation -- 3.5.2 Penalized Quasi-likelihood Estimation -- 3.5.2.1 Derivation of PQL -- 3.5.2.2 Computational Procedures -- 3.5.2.3 Variance Components -- 3.5.2.4 Inconsistency of PQL Estimators -- 3.5.3 Tests of Zero Variance Components -- 3.5.4 Maximum Hierarchical Likelihood -- 3.5.5 Note on Existing Software -- 3.6 GLMM Prediction -- 3.6.1 Joint Estimation of Fixed and Random Effects -- 3.6.1.1 Maximum a Posterior -- 3.6.1.2 Computation of MPE -- 3.6.1.3 Penalized Generalized WLS -- 3.6.1.4 Maximum Conditional Likelihood -- 3.6.1.5 Quadratic Inference Function -- 3.6.2 Empirical Best Prediction -- 3.6.2.1 Empirical Best Prediction Under GLMM -- 3.6.2.2 Model-Assisted EBP.
3.6.3 A Simulated Example -- 3.6.4 Classified Mixed Logistic Model Prediction -- 3.6.5 Best Look-Alike Prediction -- 3.6.5.1 BLAP of a Discrete/Categorical Random Variable -- 3.6.5.2 BLAP of a Zero-Inflated Random Variable -- 3.7 Real-Life Data Example Follow-Ups and More -- 3.7.1 Salamander Mating Data -- 3.7.2 Seizure Count Data -- 3.7.3 Mammography Rates -- 3.7.4 Analysis of ECMO Data -- 3.7.4.1 Prediction of Mixed Effects of Interest -- 3.8 Further Results and Technical Notes -- 3.8.1 More on NLGSA -- 3.8.2 Asymptotic Properties of PQWLS Estimators -- 3.8.3 MSPE of EBP -- 3.8.4 MSPE of the Model-Assisted EBP -- 3.9 Exercises -- 4 Generalized Linear Mixed Models: Part II -- 4.1 Likelihood-Based Inference -- 4.1.1 A Monte Carlo EM Algorithm for Binary Data -- 4.1.1.1 The EM Algorithm -- 4.1.1.2 Monte Carlo EM via Gibbs Sampler -- 4.1.2 Extensions -- 4.1.2.1 MCEM with Metropolis-Hastings Algorithm -- 4.1.2.2 Monte Carlo Newton-Raphson Procedure -- 4.1.2.3 Simulated ML -- 4.1.3 MCEM with i.i.d. Sampling -- 4.1.3.1 Importance Sampling -- 4.1.3.2 Rejection Sampling -- 4.1.4 Automation -- 4.1.5 Data Cloning -- 4.1.6 Maximization by Parts -- 4.1.7 Bayesian Inference -- 4.2 Estimating Equations -- 4.2.1 Generalized Estimating Equations (GEE) -- 4.2.2 Iterative Estimating Equations -- 4.2.3 Method of Simulated Moments -- 4.2.4 Robust Estimation in GLMM -- 4.3 GLMM Diagnostics and Selection -- 4.3.1 A Goodness-of-Fit Test for GLMM Diagnostics -- 4.3.1.1 Tailoring -- 4.3.1.2 χ2-Test -- 4.3.1.3 Application to GLMM -- 4.3.2 Fence Methods for GLMM Selection -- 4.3.2.1 Maximum Likelihood (ML) Model Selection -- 4.3.2.2 Mean and Variance/Covariance (MVC) Model Selection -- 4.3.2.3 Extended GLMM Selection -- 4.3.3 Two Examples with Simulation -- 4.3.3.1 A Simulated Example of GLMM Diagnostics -- 4.3.3.2 A Simulated Example of GLMM Selection.
4.4 Real-Life Data Examples -- 4.4.1 Fetal Mortality in Mouse Litters -- 4.4.2 Analysis of Gc Genotype Data -- 4.4.3 Salamander Mating Experiments Revisited -- 4.4.4 The National Health Interview Survey -- 4.5 Further Results and Technical Notes -- 4.5.1 Proof of Theorem 4.3 -- 4.5.2 Linear Convergence and Asymptotic Properties of IEE -- 4.5.2.1 Linear Convergence -- 4.5.2.2 Asymptotic Behavior of IEEE -- 4.5.3 Incorporating Informative Missing Data in IEE -- 4.5.4 Consistency of MSM Estimator -- 4.5.5 Asymptotic Properties of First- and Second-StepEstimators -- 4.5.6 Further Details Regarding the Fence Methods -- 4.5.6.1 Estimation of σM,M* in Case of Clustered Observations -- 4.5.6.2 Consistency of the Fence -- 4.5.7 Consistency of MLE in GLMM with Crossed Random Effects -- 4.6 Exercises -- A Matrix Algebra -- A.1 Kronecker Products -- A.2 Matrix Differentiation -- A.3 Projection and Related Results -- A.4 Inverse and Generalized Inverse -- A.5 Decompositions of Matrices -- A.6 The Eigenvalue Perturbation Theory -- B Some Results in Statistics -- B.1 Multivariate Normal Distribution -- B.2 Quadratic Forms -- B.3 OP and oP -- B.4 Convolution -- B.5 Exponential Family and Generalized Linear Models -- References -- Index.
Record Nr. UNISA-996466561103316
Jiang Jiming  
New York, New York ; ; London, England : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Autore Jiang Jiming
Edizione [Second edition.]
Pubbl/distr/stampa New York, New York ; ; London, England : , : Springer, , [2021]
Descrizione fisica 1 online resource (352 pages) : illustrations
Disciplina 519.5
Collana Springer Series in Statistics
Soggetto topico Mathematical statistics
Linear models (Statistics)
Estadística matemàtica
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 1-0716-1282-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- List of Notations -- 1 Linear Mixed Models: Part I -- 1.1 Introduction -- 1.1.1 Effect of Air Pollution Episodes on Children -- 1.1.2 Genome-Wide Association Study -- 1.1.3 Small Area Estimation of Income -- 1.2 Types of Linear Mixed Models -- 1.2.1 Gaussian Mixed Models -- 1.2.1.1 Mixed ANOVA Model -- 1.2.1.2 Longitudinal Model -- 1.2.1.3 Marginal Model -- 1.2.1.4 Hierarchical Models -- 1.2.2 Non-Gaussian Linear Mixed Models -- 1.2.2.1 Mixed ANOVA Model -- 1.2.2.2 Longitudinal Model -- 1.2.2.3 Marginal Model -- 1.3 Estimation in Gaussian Mixed Models -- 1.3.1 Maximum Likelihood -- 1.3.1.1 Point Estimation -- 1.3.1.2 Asymptotic Covariance Matrix -- 1.3.2 Restricted Maximum Likelihood (REML) -- 1.3.2.1 Point Estimation -- 1.3.2.2 Historical Note -- 1.3.2.3 Asymptotic Covariance Matrix -- 1.4 Estimation in Non-Gaussian Linear Mixed Models -- 1.4.1 Quasi-Likelihood Method -- 1.4.2 Partially Observed Information -- 1.4.3 Iterative Weighted Least Squares -- 1.4.3.1 Balanced Case -- 1.4.3.2 Unbalanced Case -- 1.4.4 Jackknife Method -- 1.4.5 High-Dimensional Misspecified Mixed Model Analysis -- 1.5 Other Methods of Estimation -- 1.5.1 Analysis of Variance Estimation -- 1.5.1.1 Balanced Data -- 1.5.1.2 Unbalanced Data -- 1.5.2 Minimum Norm Quadratic Unbiased Estimation -- 1.6 Notes on Computation and Software -- 1.6.1 Notes on Computation -- 1.6.1.1 Computation of the ML and REML Estimators -- 1.6.1.2 The EM Algorithm -- 1.6.2 Notes on Software -- 1.7 Real-Life Data Examples -- 1.7.1 Analysis of Birth Weights of Lambs -- 1.7.2 Analysis of Hip Replacements Data -- 1.7.3 Analyses of High-Dimensional GWAS Data -- 1.8 Further Results and Technical Notes -- 1.8.1 A Note on Finding the MLE -- 1.8.2 Note on Matrix X Not Being Full Rank -- 1.8.3 Asymptotic Behavior of ML and REML Estimators in Non-Gaussian Mixed ANOVA Models.
1.8.4 Truncated Estimator -- 1.8.5 POQUIM in General -- 1.9 Exercises -- 2 Linear Mixed Models: Part II -- 2.1 Tests in Linear Mixed Models -- 2.1.1 Tests in Gaussian Mixed Models -- 2.1.1.1 Exact Tests -- 2.1.1.2 Optimal Tests -- 2.1.1.3 Likelihood-Ratio Tests -- 2.1.2 Tests in Non-Gaussian Linear Mixed Models -- 2.1.2.1 Empirical Method of Moments -- 2.1.2.2 Partially Observed Information -- 2.1.2.3 Jackknife Method -- 2.1.2.4 Robust Versions of Classical Tests -- 2.2 Confidence Intervals in Linear Mixed Models -- 2.2.1 Confidence Intervals in Gaussian Mixed Models -- 2.2.1.1 Exact Confidence Intervals for Variance Components -- 2.2.1.2 Approximate Confidence Intervals for Variance Components -- 2.2.1.3 Simultaneous Confidence Intervals -- 2.2.1.4 Confidence Intervals for Fixed Effects -- 2.2.2 Confidence Intervals in Non-Gaussian Linear MixedModels -- 2.2.2.1 ANOVA Models -- 2.2.2.2 Longitudinal Models -- 2.3 Prediction -- 2.3.1 Best Prediction -- 2.3.2 Best Linear Unbiased Prediction -- 2.3.2.1 Empirical BLUP -- 2.3.3 Observed Best Prediction -- 2.3.4 Prediction of Future Observation -- 2.3.4.1 Distribution-Free Prediction Intervals -- 2.3.4.2 Standard Linear Mixed Models -- 2.3.4.3 Nonstandard Linear Mixed Models -- 2.3.4.4 A Simulated Example -- 2.3.5 Classified Mixed Model Prediction -- 2.3.5.1 CMMP of Mixed Effects -- 2.3.5.2 CMMP of Future Observation -- 2.3.5.3 CMMP When the Actual Match Does Not Exist -- 2.3.5.4 Empirical Demonstration -- 2.3.5.5 Incorporating Covariate Information in Matching -- 2.3.5.6 More Empirical Demonstration -- 2.3.5.7 Prediction Interval -- 2.4 Model Checking and Selection -- 2.4.1 Model Diagnostics -- 2.4.1.1 Diagnostic Plots -- 2.4.1.2 Goodness-of-Fit Tests -- 2.4.2 Information Criteria -- 2.4.2.1 Selection with Fixed Random Factors -- 2.4.2.2 Selection with Random Factors -- 2.4.3 The Fence Methods.
2.4.3.1 The Effective Sample Size -- 2.4.3.2 The Dimension of a Model -- 2.4.3.3 Unknown Distribution -- 2.4.3.4 Finite-Sample Performance and the Effect of a Constant -- 2.4.3.5 Criterion of Optimality -- 2.4.4 Shrinkage Mixed Model Selection -- 2.5 Bayesian Inference -- 2.5.1 Inference About Variance Components -- 2.5.2 Inference About Fixed and Random Effects -- 2.6 Real-Life Data Examples -- 2.6.1 Reliability of Environmental Sampling -- 2.6.2 Hospital Data -- 2.6.3 Baseball Example -- 2.6.4 Iowa Crops Data -- 2.6.5 Analysis of High-Speed Network Data -- 2.7 Further Results and Technical Notes -- 2.7.1 Robust Versions of Classical Tests -- 2.7.2 Existence of Moments of ML/REML Estimators -- 2.7.3 Existence of Moments of EBLUE and EBLUP -- 2.7.4 The Definition of Σn(θ) in Sect.2.4.1.2 -- 2.8 Exercises -- 3 Generalized Linear Mixed Models: Part I -- 3.1 Introduction -- 3.2 Generalized Linear Mixed Models -- 3.3 Real-Life Data Examples -- 3.3.1 Salamander Mating Experiments -- 3.3.2 A Log-Linear Mixed Model for Seizure Counts -- 3.3.3 Small Area Estimation of Mammography Rates -- 3.4 Likelihood Function Under GLMM -- 3.5 Approximate Inference -- 3.5.1 Laplace Approximation -- 3.5.2 Penalized Quasi-likelihood Estimation -- 3.5.2.1 Derivation of PQL -- 3.5.2.2 Computational Procedures -- 3.5.2.3 Variance Components -- 3.5.2.4 Inconsistency of PQL Estimators -- 3.5.3 Tests of Zero Variance Components -- 3.5.4 Maximum Hierarchical Likelihood -- 3.5.5 Note on Existing Software -- 3.6 GLMM Prediction -- 3.6.1 Joint Estimation of Fixed and Random Effects -- 3.6.1.1 Maximum a Posterior -- 3.6.1.2 Computation of MPE -- 3.6.1.3 Penalized Generalized WLS -- 3.6.1.4 Maximum Conditional Likelihood -- 3.6.1.5 Quadratic Inference Function -- 3.6.2 Empirical Best Prediction -- 3.6.2.1 Empirical Best Prediction Under GLMM -- 3.6.2.2 Model-Assisted EBP.
3.6.3 A Simulated Example -- 3.6.4 Classified Mixed Logistic Model Prediction -- 3.6.5 Best Look-Alike Prediction -- 3.6.5.1 BLAP of a Discrete/Categorical Random Variable -- 3.6.5.2 BLAP of a Zero-Inflated Random Variable -- 3.7 Real-Life Data Example Follow-Ups and More -- 3.7.1 Salamander Mating Data -- 3.7.2 Seizure Count Data -- 3.7.3 Mammography Rates -- 3.7.4 Analysis of ECMO Data -- 3.7.4.1 Prediction of Mixed Effects of Interest -- 3.8 Further Results and Technical Notes -- 3.8.1 More on NLGSA -- 3.8.2 Asymptotic Properties of PQWLS Estimators -- 3.8.3 MSPE of EBP -- 3.8.4 MSPE of the Model-Assisted EBP -- 3.9 Exercises -- 4 Generalized Linear Mixed Models: Part II -- 4.1 Likelihood-Based Inference -- 4.1.1 A Monte Carlo EM Algorithm for Binary Data -- 4.1.1.1 The EM Algorithm -- 4.1.1.2 Monte Carlo EM via Gibbs Sampler -- 4.1.2 Extensions -- 4.1.2.1 MCEM with Metropolis-Hastings Algorithm -- 4.1.2.2 Monte Carlo Newton-Raphson Procedure -- 4.1.2.3 Simulated ML -- 4.1.3 MCEM with i.i.d. Sampling -- 4.1.3.1 Importance Sampling -- 4.1.3.2 Rejection Sampling -- 4.1.4 Automation -- 4.1.5 Data Cloning -- 4.1.6 Maximization by Parts -- 4.1.7 Bayesian Inference -- 4.2 Estimating Equations -- 4.2.1 Generalized Estimating Equations (GEE) -- 4.2.2 Iterative Estimating Equations -- 4.2.3 Method of Simulated Moments -- 4.2.4 Robust Estimation in GLMM -- 4.3 GLMM Diagnostics and Selection -- 4.3.1 A Goodness-of-Fit Test for GLMM Diagnostics -- 4.3.1.1 Tailoring -- 4.3.1.2 χ2-Test -- 4.3.1.3 Application to GLMM -- 4.3.2 Fence Methods for GLMM Selection -- 4.3.2.1 Maximum Likelihood (ML) Model Selection -- 4.3.2.2 Mean and Variance/Covariance (MVC) Model Selection -- 4.3.2.3 Extended GLMM Selection -- 4.3.3 Two Examples with Simulation -- 4.3.3.1 A Simulated Example of GLMM Diagnostics -- 4.3.3.2 A Simulated Example of GLMM Selection.
4.4 Real-Life Data Examples -- 4.4.1 Fetal Mortality in Mouse Litters -- 4.4.2 Analysis of Gc Genotype Data -- 4.4.3 Salamander Mating Experiments Revisited -- 4.4.4 The National Health Interview Survey -- 4.5 Further Results and Technical Notes -- 4.5.1 Proof of Theorem 4.3 -- 4.5.2 Linear Convergence and Asymptotic Properties of IEE -- 4.5.2.1 Linear Convergence -- 4.5.2.2 Asymptotic Behavior of IEEE -- 4.5.3 Incorporating Informative Missing Data in IEE -- 4.5.4 Consistency of MSM Estimator -- 4.5.5 Asymptotic Properties of First- and Second-StepEstimators -- 4.5.6 Further Details Regarding the Fence Methods -- 4.5.6.1 Estimation of σM,M* in Case of Clustered Observations -- 4.5.6.2 Consistency of the Fence -- 4.5.7 Consistency of MLE in GLMM with Crossed Random Effects -- 4.6 Exercises -- A Matrix Algebra -- A.1 Kronecker Products -- A.2 Matrix Differentiation -- A.3 Projection and Related Results -- A.4 Inverse and Generalized Inverse -- A.5 Decompositions of Matrices -- A.6 The Eigenvalue Perturbation Theory -- B Some Results in Statistics -- B.1 Multivariate Normal Distribution -- B.2 Quadratic Forms -- B.3 OP and oP -- B.4 Convolution -- B.5 Exponential Family and Generalized Linear Models -- References -- Index.
Record Nr. UNINA-9910484963903321
Jiang Jiming  
New York, New York ; ; London, England : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Linear models and design / / Jay H. Beder
Linear models and design / / Jay H. Beder
Autore Beder Jay H.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (358 pages)
Disciplina 519.5
Soggetto topico Linear models (Statistics)
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 9783031081767
9783031081750
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910631096703321
Beder Jay H.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Linear models and design / / Jay H. Beder
Linear models and design / / Jay H. Beder
Autore Beder Jay H.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (358 pages)
Disciplina 519.5
Soggetto topico Linear models (Statistics)
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 9783031081767
9783031081750
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996499870203316
Beder Jay H.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Multivariate, multilinear and mixed linear models / / Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen, editors
Multivariate, multilinear and mixed linear models / / Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (357 pages)
Disciplina 519.535
Collana Contributions to Statistics
Soggetto topico Multivariate analysis
Mathematical statistics
Linear models (Statistics)
Anàlisi multivariable
Estadística matemàtica
Models lineals (Estadística)
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-75494-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466403403316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Multivariate, multilinear and mixed linear models / / Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen, editors
Multivariate, multilinear and mixed linear models / / Katarzyna Filipiak, Augustyn Markiewicz, Dietrich von Rosen, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (357 pages)
Disciplina 519.535
Collana Contributions to Statistics
Soggetto topico Multivariate analysis
Mathematical statistics
Linear models (Statistics)
Anàlisi multivariable
Estadística matemàtica
Models lineals (Estadística)
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-75494-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910502617303321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Visualizing linear models / / W. D. Brinda
Visualizing linear models / / W. D. Brinda
Autore Brinda W. D.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (175 pages) : illustrations
Disciplina 519.5
Soggetto topico Linear models (Statistics)
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 3-030-64167-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466544203316
Brinda W. D.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Visualizing linear models / / W. D. Brinda
Visualizing linear models / / W. D. Brinda
Autore Brinda W. D.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (175 pages) : illustrations
Disciplina 519.5
Soggetto topico Linear models (Statistics)
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 3-030-64167-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484467103321
Brinda W. D.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui