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1. Linear Model Theory : Exercises and Solutions. 1st ed. 2020
by Dale L. Zimmerman
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-52074-8
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1 A Brief Introduction
2 Selected Matrix Algebra Topics and Results
3 Generalized Inverses and Solutions to Sytems of Linear Equations
4 Moments of a Random Vector and of Linear and Quadratic Forms in a Random Vector
5 Types of Linear Models
6 Estimability
7 Least Squares Estimation for the Gauss-Markov Model
8 Least Squares Geometry and the Overall ANOVA
9 Least Squares Estimation and ANOVA for Partitioned Models
10 Constrained Least Squares Estimation and ANOVA
11 Best Linear Unbiased Estimation for the Aitken Model
12 Model Misspecification
13 Best Linear Unbiased Prediction
14 Distribution Theory
15 Inference for Estimable and Predictable Functions
16 Inference for Variance-Covariance Parameters
17 Empirical BLUE and BLUP
1 A Brief Introduction
2 Selected Matrix Algebra Topics and Results
3 Generalized Inverses and Solutions to Sytems of Linear Equations
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2. Quantitative Psychology : 84th Annual Meeting of the Psychometric Society, Santiago, Chile, 2019. 1st ed. 2020
edited by Marie Wiberg, Dylan Molenaar, Jorge González, Ulf Böckenholt, Jee-Seon Kim
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Proceedings in Mathematics & Statistics ; 322
オンライン: https://doi.org/10.1007/978-3-030-43469-4
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Chapter 1: Stories Of Successful Careers In Psychometrics And What We Can Learn From Them
Chapter 2: Developing A Concept Map For Rasch Measurement Theory
Chapter 3: Person Parameter Estimation For IRT Models Of Forced-Choice Data: Merits And Perils Of Pseudo-Likelihood Approaches
Chapter 4: An Extended Item Response Tree Model For Wording Effects In Mixed-Format Scales
Chapter 5: The Four-Parameter Normal Ogive Model With Response Time
Chapter 6: A Bayesian Graphical And Probabilistic Proposal For Bias Analysis
Chapter 7: Comparing Hyperprior Distributions To Estimate Variance Components For Interrater Reliability Coefficients
Chapter 8: A Hierarchical Joint Model For Bounded Response Time And Response Accuracy
Chapter 9: Selecting A Presmoothing Model In Kernel Equating
Chapter 10: Practical Implementation Of Test Equating Using R
Chapter 11: Predictive Validity Under Partial Observability
Chapter 12: Multiple-Group Propensity Score Inverse Weights Trimming And Its Impact On Covariate Balance And On Bias In Treatment Effect Estimation
Chapter 13: Procrustes Penalty Function For Matching Matrices To Targets With Its Applications
Chapter 14: Factor Score Estimation From The Perspective Of Item Response Theory
Chapter 15: On The Precision Matrix In The Semi-High Dimensional Settings
Chapter 16: The Performance Of The Modified Continuous A-Stratification Indices In Computerized Adaptive Testing
Chapter 17: Constant CSEM Achieved Through Scale Transformation And Adaptive Testing
Chapter 18: Synergized Bootstrapping: The Whole Is Faster Than The Sum Of Its Parts
Chapter 19: Synchronized Time Profile With Applications To Nearest Neighbor Classification
Chapter 20: Topic Modeling Of Constructed-Response Answers On Social Study Assessments
Chapter 21: Impact Of Measurement Bias On Diagnostic Clinical Measures
Chapter 22: Reliability And Structure Validity Of A Teacher Pedagogical Competencies Scale: A Case Study From Chile
Chapter 23: Psychoperiscope
Chapter 24: Modeling Household Food Insecurity With A Polytomous Rasch Model
Chapter 25: Classical Perspectives Of Controlling Acquiescence With Balanced Scales
Chapter 26: Testing Heterogeneity In Inter-Rater Reliability
Chapter 27: An Application Of Regularized Extended Redundancy Analysis Via Generalized Redundancy Analysis Via Generalized Estimating Equations To The Study Of Co-Occurring Substance Use Among US Adults
Chapter 28: Permutation Test Of Regression Coefficients In Social Network Data Analysis
Index
Chapter 1: Stories Of Successful Careers In Psychometrics And What We Can Learn From Them
Chapter 2: Developing A Concept Map For Rasch Measurement Theory
Chapter 3: Person Parameter Estimation For IRT Models Of Forced-Choice Data: Merits And Perils Of Pseudo-Likelihood Approaches
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3. Bayesian Compendium. 1st ed. 2020
by Marcel van Oijen
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-55897-0
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Preface
1 Introduction to Bayesian thinking
2 Introduction to Bayesian science
3 Assigning a prior distribution
4 Assigning a likelihood function
5 Deriving the posterior distribution
6 Sampling from any distribution by MCMC
7 Sampling from the posterior distribution by MCMC
8 Twelve ways to fit a straight line
9 MCMC and complex models
10 Bayesian calibration and MCMC: Frequently asked questions
11 After the calibration: Interpretation, reporting, visualization
2 Model ensembles: BMC and BMA
13 Discrepancy
14 Gaussian Processes and model emulation
15 Graphical Modelling (GM)
16 Bayesian Hierarchical Modelling (BHM)
17 Probabilistic risk analysis and Bayesian decision theory
18 Approximations to Bayes
19 Linear modelling: LM, GLM, GAM and mixed models
20 Machine learning
21 Time series and data assimilation
22 Spatial modelling and scaling error
23 Spatio-temporal modelling and adaptive sampling
24 What next?
Appendix 1: Notation and abbreviations
Appendix 2: Mathematics for modellers
Appendix 3: Probability theory for modellers
Appendix 4: R
Appendix 5: Bayesian software
Preface
1 Introduction to Bayesian thinking
2 Introduction to Bayesian science
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4. Monte Carlo and Quasi-Monte Carlo Methods : MCQMC 2018, Rennes, France, July 1–6. 1st ed. 2020
edited by Bruno Tuffin, Pierre L'Ecuyer
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Proceedings in Mathematics & Statistics ; 324
オンライン: https://doi.org/10.1007/978-3-030-43465-6
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Part I Invited Talks, H. Dong and M. K. Nakayama, A Tutorial on Quantile Estimation via Monte Carlo
L. Herrmann and C. Schwab, Multilevel Quasi-Monte Carlo Uncertainty Quantification for Advection-Diffusion-Reaction
B. L. Nelson, Selecting the Best Simulated System: Thinking Differently About an Old Problem
F. Pillichshammer, Discrepancy of Digital Sequences: New Results on a Classical QMC Topic
Part II Regular Talks, L. Bian, T. Cui, G. Sofronov and J. Keith, Network Structure Change Point Detection by Posterior Predictive Discrepancy
M. Billaud-Friess, A. Macherey, A. Nouy and C. Prieur, Stochastic Methods for Solving High-Dimensional Partial Differential Equations
N. Binder and A. Keller, Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete or Piecewise Constant Probability Distributions
Y. Ding, F. J. Hickernell, and L. A. J. Rugama, An Adaptive Algorithm Employing Continuous Linear Functionals
A. Ebert, P. Kritzer and D. Nuyens, Constructing QMC Finite Element Methods for Elliptic PDEs with Random Coefficients by a Reduced CBC Construction
R. El Haddad, J. El Maalouf, C. Lecot and P. L’Ecuyer, Sudoku Latin, Square Sampling for Markov Chain Simulation
T. Hartung, K. Jansen, H. Leovey, and J. Volmer, Avoiding the Sign Problem in Lattice Field Theory
R. Hofer, On Hybrid Point Sets Stemming from Halton-Type Hammersley Point Sets and Polynomial Lattice Point Sets
M. Huber, Robust Estimation of the Mean with Bounded Relative Standard Deviation
H. Hult, P. Nyquist and C. Ringqvist, Infinite Swapping Algorithm for Training Restricted Boltzmann Machines
I. Iscoe and A. Kreinin, Sensitivity Ranks by Monte Carlo
R. Kritzinger and F. Pillichshammer, Lower Bounds on the Lp Discrepancy of Digital NUT Sequences
H. Leovey and W. Romisch, Randomized QMC Methods for Mixed-Integer Two-Stage Stochastic Programs with Application to Electricity Optimization
A. F. Lopez-Lopera, F. Bachoc, N. Durrande, J. Rohmer, D. Idier and O. Roustant, Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
E. Løvbak, G. Samaey and S. Vandewalle, A Multilevel Monte Carlo Asymptotic-Preserving Particle Method for Kinetic Equations in the Diffusion Limit
D. Mandel and G. Okten, Randomized Global Sensitivity Analysis and Model Robustness
A. Petersson, Rapid Covariance-Based Sampling of Linear SPDE Approximations in the Multilevel Monte Carlo Method
A. Stein and A. Barth, A Multilevel Monte Carlo Algorithm for Parabolic Advection-Diffusion Problems with Discontinuous Coefficients
T. A. Stepanyuk, Estimates For Logarithmic and Riesz Energies Of Spherical t-Designs
Y. Suzuki and D. Nuyens, Rank-1 Lattices and Higher-Order Exponential Splitting for the Time-Dependent Schr¨odinger Equation
C. von Hallern and A. Roßler, An Analysis of the Milstein Scheme for SPDEs without a Commutative Noise Condition
Fei Xie, M. B. Giles, and Zhijian He, QMC Sampling from Empirical Datasets
Part I Invited Talks, H. Dong and M. K. Nakayama, A Tutorial on Quantile Estimation via Monte Carlo
L. Herrmann and C. Schwab, Multilevel Quasi-Monte Carlo Uncertainty Quantification for Advection-Diffusion-Reaction
B. L. Nelson, Selecting the Best Simulated System: Thinking Differently About an Old Problem
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5. Modelli Lineari Generalizzati. 1st ed. 2020
by Alessandra Salvan, Nicola Sartori, Luigi Pace
出版情報: Milano : Springer Milan : Imprint: Springer, 2020
シリーズ名: La Matematica per il 3+2 ; 124
オンライン: https://doi.org/10.1007/978-88-470-4002-1
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1. Modelli lineari e lineari generalizzati
2. Modelli lineari generalizzati
3. Modelli per dati bancari
4. Modelli per risposte politomiche
5. Modelli per dati di conteggio
6. Quasi-verosimiglianza
Modelli per risposte correlate
A Dati utilizzati nel testo
B Distribuzioni di probabilità
C Eguaglianza tra stime OLS e GLS
D Il metodo delta
E Funzioni generatrici
F Codice R per l’esempio 2.9
G Equivalenza tra residui di Pearson e di devianza
H Modelli per la sovradispersione: schema
1. Modelli lineari e lineari generalizzati
2. Modelli lineari generalizzati
3. Modelli per dati bancari
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6. Chemometrics with R : Multivariate Data Analysis in the Natural and Life Sciences. 2nd ed. 2020
by Ron Wehrens
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2020
シリーズ名: Use R! ;
オンライン: https://doi.org/10.1007/978-3-662-62027-4
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Introduction. - Data
Preprocessing
Principal Component Analysis
Self-Organizing Maps. - Clustering
Classification
Multivariate Regression. - Validation
Variable Selection
Chemometric Applications
Introduction. - Data
Preprocessing
Principal Component Analysis
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7. Advanced Statistics for Testing Assumed Causal Relationships : Multiple Regression Analysis Path Analysis Logistic Regression Analysis. 1st ed. 2020
by Hooshang Nayebi
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: University of Tehran Science and Humanities Series ;
オンライン: https://doi.org/10.1007/978-3-030-54754-7
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目次情報:
1. Multiple Regression Analysis
2. Path Analysis
3. Logistic Regression Analysis
1. Multiple Regression Analysis
2. Path Analysis
3. Logistic Regression Analysis
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8. Primer for Data Analytics and Graduate Study in Statistics. 1st ed. 2020
by Douglas Wolfe, Grant Schneider
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-47479-9
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9. Analytical Methods in Statistics : AMISTAT, Liberec, Czech Republic, September 2019. 1st ed. 2020
edited by Matúš Maciak, Michal Pešta, Martin Schindler
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Proceedings in Mathematics & Statistics ; 329
オンライン: https://doi.org/10.1007/978-3-030-48814-7
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Preface
Y. Güney, J. Jurečková and O. Arslan, Averaged Autoregression Quantiles in Autoregressive Model
J. Kalina and P. Vidnerová, Regression Neural Networks with a Highly Robust Loss Function
H. L. Koul and P. Geng, Weighted Empirical Minimum Distance Estimators in Berkson Measurement Error Regression Models
M. Maciak, M. Pešta and S. Vitali, Implied Volatility Surface Estimation via Quantile Regularization
I. Mizera, A remark on the Grenander estimator
U. Radojičić and K. Nordhausen, Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace
P. Vidnerová, J. Kalina and Y. Güney, A Comparison of Robust Model Choice Criteria within a Metalearning Study
S. Zwanzig and R. Ahmad, On Parameter Estimation for High Dimensional Errors-in-Variables Models
Preface
Y. Güney, J. Jurečková and O. Arslan, Averaged Autoregression Quantiles in Autoregressive Model
J. Kalina and P. Vidnerová, Regression Neural Networks with a Highly Robust Loss Function
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10. Fundamentals of Data Analytics : With a View to Machine Learning. 1st ed. 2020
by Rudolf Mathar, Gholamreza Alirezaei, Emilio Balda, Arash Behboodi
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-56831-3
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1 Introduction
2 Prerequisites from Matrix Analysis
3 Multivariate Distributions and Moments
4 Dimensionality Reduction
5 Classification and Clustering
6 Support Vector Machines
7 Machine Learning
Index
1 Introduction
2 Prerequisites from Matrix Analysis
3 Multivariate Distributions and Moments