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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->
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Applications of Linear and Nonlinear Models : Fixed Effects, Random Effects, and Total Least Squares / / by Erik W. Grafarend, Silvelyn Zwanzig, Joseph L. Awange
| Applications of Linear and Nonlinear Models : Fixed Effects, Random Effects, and Total Least Squares / / by Erik W. Grafarend, Silvelyn Zwanzig, Joseph L. Awange |
| Autore | Awange Joseph L. <1969-> |
| Edizione | [2nd ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (1127 pages) |
| Disciplina |
550
550.015118 |
| Collana | Springer Geophysics |
| Soggetto topico |
Geology
Algebras, Linear Statistics Surveying Linear Algebra Statistical Theory and Methods Geofísica Models lineals (Estadística) Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030945985
3030945987 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | The First Problem of Algebraic Regression -- The First problem of probabilistic regression - the bias problem -- The second problem of algebraic regression - inconsistent system of linear observational equations -- The second problem of probabilistic regression- special Gauss-Markov model without datum defect - Setup of BLUUE for the moments of first order and of BIQUUE for the central moment of second order -- The third problem of probabilistic regression - special Gauss - Markov model with datum problem -Setup of BLUMBE and BLE for the moments of first order and of BIQUUE and BIQE for the central moment of second order. |
| Record Nr. | UNINA-9910616381603321 |
|
Awange Joseph L. <1969->
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Generalized Linear Mixed Models with Applications in Agriculture and Biology / / by Josafhat Salinas Ruíz, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, Jose Crossa Hiriart
| Generalized Linear Mixed Models with Applications in Agriculture and Biology / / by Josafhat Salinas Ruíz, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, Jose Crossa Hiriart |
| Autore | Salinas Ruíz Josafhat |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Springer International Publishing, 2023 |
| Descrizione fisica | 1 online resource (434 pages) |
| Disciplina | 570.15195 |
| Altri autori (Persone) |
Montesinos LópezOsval Antonio
Hernández RamírezGabriela Crossa HiriartJose |
| Soggetto topico |
Biometry
Multivariate analysis Regression analysis Agriculture Biostatistics Multivariate Analysis Linear Models and Regression Models lineals (Estadística) Agricultura Estadística matemàtica Biometria |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031328008
3031328000 |
| Classificazione | MAT029000SCI086000TEC003000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1) Elements of the Generalized Linear Mixed Models -- Chapter 2) Generalized Linear Models -- Chapter 3) Objectives in Model Inference -- Chapter 4) Generalized Linear Mixed Models for non-normal responses -- Chapter 5) Generalized Linear Mixed Models for Count response -- Chapter 6) Generalized Linear Mixed Models for Proportions and Percentages response -- Chapter 7) Times of occurrence of an event of interest -- Chapter 8) Generalized Linear Mixed Models for Categorial and Ordinal responses -- Chapter 9) Generalized Linear Mixed Models for Repeated Measurements. |
| Record Nr. | UNINA-9910853993903321 |
|
Salinas Ruíz Josafhat
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| Springer International Publishing, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Jiang Jiming
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| New York, New York ; ; London, England : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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.
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Linear Models and Design / / by Jay H. Beder
| Linear Models and Design / / by Jay H. Beder |
| Autore | Beder Jay H. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (358 pages) |
| Disciplina |
519.5
519.535 |
| Soggetto topico |
Statistics
Experimental design Biometry Statistical Theory and Methods Design of Experiments Biostatistics 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.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Minimum Gamma-Divergence for Regression and Classification Problems / / by Shinto Eguchi
| Minimum Gamma-Divergence for Regression and Classification Problems / / by Shinto Eguchi |
| Autore | Eguchi Shinto |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (212 pages) |
| Disciplina | 519.5 |
| Collana | JSS Research Series in Statistics |
| Soggetto topico |
Statistics
Stochastic models Mathematical statistics Machine learning Regression analysis Biometry Statistical Theory and Methods Stochastic Modelling in Statistics Parametric Inference Machine Learning Linear Models and Regression Biostatistics Estadística Estadística matemàtica Aprenentatge automàtic Anàlisi de regressió Biometria Models lineals (Estadística) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 9789819788804 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Framework of gamma-divergence -- 2.1. Scale invariance -- 2.2 GM divergence and HM divergence -- 3. Minimum divergence methods for generalized linear models -- 3.1. Bernoulli logistic model -- 3.2. Poisson log-linear model -- 3.3. Poisson point process model -- 4. Minimum divergence methods in machine leaning -- 4.1. Multi-class AdaBoost -- 4.2. Boltzmann machine -- 5. gamma-divergence for real valued functions -- 6. Discussion. |
| Record Nr. | UNINA-9910986137903321 |
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Eguchi Shinto
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 | ||
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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.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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