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1. Strength in Numbers: The Rising of Academic Statistics Departments in the U. S.
edited by Alan Agresti, Xiao-Li Meng
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
オンライン: http://dx.doi.org/10.1007/978-1-4614-3649-2
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Statistics as an Academic Discipline
Carnegie-Mellon
Columbia University
Cornell University
Florida State University
George Washington University
Harvard University
Iowa State University
Johns Hopkins University
Kansas State University
Michigan State University
North Carolina State
Oregon State University
Penn State University
Princeton University
Purdue University
Rutgers University
Southern Methodist University
Stanford University
SUNY at Buffalo
Texas A&M
University of California
University of Chicago
University of Connecticut
University of Florida
University of Georgia
University of Iowa
University of Michigan
University of Minnesota
University of Missouri
University of North Carolina
University of Pennsylvania
University of Pittsburgh
University of Washington
University of Wisconsin
Virginia Tech University
Yale University
Referees
Statistics as an Academic Discipline
Carnegie-Mellon
Columbia University
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2. Innovative Statistical Methods for Public Health Data. 1st ed. 2015
edited by Ding-Geng (Din) Chen, Jeffrey Wilson
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2015
シリーズ名: ICSA Book Series in Statistics ;
オンライン: http://dx.doi.org/10.1007/978-3-319-18536-1
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Part 1: Modelling Clustered Data
Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies
Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems
On the Inference of Partially Correlated Data with Applications to Public Health Issues
Modeling Time-Dependent Covariates in Longitudinal Data Analyses
Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data
Part II: Modelling Incomplete or Missing Data
On the Effects of Structural Zeros in Regression Models
Modeling Based on Progressively Type-I Interval Censored Sample
Techniques for Analyzing Incomplete Data in Public Health Research
A Continuous Latent Factor Model for Non-ignorable Missing Data
Part III: Healthcare Research Models
Health Surveillance
Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies
Cusp Catastrophe Modeling in Medical and Health Research
On Ranked Set Sampling Variation and its Applications to Public Health Research
Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data
Meta-analytic Methods for Public Health Research
Part 1: Modelling Clustered Data
Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies
Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems
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3. Contact Tracing in Post-Covid World : A Cryptologic Approach. 1st ed. 2020
by Pranab Chakraborty, Subhamoy Maitra, Mridul Nandi, Suprita Talnikar
出版情報: Singapore : Springer Singapore : Imprint: Springer, 2020
シリーズ名: Indian Statistical Institute Series ;
オンライン: https://doi.org/10.1007/978-981-15-9727-5
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1. Introduction and Preliminaries
2. Centralized Systems
3. Decentralized Contact Tracing Protocols
4. A New Contact Tracing Protocol
1. Introduction and Preliminaries
2. Centralized Systems
3. Decentralized Contact Tracing Protocols
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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. Statistics for Health Data Science : An Organic Approach. 1st ed. 2020
by Ruth Etzioni, Micha Mandel, Roman Gulati
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Texts in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-59889-1
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6. Statistical Learning from a Regression Perspective. 3rd ed. 2020
by Richard A. Berk
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Texts in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-40189-4
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Preface
Preface To Second Edition
Preface To Third Edition
1 Statistical Learning as a Regression Problem
2 Splines, Smoothers, and Kernels
3 Classification and Regression Trees (CART)
4 Bagging
5 Random Forests
6 Boosting
7 Support Vector Machines
8 Neural Networks
9 Reinforcement Learning and Genetic Algorithms
10 Integration Themes and a Bit of Craft Lore
Index
Preface
Preface To Second Edition
Preface To Third Edition
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7. Particle emission concept and probabilistic consideration of the development of infections in systems : Dynamics from logarithm and exponent in the infection process, percolation effects. 1st ed. 2021
by Marcus Hellwig
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
オンライン: https://doi.org/10.1007/978-3-030-69500-2
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Trends in the spread of infections, distribution and contact rates
Addition of the 4th parameter kurtosis to the density Eqb
Prediction using the density function and continuous adjustment of the parameters
Basics for exponential propagation, the logarithm of historical data
Developments in the USA
Incidence under probabilistic aspects
On the percolation theory COVID
Examples of percolation effects
Trends in the spread of infections, distribution and contact rates
Addition of the 4th parameter kurtosis to the density Eqb
Prediction using the density function and continuous adjustment of the parameters
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8. Using R for Biostatistics. 1st ed. 2021
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
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9. Linear and Generalized Linear Mixed Models and Their Applications. 2nd ed. 2021
by Jiming Jiang, Thuan Nguyen
出版情報: New York, NY : Springer New York : Imprint: Springer, 2021
シリーズ名: Springer Series in Statistics ;
オンライン: https://doi.org/10.1007/978-1-0716-1282-8
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1. Linear Mixed Models: Part I
2. Linear Mixed Models: Part II
3. Generalized Linear Mixed Models: Part I
4. Generalized Linear Mixed Models: Part II
1. Linear Mixed Models: Part I
2. Linear Mixed Models: Part II
3. Generalized Linear Mixed Models: Part I
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10. Applying Quantitative Bias Analysis to Epidemiologic Data. 2nd ed. 2021
by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: Statistics for Biology and Health ;
オンライン: https://doi.org/10.1007/978-3-030-82673-4
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1. Introduction and Objectives
2. A Guide to Implementing Quantitative Bias Analysis
3. Data Sources for Bias Analysis
4. Selection Bias
5. Uncontrolled Confounders
6. Misclassification
7. Measurement Error for Continuous Variables
8. Multiple Bias Modeling
8. Bias Analysis by Simulation for Summary Level Data
9. Bias Analysis by Simulation for Record Level Data
10. Combining Systematic and Random Error
11. Bias Analysis by Missing Data Methods
12. Bias Analysis by Empirical Methods
13. Bias Analysis by Bayesian Methods
14. Multiple Bias Modeling
15. Good Practices for Quantitative Bias Analysis
15. Presentation and Inference
References
Index
1. Introduction and Objectives
2. A Guide to Implementing Quantitative Bias Analysis
3. Data Sources for Bias Analysis