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Biblioteca

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Advanced linear modeling : multivariate, time series, and spatial data; nonparametric regression and response surface maximization / Ronald Christensen
Advanced linear modeling : multivariate, time series, and spatial data; nonparametric regression and response surface maximization / Ronald Christensen
Autore Christensen, Ronald, 1951-
Edizione [2nd ed.]
Pubbl/distr/stampa New York : Springer, c2001
Descrizione fisica xiii, 398 p. : ill. ; 24 cm.
Disciplina 519.535
Altri autori (Persone) Christensen, Ronald, 1951-author
Collana Springer texts in statistics
Soggetto topico Linear models (Statistics)
ISBN 0387952969 (alk. paper)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001534099707536
Christensen, Ronald, 1951-  
New York : Springer, c2001
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Analysis of generalized linear mixed models in the agricultural and natural resources sciences
Analysis of generalized linear mixed models in the agricultural and natural resources sciences
Autore Gbur, Edward E
Pubbl/distr/stampa [Place of publication not identified], : American Society of Agronomy, 2012
Disciplina 519.5/38
Soggetto topico Agriculture - Statistical methods - Research
Analysis of variance
Linear models (Statistics)
Agriculture
Earth & Environmental Sciences
Agriculture - General
ISBN 0-89118-183-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910555192203321
Gbur, Edward E  
[Place of publication not identified], : American Society of Agronomy, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analysis of generalized linear mixed models in the agricultural and natural resources sciences
Analysis of generalized linear mixed models in the agricultural and natural resources sciences
Autore Gbur, Edward E
Pubbl/distr/stampa [Place of publication not identified], : American Society of Agronomy, 2012
Disciplina 519.5/38
Soggetto topico Agriculture - Statistical methods - Research
Analysis of variance
Linear models (Statistics)
Agriculture
Earth & Environmental Sciences
Agriculture - General
ISBN 0-89118-183-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830706703321
Gbur, Edward E  
[Place of publication not identified], : American Society of Agronomy, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analysis of generalized linear mixed models in the agricultural and natural resources sciences
Analysis of generalized linear mixed models in the agricultural and natural resources sciences
Autore Gbur, Edward E
Pubbl/distr/stampa [Place of publication not identified], : American Society of Agronomy, 2012
Disciplina 519.5/38
Soggetto topico Agriculture - Statistical methods - Research
Analysis of variance
Linear models (Statistics)
Agriculture
Earth & Environmental Sciences
Agriculture - General
ISBN 0-89118-183-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910841234803321
Gbur, Edward E  
[Place of publication not identified], : American Society of Agronomy, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
ANOVA and ANCOVA [[electronic resource] ] : a GLM approach / / Andrew Rutherford
ANOVA and ANCOVA [[electronic resource] ] : a GLM approach / / Andrew Rutherford
Autore Rutherford Andrew <1958->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley, c2011
Descrizione fisica 1 online resource (360 p.)
Disciplina 519.538
Soggetto topico Analysis of variance
Analysis of covariance
Linear models (Statistics)
ISBN 1-118-49168-8
1-283-59290-8
9786613905352
1-118-49171-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ANOVA and ANCOVA A GLM Approach; Contents; Acknowledgments; 1 An Introduction to General Linear Models: Regression, Analysis of Variance, and Analysis of Covariance; 1.1 Regression, Analysis of Variance, and Analysis of Covariance; 1.2 A Pocket History of Regression, ANOVA, and ANCOVA; 1.3 An Outline of General Linear Models (GLMs); 1.3.1 Regression; 1.3.2 Analysis of Variance; 1.3.3 Analysis of Covariance; 1.4 The ""General"" in GLM; 1.5 The ""Linear"" in GLM; 1.6 Least Squares Estimates; 1.7 Fixed, Random, and Mixed Effects Analyses; 1.8 The Benefits of a GLM Approach to ANOVA and ANCOVA
1.9 The GLM Presentation 1.10 Statistical Packages for Computers; 2 Traditional and GLM Approaches to Independent Measures Single Factor ANOVA Designs; 2.1 Independent Measures Designs; 2.2 Balanced Data Designs; 2.3 Factors and Independent Variables; 2.4 An Outline of Traditional ANOVA for Single Factor Designs; 2.5 Variance; 2.6 Traditional ANOVA Calculations for Single Factor Designs; 2.7 Confidence Intervals; 2.8 GLM Approaches to Single Factor ANOVA; 2.8.1 Experimental Design GLMs; 2.8.2 Estimating Effects by Comparing Full and Reduced Experimental Design GLMs; 2.8.3 Regression GLMs
2.8.4 Schemes for Coding Experimental Conditions 2.8.4.1 Dummy Coding; 2.8.4.2 Why Only (p - 1) Variables Are Used to Represent All Experimental Conditions?; 2.8.4.3 Effect Coding; 2.8.5 Coding Scheme Solutions to the Overparameterization Problem; 2.8.6 Cell Mean GLMs; 2.8.7 Experimental Design Regression and Cell Mean GLMs; 3 Comparing Experimental Condition Means, Multiple Hypothesis Testing, Type 1 Error, and a Basic Data Analysis Strategy; 3.1 Introduction; 3.2 Comparisons Between Experimental Condition Means; 3.3 Linear Contrasts; 3.4 Comparison Sum of Squares; 3.5 Orthogonal Contrasts
3.6 Testing Multiple Hypotheses 3.6.1 Type 1 and Type 2 Errors; 3.6.2 Type 1 Error Rate Inflation with Multiple Hypothesis Testing; 3.6.3 Type 1 Error Rate Control and Analysis Power; 3.6.4 Different Conceptions of Type 1 Error Rate; 3.6.4.1 Test wise Type 1 Error Rate; 3.6.4.2 Family wise Type 1 Error Rate; 3.6.4.3 Experiment wise Type 1 Error Rate; 3.6.4.4 False Discovery Rate; 3.6.5 Identifying the ""Family"" in Family wise Type 1 Error Rate Control; 3.6.6 Logical and Empirical Relations; 3.6.6.1 Logical Relations; 3.6.6.2 Empirical Relations; 3.7 Planned and Unplanned Comparisons
3.7.1 Direct Assessment of Planned Comparisons 3.7.2 Contradictory Results with ANOVA Omnibus F-tests and Direct Planned Comparisons; 3.8 A Basic Data Analysis Strategy; 3.8.1 ANOVA First?; 3.8.2 Strong and Weak Type 1 Error Control; 3.8.3 Step wise Tests; 3.8.4 Test Power; 3.9 The Three Basic Stages of Data Analysis; 3.9.1 Stage 1; 3.9.2 Stage 2; 3.9.2.1 Rom's Test; 3.9.2.2 Shaffer's R Test; 3.9.2.3 Applying Shaffer's R Test After a Significant F-test; 3.9.3 Stage 3; 3.10 The Role of the Omnibus F-Test; 4 Measures of Effect Size and Strength of Association, Power, and Sample Size
4.1 Introduction
Record Nr. UNINA-9910141423003321
Rutherford Andrew <1958->  
Hoboken, NJ, : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
ANOVA and ANCOVA [[electronic resource] ] : a GLM approach / / Andrew Rutherford
ANOVA and ANCOVA [[electronic resource] ] : a GLM approach / / Andrew Rutherford
Autore Rutherford Andrew <1958->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley, c2011
Descrizione fisica 1 online resource (360 p.)
Disciplina 519.538
Soggetto topico Analysis of variance
Analysis of covariance
Linear models (Statistics)
ISBN 1-118-49168-8
1-283-59290-8
9786613905352
1-118-49171-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ANOVA and ANCOVA A GLM Approach; Contents; Acknowledgments; 1 An Introduction to General Linear Models: Regression, Analysis of Variance, and Analysis of Covariance; 1.1 Regression, Analysis of Variance, and Analysis of Covariance; 1.2 A Pocket History of Regression, ANOVA, and ANCOVA; 1.3 An Outline of General Linear Models (GLMs); 1.3.1 Regression; 1.3.2 Analysis of Variance; 1.3.3 Analysis of Covariance; 1.4 The ""General"" in GLM; 1.5 The ""Linear"" in GLM; 1.6 Least Squares Estimates; 1.7 Fixed, Random, and Mixed Effects Analyses; 1.8 The Benefits of a GLM Approach to ANOVA and ANCOVA
1.9 The GLM Presentation 1.10 Statistical Packages for Computers; 2 Traditional and GLM Approaches to Independent Measures Single Factor ANOVA Designs; 2.1 Independent Measures Designs; 2.2 Balanced Data Designs; 2.3 Factors and Independent Variables; 2.4 An Outline of Traditional ANOVA for Single Factor Designs; 2.5 Variance; 2.6 Traditional ANOVA Calculations for Single Factor Designs; 2.7 Confidence Intervals; 2.8 GLM Approaches to Single Factor ANOVA; 2.8.1 Experimental Design GLMs; 2.8.2 Estimating Effects by Comparing Full and Reduced Experimental Design GLMs; 2.8.3 Regression GLMs
2.8.4 Schemes for Coding Experimental Conditions 2.8.4.1 Dummy Coding; 2.8.4.2 Why Only (p - 1) Variables Are Used to Represent All Experimental Conditions?; 2.8.4.3 Effect Coding; 2.8.5 Coding Scheme Solutions to the Overparameterization Problem; 2.8.6 Cell Mean GLMs; 2.8.7 Experimental Design Regression and Cell Mean GLMs; 3 Comparing Experimental Condition Means, Multiple Hypothesis Testing, Type 1 Error, and a Basic Data Analysis Strategy; 3.1 Introduction; 3.2 Comparisons Between Experimental Condition Means; 3.3 Linear Contrasts; 3.4 Comparison Sum of Squares; 3.5 Orthogonal Contrasts
3.6 Testing Multiple Hypotheses 3.6.1 Type 1 and Type 2 Errors; 3.6.2 Type 1 Error Rate Inflation with Multiple Hypothesis Testing; 3.6.3 Type 1 Error Rate Control and Analysis Power; 3.6.4 Different Conceptions of Type 1 Error Rate; 3.6.4.1 Test wise Type 1 Error Rate; 3.6.4.2 Family wise Type 1 Error Rate; 3.6.4.3 Experiment wise Type 1 Error Rate; 3.6.4.4 False Discovery Rate; 3.6.5 Identifying the ""Family"" in Family wise Type 1 Error Rate Control; 3.6.6 Logical and Empirical Relations; 3.6.6.1 Logical Relations; 3.6.6.2 Empirical Relations; 3.7 Planned and Unplanned Comparisons
3.7.1 Direct Assessment of Planned Comparisons 3.7.2 Contradictory Results with ANOVA Omnibus F-tests and Direct Planned Comparisons; 3.8 A Basic Data Analysis Strategy; 3.8.1 ANOVA First?; 3.8.2 Strong and Weak Type 1 Error Control; 3.8.3 Step wise Tests; 3.8.4 Test Power; 3.9 The Three Basic Stages of Data Analysis; 3.9.1 Stage 1; 3.9.2 Stage 2; 3.9.2.1 Rom's Test; 3.9.2.2 Shaffer's R Test; 3.9.2.3 Applying Shaffer's R Test After a Significant F-test; 3.9.3 Stage 3; 3.10 The Role of the Omnibus F-Test; 4 Measures of Effect Size and Strength of Association, Power, and Sample Size
4.1 Introduction
Record Nr. UNINA-9910826581903321
Rutherford Andrew <1958->  
Hoboken, NJ, : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
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. 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
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Applied linear regression [[electronic resource] /] / Sanford Weisberg
Applied linear regression [[electronic resource] /] / Sanford Weisberg
Autore Weisberg Sanford <1947->
Edizione [4th ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2014
Descrizione fisica 1 online resource (xvii, 340 p.) : ill
Disciplina 519.536
Collana Wiley series in probability and statistics
Soggetto topico Regression analysis
Linear models (Statistics)
Soggetto genere / forma Electronic books.
ISBN 1-118-78955-5
1-118-59485-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Scatterplots -- 2 Simple Linear Regression -- 3 Multiple Regression -- 4 Interpretation of Main Effects -- 5 Complex Regressors -- 6 Testing and Analysis of Variance -- 7 Variances -- 8 Transformations -- 9 Regression Diagnostics -- 10 Variable Selection -- 11 Nonlinear Regression -- 12 Binomial and Poisson Regression -- A Appendix -- Bibliography -- Index.
Record Nr. UNINA-9910462981403321
Weisberg Sanford <1947->  
Hoboken, N.J., : Wiley, 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
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The catalogue of computational material models : basic geometrically linear models in 1D / / Paul Steinmann; Kenneth Runesson
The catalogue of computational material models : basic geometrically linear models in 1D / / Paul Steinmann; Kenneth Runesson
Autore Steinmann Paul <1885-1953, >
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XII, 402 p. 187 illus.)
Disciplina 519.5
Soggetto topico Linear models (Statistics)
ISBN 3-030-63684-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. Preliminaries -- 3. Elasticity -- 4. Visco-Elasticity -- 5. Plasticity -- 6. Visco-Plasticity.
Record Nr. UNINA-9910484550803321
Steinmann Paul <1885-1953, >  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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