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1. Multivariate Exponential Families: A Concise Guide to Statistical Inference. 1st ed. 2021
by Stefan Bedbur, Udo Kamps
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: SpringerBriefs in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-81900-2
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Introduction
Parametrizations and Basic Properties
Distributional and Statistical Properties
Parameter Estimation
Hypotheses Testing
Exemplary Multivariate Applications
Introduction
Parametrizations and Basic Properties
Distributional and Statistical Properties
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2. Geostatistics for Compositional Data with R. 1st ed. 2021
by Raimon Tolosana-Delgado, Ute Mueller
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: Use R! ;
オンライン: https://doi.org/10.1007/978-3-030-82568-3
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1 Introduction
2 A review of compositional data analysis
3 Exploratory data analysis
4 Exploratory spatial analysis
5 Variogram Models
6 Geostatistical estimation
7 Cross-validation
8 Multivariate normal score transformation
9 Simulation
10 Compositional Direct Sampling Simulation
11 Evaluation and Postprocessing of Results
A Matrix decompositions
B Complete data analysis workflows
Index
1 Introduction
2 A review of compositional data analysis
3 Exploratory data analysis
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3. Modern Quantification Theory : Joint Graphical Display, Biplots, and Alternatives. 1st ed. 2021
by Shizuhiko Nishisato, Eric J. Beh, Rosaria Lombardo, Jose G. Clavel
出版情報: Singapore : Springer Nature Singapore : Imprint: Springer, 2021
シリーズ名: Behaviormetrics: Quantitative Approaches to Human Behavior ; 8
オンライン: https://doi.org/10.1007/978-981-16-2470-4
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Personal Reflections
Mathematical Preliminaries
Data Formats and Geometry
Coordinates for Joint Graphs
Clustering as an Alternative
Scoring and Profiles
Some Generalizations of Reciprocal Averaging
History of the Biplot
Biplots for Variants of Correspondence Analysis
Personal Reflections
Mathematical Preliminaries
Data Formats and Geometry
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4. The Elements of Hawkes Processes. 1st ed. 2021
by Patrick J. Laub, Young Lee, Thomas Taimre
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-84639-8
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Background
Hawes Process Essentials
Simulation Methods
Likelihood Methods
EM Algorithm
Bayesian Methods
Spectral Methods
Goodness of Fit
Traditional Applications
Financial and Actuarial Applications
Biological Applications
Background
Hawes Process Essentials
Simulation Methods
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5. Extreme Value Theory with Applications to Natural Hazards : From Statistical Theory to Industrial Practice. 1st ed. 2021
edited by Nicolas Bousquet, Pietro Bernardara
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-74942-2
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1 E. Garnier: Extreme Events and History: for a better consideration of natural hazards
2 N. Bousquet and P. Bernardara: Introduction
Part I Standard Extreme Value Theory
3 P. Bernardara and N. Bousquet: Probabilistic modeling and statistical quantification of natural hazards
4 N. Bousquet: Fundamental concepts of probability and statistics
5 M. Andreewsky and N. Bousquet: Collecting and analyzing data
6 A. Dutfoy: Univariate extreme value theory: practice and limitations
Part II Elements of Extensive Statistical Analysis
7 J. Weiss and M. Andreewsky: Regional extreme value analysis
8 S. Parey, T. Hoang: Extreme values of non-stationary time series
9 A. Dutfoy: Multivariate extreme value theory: practice and limits
10 S., T. Hoang and N. Bousquet: Stochastic and physics-based simulation of extreme situations
11 N. Bousquet: Bayesian extreme value theory
12 M. Andreewsky, P. Bernardara, N. Bousquet, A. Dutfoy and S. Parey: Perspectives
Part III Detailed Case Studies on Natural Hazards
13 P. Bernardara: Predicting extreme ocean swells
14 M. Andreewsky: Predicting storm surges
15 S. Parey: Forecasting extreme winds
16 N. Roche and A. Dutfoy: Conjunction of rainfall in neighboring watersheds
17 A. Sibler and A. Dutfoy: Conjunction of a flood and a storm
18 E. Paquet: SCHADEX: an alternative to extreme value statistics in hydrology
Appendix A
Appendix B
References
Index
1 E. Garnier: Extreme Events and History: for a better consideration of natural hazards
2 N. Bousquet and P. Bernardara: Introduction
Part I Standard Extreme Value Theory
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6. Statistical Foundations, Reasoning and Inference : For Science and Data Science. 1st ed. 2021
by Göran Kauermann, Helmut Küchenhoff, Christian Heumann
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: Springer Series in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-69827-0
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Introduction
Background in Probability
Parametric Statistical Models
Maximum Likelihood Inference
Bayesian Statistics
Statistical Decisions
Regression
Bootstrapping
Model Selection and Model Averaging
Multivariate and Extreme Value Distributions
Missing and Deficient Data
Experiments and Causality
Introduction
Background in Probability
Parametric Statistical Models
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7. Advances in Compositional Data Analysis : Festschrift in Honour of Vera Pawlowsky-Glahn. 1st ed. 2021
edited by Peter Filzmoser, Karel Hron, Josep Antoni Martín-Fernández, Javier Palarea-Albaladejo
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-71175-7
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Preface
J.J. Egozcue and W.L. Maldonado: An interpretable orthogonal decomposition of positive square matrices
Part I Fundamentals
I. Erb and N. Ay: The information-geometric perspective of compositional data analysis
D.R. Lovell: Log-ratio analysis of finite precision data: caveats, and connections to digital lines and number theory
G. Mateu-Figueras, G.S. Monti and J.J. Egozcue: Distributions on the simplex revisited
J. Graffelman: Compositional biplots: a story of false leads and hidden features revealed by the last dimensions
Part II Statistical Methodology
K. Fačevicová, P. Kynčlová and K. Macků: Geographically weighted regression analysis for two-factorial compositional data
C. Barceló-Vidal and J.A. Martín-Fernández: Factor analysis of compositional data with a total
M. Gallo, V. Simonacci and V. Todorov: A compositional three-way approach for student satisfaction analysis
M. Templ: Artificial neural networks to impute rounded zeros in compositional data
E. Saus–Sala, À. Farreras–Noguer, N. Arimany–Serrat, and G. Coenders: Compositional du pont analysis. A visual tool for strategic financial performance assessment
A. Menafoglio: Spatial statistics for distributional data in Bayes spaces: from object-oriented kriging to the analysis of warping functions
C. Thomas-Agnan, T. Laurent, A. Ruiz-Gazen, N. Thi Huong An, R. Chakir and A. Lungarska: Spatial simultaneous autoregressive models for compositional data: application to land use
Part III Applications
A. Buccianti, C. Gozzi: The whole versus the parts: the challenge of compositional data analysis (CoDA) methods for geochemistry
M.A. Engle and J.A. Chaput: Groundwater origin determination in historic chemical datasets through supervised compositional data analysis: Brines of the Permian Basin, USA
J.M. McKinley, U. Mueller, P.M. Atkinson, U. Ofterdinger, S.F. Cox, R. Doherty, D. Fogarty and J.J. Egozcue
Chronic kidney disease of uncertain aetiology and its relation with waterborne environmental toxins: An investigation via compositional balances
R.A. Olea, J.A. Martín-Fernández and W.H. Craddock: Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers
J.R. Wu, J.M. Macklaim, B.L. Genge and G.B. Gloor: Finding the centre: compositional asymmetry in high-throughput sequencing datasets
L. Huang and H. Li: Bayesian balance-regression in microbiome studies using stochastic search
D.E. McGregor, P.M. Dall, J. Palarea-Albaladejo and S.F.M. Chastin: Compositional data analysis in physical activity and health research. Looking for the right balance
D. Dumuid, Ž. Pedišić, J. Palarea-Albaladejo, J.A. Martín-Fernández, K. Hron and T. Olds: Compositional data analysis in time-use epidemiology
Preface
J.J. Egozcue and W.L. Maldonado: An interpretable orthogonal decomposition of positive square matrices
Part I Fundamentals
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8. Uncertainty in Engineering : Introduction to Methods and Applications. 1st ed. 2022
edited by Louis J. M. Aslett, Frank P. A. Coolen, Jasper De Bock
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名: SpringerBriefs in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-83640-5
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Introduction to Bayesian statistical inference
Sampling from complex probability distributions: a Monte Carlo primer for engineers
Introduction to the theory of imprecise probability
Imprecise discrete-time Markov chains
Statistics with imprecise probabilities – a short survey
Reliability
Simulation methods for the analysis of complex systems
Overview of stochastic model updating in aerospace application under uncertainty treatment
Aerospace flight modeling and experimental testing
Introduction to Bayesian statistical inference
Sampling from complex probability distributions: a Monte Carlo primer for engineers
Introduction to the theory of imprecise probability
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9. Eco-Stats: Data Analysis in Ecology : From t-tests to Multivariate Abundances. 1st ed. 2022
by David I Warton
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名: Methods in Statistical Ecology ;
オンライン: https://doi.org/10.1007/978-3-030-88443-7
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1. "Stats 101" Revision
2. An important equivalence result
3. Regression with multiple predictor variables
4. Linear models – anything goes
5. Model selection
6. Mixed effects models
7. Correlated samples in time, space, phylogeny
8. Wiggly Models
9. Design-based inference
10. Analysing discrete data
11. Multivariate analysis
12. Visualising many responses
13. Allometric line-fitting
14. Multivariate abundances and environmental association
15. Predicting multivariate abundances
16. Explaining variation in response across taxa
17. Studying co-occurrence patterns
18. Closing advice
1. "Stats 101" Revision
2. An important equivalence result
3. Regression with multiple predictor variables
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10. Engineering Statistics : An Introduction. 1st ed. 2022
by Edward B. Magrab
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
オンライン: https://doi.org/10.1007/978-3-031-05010-7
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