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1.

電子ブック

EB
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
出版情報: New York, NY : Springer US : Imprint: Springer, 2021
シリーズ名: Springer Texts in Statistics ;
オンライン: https://doi.org/10.1007/978-1-0716-1418-1
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Preface
1 Introduction
2 Statistical Learning
3 Linear Regression
4 Classification
5 Resampling Methods
6 Linear Model Selection and Regularization
7 Moving Beyond Linearity
8 Tree-Based Methods
9 Support Vector Machines
10 Deep Learning
11 Survival Analysis and Censored Data
12 Unsupervised Learning
13 Multiple Testing
Index
Preface
1 Introduction
2 Statistical Learning
2.

電子ブック

EB
edited by Nobuaki Hoshino, Shuhei Mano, Takaaki Shimura
出版情報: Singapore : Springer Nature Singapore : Imprint: Springer, 2021
シリーズ名: JSS Research Series in Statistics ;
オンライン: https://doi.org/10.1007/978-981-16-0768-4
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Chap. 1 Estimation of generalized beta distributions (Authors: Sibuya and Mano)
Chap. 2 On Some Resampling Procedures with the Empirical Beta Copula (Kiriliouk, Segers and Tsukahara)
Chap. 3 Regression Analysis for Imbalanced Binary Data: Multi-Dimensional Case (Sei)
Chap. 4 An Analysis of Extremes: Semiparametric Efficiency in Regression (Ozeki and Doksum)
Chap. 5 Future Change in Relationships among Extreme Precipitation Statistics Using “d4PDF” (Tanaka)
Chap. 6 History and Perspectives of Hydrological Frequency Analysis in Japan (Takara)
Chap. 1 Estimation of generalized beta distributions (Authors: Sibuya and Mano)
Chap. 2 On Some Resampling Procedures with the Empirical Beta Copula (Kiriliouk, Segers and Tsukahara)
Chap. 3 Regression Analysis for Imbalanced Binary Data: Multi-Dimensional Case (Sei)
3.

電子ブック

EB
by Enrico Bernardi, Silvia Romagnoli
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-64250-1
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Preface
I The Main Ingredients
1 Clustering
2 Copula Function and C-volume
3 Combinatorics and Random Matrices: A Brief Review
II Mixing the Ingredients: A Recipe for a New Aggregation Algorithm
4 Counting a Random Event: Traditional Approach and New Perspectives
5 A New Copula-based Approach for Counting: The Distorted and the Limiting Case
6 Real Data Empirical Applications
Preface
I The Main Ingredients
1 Clustering
4.

電子ブック

EB
by Raosaheb Latpate, Jayant Kshirsagar, Vinod Kumar Gupta, Girish Chandra
出版情報: Singapore : Springer Nature Singapore : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-981-16-0622-9
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-1. Introduction
2. Simple Random Sampling
3. Stratied Random Sampling
4. Cluster Sampling
5. Double Sampling
6. Probability Proportional to Size Sampling
7. Systematic Sampling
8. Resampling Techniques
9. Adaptive Cluster Sampling
10. Two-Stage Adaptive Cluster Sampling
11. Adaptive Cluster Double Sampling
12. Inverse Adaptive Cluster Sampling
13. Two Stage Inverse Adaptive Cluster Sampling
14. Stratified Inverse Adaptive Cluster Sampling
15. Negative Adaptive Cluster Sampling
16. Negative Adaptive Cluster Double Sampling
17. Two- Stage Negative Adaptive Cluster Sampling
18. Balanced and Unbalanced Ranked Set Sampling
19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference
20. Important Versions of Ranked Set Sampling
21. Sampling Errors
-1. Introduction
2. Simple Random Sampling
3. Stratied Random Sampling
5.

電子ブック

EB
edited by Abdelaati Daouia, Anne Ruiz-Gazen
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-73249-3
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6.

電子ブック

EB
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
7.

電子ブック

EB
by Inge S. Helland
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-81923-1
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1. The epistemic view upon science
2. Statistical inference
3. Inference in an epistemic process
4. Towards quantum theory
5. Aspects of quantum theory
6. Macroscopic consequences
1. The epistemic view upon science
2. Statistical inference
3. Inference in an epistemic process
8.

電子ブック

EB
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
9.

電子ブック

EB
by Thomas W. MacFarland, Jan M. Yates
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-62404-0
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1 Introduction: Biostatistics and R
1.1 Purpose of this Text
1.2 Development of Biostatistics
1.3 Development of R
1.4 How R is Used in this Text
1.5 Import Data into R
1.6 Addendum1: Efficient Programming with R, Project Workflow, and Good Programming Practices (gpp)
1.7 Addendum2: Preview of Descriptive Statistics and Graphics Using R
1.8 Addendum3: R and Beautiful Graphics
1.9 Addendum4: Research Designs Used in Biostatistics
1.10 Prepare to Exit, Save, and Later Retrieve this R Session
1.11 External Data and/or Data Resources Used in this Lesson
2 Data Exploration, Descriptive Statistics, and Measures of Central Tendency
2.1 Background
2.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
2.3 Organize the Data and Display the Code Book
2.4 Conduct a Visual Data Check Using Graphics (e.g., Figures)
2.5 Descriptive Statistics for Initial Analysis of the Data
2.6 Quality Assurance, Data Distribution, and Tests for Normality
2.7 Statistical Test(s)
2.8 Summary
2.9 Addendum1: Specialized External Packages and Functions
2.10 Addendum2: Parametric v Nonparametric
2.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns
2.12 Prepare to Exit, Save, and Later Retrieve this R Session
2.13 External Data and/or Data Resources Used in this Lesson
3 Student's t-Test for Independent Samples
3.1 Background
3.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
3.3 Organize the Data and Display the Code Book
3.4 Conduct a Visual Data Check Using Graphics (e.g., Figures)
3.5 Descriptive Statistics for Initial Analysis of the Data
3.6 Quality Assurance, Data Distribution, and Tests for Normality
3.7 Statistical Test(s)
3.8 Summary of Outcomes
3.9 Addendum1: t-Statistic v z-Statistic
3.10 Addendum2: Parametric v Nonparametric
3.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns
3.12 Prepare to Exit, Save, and Later Retrieve This R Session
3.13 External Data and/or Data Resources Used in this Lesson
4 Student's t-Test for Matched Pairs
4.1 Background
4.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
4.3 Organize the Data and Display the Code Book
4.4 Conduct a Visual Data Check Using Graphics(e.g., Figures)
4.5 Descriptive Statistics for Initial Analysis of the Data
4.6 Quality Assurance, Data Distribution, and Tests for Normality
4.7 Statistical Test(s)
4.8 Summary of Outcomes
4.9 Addendum1: R-Based Tools for Unstacked (e.g. Wide) Data
4.10 Addendum2: Stacked Data and Student's t-Test for Matched Pairs
4.11 Addendum 3: The Impact of N on Student's t-Test
4.12 Addendum 4: Parametric v Nonparametric
4.13 Addendum5: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns
4.14 Prepare to Exit, Save, and Later Retrieve This R Session
4.15 External Data and/or Data Resources Used in this Lesson
5 Oneway Analysis of Variance (ANOVA)
5.1 Background
5.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
5.3 Organize the Data and Display the Code Book
5.4 Conduct a Visual Data Check Using Graphics(e.g., Figures)
5.5 Descriptive Statistics for Initial Analysis of the Data
5.6 Quality Assurance, Data Distribution, and Tests for Normality
5.7 Statistical Test(s)
5.8 Summary of Outcomes
5.9 Addendum1: Other Packages for Display of Oneway ANOVA
5.10 Addendum2: Parametric v Nonparametric
5.11 Addendum3: Additional Practice Data Sets
5.12 Prepare to Exit, Save, and Later Retrieve This R Session
5.13 External Data and/or Data Resources Used in this Lesson
6 Twoway Analysis of Variance (ANOVA)
6.1 Background
6.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
6.3 Organize the Data and Display the Code Book
6.4 Conduct a Visual Data Check Using Graphics (e.g., Figures)
6.5 Descriptive Statistics for Initial Analysis of the Data
6.6 Quality Assurance, Data Distribution, and Tests for Normality
6.7 Statistical Test(s)
6.8 Summary of Outcomes
6.9 Addendum 1: Other Packages for Display of Twoway ANOVA
6.10 Addendum 2: Parametric v Nonparametric
6.11 Addendum 3: Additional Practice Data Sets
6.12 Prepare to Exit, Save, and Later Retrieve This R Session
6.13 External Data and/or Data Resources Used in this Lesson
7 Correlation, Association, Regression, Likelihood, and Prediction
7.1 Background
7.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
7.3 Organize the Data and Display the Code Book
7.4 Quality Assurance, Data Distribution, and Tests for Normality
7.5 Statistical Test(s)
7.6 Summary of Outcomes
7.7 Addendum 1: Multiple Regression
7.8 Addendum 2: Likelihood and Odds Ratio
7.9 Addendum 3:Parametric v Nonparametric
7.10 Addendum 4: Additional Practice Data Sets
7.11 Prepare to Exit, Save, and Later Retrieve This R Session
7.12 External Data and/or Data Resources Used in this Lesson
8 Working with Large and Complex Datasets
8.1 Background
8.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions
8.3 Organize the Data and Display the Code Book
8.4 Conduct a Visual Data Check Using Graphics (e.g., Figures)
8.5 Descriptive Statistics for Initial Analysis of the Data
8.6 Quality Assurance, Data Distribution, and Tests for Normality
8.7 Statistical Test(s)
8.8 Summary of Outcomes
8.9 Addendum1: Additional Graphics, to Show Relationships Between and Among Data
8.10 Addendum2: Graphics Using the lattice Package
8.11 Addendum3: Graphics Using the ggplot2 Package
8.12 Addendum 4: Beyond an Introduction to R - Use the tidyverse to Create Subsets of Original Datasets
8.13 Prepare to Exit, Save, and Later Retrieve This R Session
8.14 External Data and/or Data Resources Used in this Lesson
9 Future Actions and Next Steps
9.1 Use of This Text
9.2 R and Beautiful Reporting with R Markdown
9.3 Future Use of R for Biostatistics
9.4 Big Data and Bio Informatics
9.5 External Resources
9.6 Contact the Authors
1 Introduction: Biostatistics and R
1.1 Purpose of this Text
1.2 Development of Biostatistics
10.

電子ブック

EB
by Shailaja Deshmukh, Madhuri Kulkarni
出版情報: Singapore : Springer Nature Singapore : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-981-15-9003-0
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目次情報: 続きを見る
Chapter 1: Introduction
Chapter 2: Consistency of an Estimator
Chapter 3: Consistent and Asymptotically Normal Estimators
Chapter 4: CAN Estimators in Exponential and Cramer Families
Chapter 5: Large Sample Test Procedures
Chapter 6: Goodness of Fit Test and Tests for Contingency Tables
Chapter 7: Solutions to Conceptual Exercises
Chapter 1: Introduction
Chapter 2: Consistency of an Estimator
Chapter 3: Consistent and Asymptotically Normal Estimators