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1. 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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2. Monte Carlo Methods. 1st ed. 2020
by Adrian Barbu, Song-Chun Zhu
出版情報: Singapore : Springer Singapore : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-981-13-2971-5
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1 Introduction to Monte Carlo Methods
2 Sequential Monte Carlo
3 Markov Chain Monte Carlo - the Basics
4 Metropolis Methods and Variants
5 Gibbs Sampler and its Variants
6 Cluster Sampling Methods
7 Convergence Analysis of MCMC
8 Data Driven Markov Chain Monte Carlo
9 Hamiltonian and Langevin Monte Carlo
10 Learning with Stochastic Gradient
11 Mapping the Energy Landscape
1 Introduction to Monte Carlo Methods
2 Sequential Monte Carlo
3 Markov Chain Monte Carlo - the Basics
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3. Recent Developments in Multivariate and Random Matrix Analysis : Festschrift in Honour of Dietrich von Rosen. 1st ed. 2020
edited by Thomas Holgersson, Martin Singull
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-56773-6
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4. Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. 1st ed. 2020
by Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Emerging Topics in Statistics and Biostatistics ;
オンライン: https://doi.org/10.1007/978-3-030-48904-5
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1. Introduction to Binary Regression Models
2. Generalized Estimating Equations Binary Models
3. Lai and Small Models for Time-Dependent Covariates
4. Lalonde, wilson, and Yin Models for Time-Dependent Covariates
5. Irimata, Broatch, and Wilson Models for Time-Dependent Covariates
6. Bayesian GMM to IBW Method of Analysis
7. Models for Joint Responses for Time-Dependent Covariates
8. Other Models for Time-Dependent Covariates
1. Introduction to Binary Regression Models
2. Generalized Estimating Equations Binary Models
3. Lai and Small Models for Time-Dependent Covariates
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5. Singular Spectrum Analysis for Time Series. 2nd ed. 2020
by Nina Golyandina, Anatoly Zhigljavsky
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2020
シリーズ名: SpringerBriefs in Statistics ;
オンライン: https://doi.org/10.1007/978-3-662-62436-4
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1 Introduction
1.1 Overview of SSA methodology and the structure of the book
1.2 SSA and other techniques
1.3 Computer implementation of SSA
1.4 Historical and bibliographical remarks
1.5 Common symbols and acronyms
2 Basic SSA - 2.1 The main algorithm
2.2 Potential of Basic SSA
2.3 Models of time series and SSA objectives
2.4 Choice of parameters in Basic SSA
2.5 Some variations of Basic SSA
2.6 Multidimensional and multivariate extensions of SSA
3 SSA for forecasting, interpolation, filtering and estimation
3.1 SSA forecasting algorithms
3.2 LRR and associated characteristic polynomials
3.3 Recurrent forecasting as approximate continuation
3.4 Confidence bounds for the forecasts
3.5 Summary and recommendations on forecasting parameters
3.6 Case study: ‘Fortified wine’
3.7 Imputation of missing values
3.8 Subspace-based methods and estimation of signal parameters
3.9 SSA and filters
3.10 Multidimensional/Multivariate SSA
1 Introduction
1.1 Overview of SSA methodology and the structure of the book
1.2 SSA and other techniques
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6. Functional and High-Dimensional Statistics and Related Fields. 1st ed. 2020
edited by Germán Aneiros, Ivana Horová, Marie Hušková, Philippe Vieu
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Contributions to Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-47756-1
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Preface
List of Contributors
1 An introduction to the (postponed) 5th edition of the International Workshop on Functional and Operatorial Statistics
2 Analysis of Telecom Italia Mobile Phone Data by Space-time Regression with Differential Regularization
3 Some Numerical Test on the Convergence Rates of Regression with Differential Regularization
4 Learning with Signatures
5 About the Complexity Function in Small-ball Probability Factorization
6 Principal Components Analysis of a Cyclostationary Random Function
7 Level Set and Density Estimation on Manifolds
8 Pseudo-metrics as Interesting Tool in Nonparametric Functional Regression
9 Testing a Specification Form in Single Functional Index Model
10 A New Method for Ordering Functional Data and its Application to Diagnostic Test
11 A Functional Data Analysis Approach to the Estimation of Densities over Complex Regions
12 A Conformal Approach for Distribution-free Prediction of Functional Data
13 G-Lasso Network Analysis for Functional Data
14 Modelling Functional Data with High-dimensional Error Structure
15 Goodness-of-fit Tests for Functional Linear Models Based on Integrated Projections
16 From High-dimensional to Functional Data: Stringing Via Manifold Learning
17 Functional Two-sample Tests Based on Empirical Characteristic Functionals
18 Some Remarks on the Nelson–Siegel Model
19 Modeling the Effect of Recurrent Events on Time-to-event Processes by Means of Functional Data
20 On Robust Training of Regression Neural Networks
21 Simultaneous Inference for Function-valued Parameters: a Fast and Fair Approach
22 Single Functional Index Model under Responses MAR and Dependent Observations
23 O2S2 for the Geodata Deluge
24 Riemannian Distances between Covariance Operators and Gaussian Processes
25 Depth in Infinite-dimensional Spaces
26 Variable Selection in Semiparametric Bi-functional Models
27 Local Inference for Functional Data Controlling the Functional False Discovery Rate
28 Optimum Scale Selection for 3D Point Cloud Classification through Distance Correlation
29 Generalized Functional Partially Linear Single-index Models
30 Functional Outlier Detection through Probabilistic Modelling
31 Topological Object Data Analysis Methods with an Application to Medical Imaging
32 Distribution-free Pointwise Adjusted %-values for Functional Hypotheses
Authors Index
Preface
List of Contributors
1 An introduction to the (postponed) 5th edition of the International Workshop on Functional and Operatorial Statistics
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7. Introduction to Statistics in Metrology. 1st ed. 2020
by Stephen Crowder, Collin Delker, Eric Forrest, Nevin Martin
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-53329-8
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1. Introduction
2. Basic Concepts
3. The International System of Units (SI), Traceability, and Calibration
4. Introduction to Statistics and Probability
5. Measurement Uncertainty in Decision Making
6. The Measurement Model and Uncertainty
7. Analytical Methods for the Propagation of Uncertainties
8. Monte Carlo Methods for the Propagation of Uncertainties
9. Determining Uncertainties in Fitted Curves
10. Design of Experiments in Metrology
11. Special Topics in Metrology
12. Summary and Acknowledgments
1. Introduction
2. Basic Concepts
3. The International System of Units (SI), Traceability, and Calibration
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8. An Introduction to Sequential Monte Carlo. 1st ed. 2020
by Nicolas Chopin, Omiros Papaspiliopoulos
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Series in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-47845-2
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1 Preface
2 Introduction to state-space models
3 Beyond state-space models
4 Introduction to Markov processes
5 Feynman-Kac models: definition, properties and recursions
6 Finite state-spaces and hidden Markov models
7 Linear-Gaussian state-space models
8 Importance sampling
9 Importance resampling
10 Particle filtering
11 Convergence and stability of particle filters
12 Particle smoothing
13 Sequential quasi-Monte Carlo
14 Maximum likelihood estimation of state-space models
15 Markov chain Monte Carlo
16 Bayesian estimation of state-space models and particle MCMC
17 SMC samplers
18 SMC2, sequential inference in state-space models
19 Advanced topics and open problems
1 Preface
2 Introduction to state-space models
3 Beyond state-space models
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9. Statistical Data Analysis and Entropy. 1st ed. 2020
by Nobuoki Eshima
出版情報: Singapore : Springer Singapore : Imprint: Springer, 2020
シリーズ名: Behaviormetrics: Quantitative Approaches to Human Behavior ; 3
オンライン: https://doi.org/10.1007/978-981-15-2552-0
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Entropy and basic statistics
Analysis of the association in two-way contingency tables
Analysis of the association in multiway contingency tables
Analysis of continuous variables
Entropy and basic statistics
Analysis of the association in two-way contingency tables
Analysis of the association in multiway contingency tables
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10. Identifiability and Regression Analysis of Biological Systems Models : Statistical and Mathematical Foundations and R Scripts. 1st ed. 2020
by Paola Lecca
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: SpringerBriefs in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-41255-5
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1 Complex systems and sets of data
2 Dynamic models
3 Model identifiability
4 Relationships between phenomena
5 Codes
1 Complex systems and sets of data
2 Dynamic models
3 Model identifiability