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1.

図書
図書
1. Statistical curves and parameters : choosing an appropriate approach
Michael E. Tarter
出版情報: Natick, Mass. : A K Peters, c2000  xiii, 386 p. ; 24 cm
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Preface
Introduction / 1：
Background / 1.1：
A fictional example / 1.2：
Curves and statistical history / 1.3：
Model and Distribution Terminology / 2：
Modeling background / 2.1：
Representative number / 2.2：
Curve types / 2.3：
Distribution and data terminology / 2.4：
Parameter validity and property existence / 2.5：
Estimator terminology / 2.6：
Degenerate curves / 2.7：
Variability and Related Curve Properties / 3：
Uncertainty and variability / 3.1：
The absolute deviation curve property / 3.2：
The general AD and the ADM curve properties / 3.3：
Curve property selection / 3.4：
The history of variability appreciation / 3.5：
Simplistic approaches and the history of probability / 3.6：
Moments and Curve Uncertainty / 4：
E and Var Geometry / 4.1：
Higher order moments and the indicator function / 4.2：
Early statistical models / 4.3：
Early statistical models and higher order moments / 4.4：
Curve sub-types and model choice / 4.5：
Goodness of fit / 5：
Neyman's and alternative criteria / 5.1：
Criteria, metrics and estimators / 5.2：
The Kolmogoroff-Smirnoff criteria / 5.3：
Bernoulli variation and the Cauchy density / 5.4：
Comparative goodness of fit / 5.5：
Variates, Variables and Regression / 6：
Variates and variables / 6.1：
Variates and subjects / 6.2：
Expressions, algorithms and life tables / 6.3：
Distinctions between curve types / 6.4：
Curve properties and symbols / 6.5：
Variates, variables, and E[subscript f](Y|x) regression / 6.6：
[mu](x), E[subscript f] (Y|x) and regression alternatives / 6.7：
Mixing Parameters and Data-generation models / 7：
An introduction to data-generation models / 7.1：
Error, regression, and probit, models / 7.2：
Regression and data-generation models / 7.3：
Probability, proportion, and data-generation models / 7.4：
The generation of contagious model and mixture model data / 7.5：
The Association Parameter [rho] / 8：
Response, key, and nuisance, variates / 8.1：
The association parameter [rho] / 8.2：
Conditional, joint and marginal, notation / 8.3：
The sample and the population correlation coefficient / 8.4：
Correlation geometry / 8.5：
Regression and Association Parameters / 9：
The curse of dimensionality / 9.1：
Multiple variable interdependence / 9.2：
Logit and linear models / 9.3：
Dual regression functions / 9.4：
Parameters, Confounding, and Least Squares / 10：
Ideal objects / 10.1：
Linear data-generation models and mixture models / 10.2：
Parameter distinctiveness / 10.3：
Representational uniqueness and model fitting / 10.4：
Model-fitting considerations / 10.5：
The variance curve property and bathtub functions / 10.6：
Regression and least squares / 10.7：
Nonparametric Adjustment / 11：
Age-adjustment and logistic regression / 11.1：
Crude and specific rates / 11.2：
Age-adjustment; marginal, joint, and conditional curves / 11.3：
Age-adjustment and partial correlation / 11.4：
Direct and indirect adjustment / 11.5：
The computation of adjusted rates / 11.6：
Continuous Variate Adjustment / 12：
Observed and expected rates / 12.1：
Trivariate data-generation and additive regression models / 12.2：
Regression and data generation / 12.3：
Correlation, regression, and nuisance variables / 12.4：
Trivariate Normality graphics / 12.5：
Procedural Road Maps / 13：
The organization of statistical data and statistical methods / 13.1：
Log and log(-log) transformations / 13.2：
Methodological alternatives / 13.3：
Conditional and joint density models / 13.4：
Model-based and Generalized Representation / 14：
Multiple properties and parameters / 14.1：
Specification and generalized representation / 14.2：
Identifiability of generalized versus extended model representation / 14.3：
The E(X) curve property's relationship to location and scale / 14.4：
Parameters, Transformations, and Quantiles / 15：
Location and scale parameter representation of continuous variates / 15.1：
[rho]-focused transformations and [sigma]-focused transformations / 15.2：
Quantiles, quartiles, and box-and-whisker plots / 15.3：
Normal ranges and box sizes / 15.4：
Confidence bands and prediction bands / 15.5：
Notches, stems, and leaves / 15.6：
The log transformation and skewness / 15.7：
Noncentrality Parameters and Degress of Freedom / 16：
The (C[subscript 1]|A[subscript 2]) case and variate-variable relationships / 16.1：
Invariance and confounding / 16.2：
ANOVA tables and confounding / 16.3：
Contingency tables and the parameter v / 16.4：
Student-t and Cauchy densities / 16.5：
Parameter-Based Estimation / 17：
Likelihood and BLU estimation / 17.1：
Censoring and incompleteness / 17.2：
Outliers and errors / 17.3：
Ordered variates and subscripts / 17.4：
BLU estimators / 17.5：
BLU estimation and censoring / 17.6：
BLU estimators and alternatives / 17.7：
Inference and Composite Variates / 18：
Curves and composite variates / 18.1：
Specific sampling distributions / 18.2：
The mean's variance formula and mixtures / 18.3：
Inference and a two-valued metric / 18.4：
The one tail z-test / 18.5：
Parameters and Test Statistics / 19：
The parameter [Delta] / 19.1：
Power and efficiency / 19.2：
Power and test considerations / 19.3：
The sample mean and the sample median / 19.4：
Tables and the details of test construction / 19.5：
Power, efficiency and BLU estimators / 19.6：
Curve Truncation and the Curve e(x) / 20：
Expectation as a limit and the effects of truncation / 20.1：
Truncation symmetry / 20.2：
Truncation and bias / 20.3：
Truncation and the curve e(x) / 20.4：
When are curve properties relevant and when are model parameters relevant / 20.5：
Models and Notation / I：
Notation historical background / I.1：
Specific models, the Normal / I.2：
Specific models, lognormals and related curves / I.3：
Model families / I.4：
Mixtures and Bayesian statistics / I.5：
Notational conventions about moments and variates / I.6：
Variate Independence and Curve Identity / II：
Independence and identical distribution / II.1：
Regression notation / II.2：
General Statistical and Mathematical Notation / III：
References
Index
Preface
Introduction / 1：
Background / 1.1：
2.

図書
図書
2. Statistical experiments and decisions : asymptotic theory
A N Shiryaev, V G Spokoiny
出版情報: Singapore : World Scientific, c2000  xvi, 283 p. ; 26 cm
シリーズ名: Advanced series on statistical science & applied probability ; vol. 8
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3.

図書
図書
3. Probability, reliability and statistical methods in engineering design
Achintya Haldar, Sankaran Mahadevan
出版情報: New York ; Chichester : Wiley, c2000  xvi, 304 p. ; 25 cm
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Basic Concept of Reliability
Mathematics of Probability
Modeling of Uncertainty
Commonly Used Probability Distributions
Determination of Distributions and Parameters from Observed Data
Randomness in Response Variables
Fundamentals of Reliability Analysis
Advanced Topics on Reliability Analysis
Simulation Techniques
Appendices
Conversion Factors
References
Index
Basic Concept of Reliability
Mathematics of Probability
Modeling of Uncertainty
4.

図書
図書
4. Statistical inference. 2nd ed
George Casella, Roger L. Berger
出版情報: Belmont, Calif. : Brooks/Cole, c2002  xxviii, 660 p. ; 25 cm
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Probability Theory / 1：
Set Theory / 1.1：
Basics of Probability Theory / 1.2：
Axiomatic Foundations / 1.2.1：
The Calculus of Probabilities / 1.2.2：
Counting / 1.2.3：
Enumerating Outcomes / 1.2.4：
Conditional Probability and Independence / 1.3：
Random Variables / 1.4：
Distribution Functions / 1.5：
Density and Mass Functions / 1.6：
Exercises / 1.7：
Miscellanea / 1.8：
Transformations and Expectations / 2：
Distributions of Functions of a Random Variable / 2.1：
Expected Values / 2.2：
Moments and Moment Generating Functions / 2.3：
Differentiating Under an Integral Sign / 2.4：
Common Families of Distributions / 2.5：
Introduction / 3.1：
Discrete Distributions / 3.2：
Continuous Distributions / 3.3：
Exponential Families / 3.4：
Location and Scale Families / 3.5：
Inequalities and Identities / 3.6：
Probability Inequalities / 3.6.1：
Identities / 3.6.2：
Multiple Random Variables / 3.7：
Joint and Marginal Distributions / 4.1：
Conditional Distributions and Independence / 4.2：
Bivariate Transformations / 4.3：
Hierarchical Models and Mixture Distributions / 4.4：
Covariance and Correlation / 4.5：
Multivariate Distributions / 4.6：
Inequalities / 4.7：
Numerical Inequalities / 4.7.1：
Functional Inequalities / 4.7.2：
Properties of a Random Sample / 4.8：
Basic Concepts of Random Samples / 5.1：
Sums of Random Variables from a Random Sample / 5.2：
Sampling from the Normal Distribution / 5.3：
Properties of the Sample Mean and Variance / 5.3.1：
The Derived Distributions: Student's t and Snedecor's F / 5.3.2：
Order Statistics / 5.4：
Convergence Concepts / 5.5：
Convergence in Probability / 5.5.1：
Almost Sure Convergence / 5.5.2：
Convergence in Distribution / 5.5.3：
The Delta Method / 5.5.4：
Generating a Random Sample / 5.6：
Direct Methods / 5.6.1：
Indirect Methods / 5.6.2：
The Accept/Reject Algorithm / 5.6.3：
Principles of Data Reduction / 5.7：
The Sufficiency Principle / 6.1：
Sufficient Statistics / 6.2.1：
Minimal Sufficient Statistics / 6.2.2：
Ancillary Statistics / 6.2.3：
Sufficient, Ancillary, and Complete Statistics / 6.2.4：
The Likelihood Principle / 6.3：
The Likelihood Function / 6.3.1：
The Formal Likelihood Principle / 6.3.2：
The Equivariance Principle / 6.4：
Point Estimation / 6.5：
Methods of Finding Estimators / 7.1：
Method of Moments / 7.2.1：
Maximum Likelihood Estimators / 7.2.2：
Bayes Estimators / 7.2.3：
The EM Algorithm / 7.2.4：
Methods of Evaluating Estimators / 7.3：
Mean Squared Error / 7.3.1：
Best Unbiased Estimators / 7.3.2：
Sufficiency and Unbiasedness / 7.3.3：
Loss Function Optimality / 7.3.4：
Hypothesis Testing / 7.4：
Methods of Finding Tests / 8.1：
Likelihood Ratio Tests / 8.2.1：
Bayesian Tests / 8.2.2：
Union-Intersection and Intersection-Union Tests / 8.2.3：
Methods of Evaluating Tests / 8.3：
Error Probabilities and the Power Function / 8.3.1：
Most Powerful Tests / 8.3.2：
Sizes of Union-Intersection and Intersection-Union Tests / 8.3.3：
p-Values / 8.3.4：
Interval Estimation / 8.3.5：
Methods of Finding Interval Estimators / 9.1：
Inverting a Test Statistic / 9.2.1：
Pivotal Quantities / 9.2.2：
Pivoting the CDF / 9.2.3：
Bayesian Intervals / 9.2.4：
Methods of Evaluating Interval Estimators / 9.3：
Size and Coverage Probability / 9.3.1：
Test-Related Optimality / 9.3.2：
Bayesian Optimality / 9.3.3：
Asymptotic Evaluations / 9.3.4：
Consistency / 10.1：
Efficiency / 10.1.2：
Calculations and Comparisons / 10.1.3：
Bootstrap Standard Errors / 10.1.4：
Robustness / 10.2：
The Mean and the Median / 10.2.1：
M-Estimators / 10.2.2：
Asymptotic Distribution of LRTs / 10.3：
Other Large-Sample Tests / 10.3.2：
Approximate Maximum Likelihood Intervals / 10.4：
Other Large-Sample Intervals / 10.4.2：
Analysis of Variance and Regression / 10.5：
Oneway Analysis of Variance / 11.1：
Model and Distribution Assumptions / 11.2.1：
The Classic ANOVA Hypothesis / 11.2.2：
Inferences Regarding Linear Combinations of Means / 11.2.3：
The ANOVA F Test / 11.2.4：
Simultaneous Estimation of Contrasts / 11.2.5：
Partitioning Sums of Squares / 11.2.6：
Simple Linear Regression / 11.3：
Least Squares: A Mathematical Solution / 11.3.1：
Best Linear Unbiased Estimators: A Statistical Solution / 11.3.2：
Models and Distribution Assumptions / 11.3.3：
Estimation and Testing with Normal Errors / 11.3.4：
Estimation and Prediction at a Specified x = x[subscript 0] / 11.3.5：
Simultaneous Estimation and Confidence Bands / 11.3.6：
Regression Models / 11.4：
Regression with Errors in Variables / 12.1：
Functional and Structural Relationships / 12.2.1：
A Least Squares Solution / 12.2.2：
Maximum Likelihood Estimation / 12.2.3：
Confidence Sets / 12.2.4：
Logistic Regression / 12.3：
The Model / 12.3.1：
Estimation / 12.3.2：
Robust Regression / 12.4：
Computer Algebra / 12.5：
Table of Common Distributions
References
Author Index
Subject Index
Probability Theory / 1：
Set Theory / 1.1：
Basics of Probability Theory / 1.2：
5.

図書
図書
5. Modern statistics for the life sciences
Alan Grafen, Rosie Hails
出版情報: Oxford : Oxford University Press, 2002  xv, 351 p. ; 25 cm
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Why use this book
How to use this book
How to teach this text
An introduction to analysis of variance / 1：
Model formulae and geometrical pictures / 1.1：
General Linear Models / 1.2：
The basic principles of ANOVA / 1.3：
An example of ANOVA / 1.4：
The geometrical approach for an ANOVA / 1.5：
Regression / 2：
What kind of data are suitable for regression? / 2.1：
How is the best fit line chosen? / 2.2：
The geometrical view of regression / 2.3：
Regression--an example / 2.4：
Confidence and prediction intervals / 2.5：
Conclusions from a regression analysis / 2.6：
Unusual observations / 2.7：
The role of X and Y--does it matter which is which? / 2.8：
Models, parameters and GLMs / 3：
Populations and parameters / 3.1：
Expressing all models as linear equations / 3.2：
Turning the tables and creating datasets / 3.3：
Using more than one explanatory variable / 4：
Why use more than one explanatory variable? / 4.1：
Elimination by considering residuals / 4.2：
Two types of sum of squares / 4.3：
Urban Foxes--an example of statistical elimination / 4.4：
Statistical elimination by geometrical analogy / 4.5：
Designing experiments--keeping it simple / 5：
Three fundamental principles of experimental design / 5.1：
The geometrical analogy for blocking / 5.2：
The concept of orthogonality / 5.3：
Combining continuous and categorical variables / 6：
Reprise of models fitted so far / 6.1：
Orthogonality in the context of continuous and categorical variables / 6.2：
Treating variables as continuous or categorical / 6.4：
The general nature of General Linear Models / 6.5：
Interactions--getting more complex / 7：
The factorial principle / 7.1：
Analysis of factorial experiments / 7.2：
What do we mean by an interaction? / 7.3：
Presenting the results / 7.4：
Extending the concept of interactions to continuous variables / 7.5：
Uses of interactions / 7.6：
Checking the models I: independence / 8：
Heterogeneous data / 8.1：
Repeated measures / 8.2：
Nested data / 8.3：
Detecting non-independence / 8.4：
Checking the models II: the other three asumptions / 9：
Homogeneity of variance / 9.1：
Normality of error / 9.2：
Linearity/additivity / 9.3：
Model criticism and solutions / 9.4：
Predicting the volume of merchantable wood: an example of model criticism / 9.5：
Selecting a transformation / 9.6：
Model selection I: principles of model choice and designed experiments / 10：
The problem of model choice / 10.1：
Three principles of model choice / 10.2：
Four different types of model choice problem / 10.3：
Orthogonal and near orthogonal designed experiments / 10.4：
Looking for trends across levels of a categorical variable / 10.5：
Model selection II: datasets with several explanatory variables / 11：
Economy of variables in the context of multiple regression / 11.1：
Multiplicity of p-values in the context of multiple regression / 11.2：
Automated model selection procedures / 11.3：
Whale Watching: using the GLM approach / 11.4：
Random effects / 12：
What are random effects? / 12.1：
Four new concepts to deal with random effects / 12.2：
A one-way ANOVA with a random factor / 12.3：
A two-level nested ANOVA / 12.4：
Mixing random and fixed effects / 12.5：
Using mock analyses to plan an experiment / 12.6：
Categorical data / 13：
Categorical data: the basics / 13.1：
The Poisson distribution / 13.2：
The chi-squared test in contingency tables / 13.3：
General linear models and categorical data / 13.4：
What lies beyond? / 14：
Generalised Linear Models / 14.1：
Multiple y variables, repeated measures and within-subject factors / 14.2：
Conclusion / 14.3：
Answers to exercises / 15：
Revision section: The basics / Chapter 1：
Populations and samples / R1.1：
Three types of variability: of the sample, the population and the estimate / R1.2：
Confidence intervals: a way of precisely representing uncertainty / R1.3：
The null hypothesis--taking the conservative approach / R1.4：
Comparing two means / R1.5：
The meaning of p-values and confidence intervals / R1.6：
What is a p-value?
What is a confidence interval?
Analytical results about variances of sample means / Appendix 2：
Introducing the basic notation
Using the notation to define the variance of a sample
Using the notation to define the mean of a sample
Defining the variance of the sample mean
To illustrate why the sample variance must be calculated with n - 1 in its denominator (rather than n) to be an unbiased estimate of the population variance
Probability distributions / Appendix 3：
Some gentle theory
Confirming simulations
Bibliography
Index
Why use this book
How to use this book
How to teach this text
6.

図書
図書
6. Statistics : a guide to the unknown. 4th ed
Roxy Peck ... [et al.]
出版情報: Belmont, CA : Thomson, Brooks/Cole, c2006  xxiv, 440 p. ; 24 cm
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Essays Classified by Data Sources
Essays Classified by Statistical Tools
Our Biologic World / Part 1：
Health and Sickness
The Biggest Public Health Experiment Ever: The 1954 Field Trial of the Salk Poliomyelitis Vaccine / Paul Meier
Safety of Anesthetics / Lincoln E. Moses ; Frederick Mosteller
The Metro Firm Trials and Ongoing Patient Randomization / Duncan Neuhauser
A Health Insurance Experiment / Joseph P. Newhouse
Cigarette Price, Smoking, and Excise Tax Policy / Kenneth E. Warner
People and Animals
Does Inheritance Matter in Disease? / D. D. Reid
The Plight of the Whales / D. G. Chapman
The Importance of Being Human / W. W. Howells
Our Political World / Part 2：
Statistical Proof of Employment Discrimination / Sandy L. Zabell
Statistics in Jury Selection: How to Avoid Unfavorable Jurors / S. James Press
Measuring the Effects of Social Innovations by Means of Time Series / Donald T. Campbell
Election Night on Television / Richard F. Link
Our Social World / Part 3：
Communicating with Others
Deciding Authorship / David L. Wallace
Children's Recall of Pictorial Information / Doris R. Entwisle ; W. H. Huggins
The Meaning of Words / Joseph B. Kruskal
The Sizes of Things / Herbert A. Simon
People at Work
How Accountants Save Money By Sampling / John Neter
Preliminary Evaluation of a New Food Product / Elisabeth Street ; Mavis B. Carroll
Statistical Determination of Numerical Color Tolerances / Lonnie C. Vance
People at School and Play
Making Essay Test Scores Fairer With Statistics / Henry I. Braun ; Howard Wainer
Statistics, Sports, and Some Other Things / Robert Hooke
Counting People and Their Goods
The Consumer Price Index / Philip J. McCarthy
How To Count Better: Using Statistics to Improve the Census / Morris H. Hansen ; Barbara A. Bailar
How the Nation's Employment and Unemployment Estimates Are Made / Carol Boyd Leon ; Philip L. Rones
The Development and Analysis of Economic Indicators / Geoffrey H. Moore
Our Physical World / Part 4：
Optimization and the Traveling Salesman Problem / Charles A. Whitney
Estimating the Chances of Large Earthquakes by Radiocarbon Dating and Statistical Modeling / David R. Brillinger
Very Short Range Weather Forecasting Using Automated Observations / Robert G. Miller
Statistics, the Sun, and the Stars
Acknowledgments
Index
Essays Classified by Data Sources
Essays Classified by Statistical Tools
Our Biologic World / Part 1：
7.

図書
図書
7. Statistics. 4th ed
David Freedman, Robert Pisani, Roger Purves
出版情報: New York ; London : W.W. Norton, c2007  xvi, 576, 121 p. ; 27 cm
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8.

図書
図書
8. Quantitative risk management : concepts, techniques, and tools (: cloth, alk. paper)
Alexander J. McNeil, Rüdiger Frey, Paul Embrechts
出版情報: Princeton, N.J. : Princeton University Press, c2005  xv, 538 p. ; 24 cm
シリーズ名: Princeton series in finance
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Preface
Risk in Perspective / 1：
Risk / 1.1：
Risk and Randomness / 1.1.1：
Financial Risk / 1.1.2：
Measurement and Management / 1.1.3：
A Brief History of Risk Management / 1.2：
From Babylon to Wall Street / 1.2.1：
The Road to Regulation / 1.2.2：
The New Regulatory Framework / 1.3：
Basel II / 1.3.1：
Solvency 2 / 1.3.2：
Why Manage Financial Risk? / 1.4：
A Societal View / 1.4.1：
The Shareholder's View / 1.4.2：
Economic Capital / 1.4.3：
Quantitative Risk Management / 1.5：
The Nature of the Challenge / 1.5.1：
QRM for the Future / 1.5.2：
Basic Concepts in Risk Management / 2：
Risk Factors and Loss Distributions / 2.1：
General Definitions / 2.1.1：
Conditional and Unconditional Loss Distribution / 2.1.2：
Mapping of Risks: Some Examples / 2.1.3：
Risk Measurement / 2.2：
Approaches to Risk Measurement / 2.2.1：
Value-at-Risk / 2.2.2：
Further Comments on VaR / 2.2.3：
Other Risk Measures Based on Loss Distributions / 2.2.4：
Standard Methods for Market Risks / 2.3：
Variance-Covariance Method / 2.3.1：
Historical Simulation / 2.3.2：
Monte Carlo / 2.3.3：
Losses over Several Periods and Scaling / 2.3.4：
Backtesting / 2.3.5：
An Illustrative Example / 2.3.6：
Multivariate Models / 3：
Basics of Multivariate Modelling / 3.1：
Random Vectors and Their Distributions / 3.1.1：
Standard Estimators of Covariance and Correlation / 3.1.2：
The Multivariate Normal Distribution / 3.1.3：
Testing Normality and Multivariate Normality / 3.1.4：
Normal Mixture Distributions / 3.2：
Normal Variance Mixtures / 3.2.1：
Normal Mean-Variance Mixtures / 3.2.2：
Generalized Hyperbolic Distributions / 3.2.3：
Fitting Generalized Hyperbolic Distributions to Data / 3.2.4：
Empirical Examples / 3.2.5：
Spherical and Elliptical Distributions / 3.3：
Spherical Distributions / 3.3.1：
Elliptical Distributions / 3.3.2：
Properties of Elliptical Distributions / 3.3.3：
Estimating Dispersion and Correlation / 3.3.4：
Testing for Elliptical Symmetry / 3.3.5：
Dimension Reduction Techniques / 3.4：
Factor Models / 3.4.1：
Statistical Calibration Strategies / 3.4.2：
Regression Analysis of Factor Models / 3.4.3：
Principal Component Analysis / 3.4.4：
Financial Time Series / 4：
Empirical Analyses of Financial Time Series / 4.1：
Stylized Facts / 4.1.1：
Multivariate Stylized Facts / 4.1.2：
Fundamentals of Time Series Analysis / 4.2：
Basic Definitions / 4.2.1：
ARMA Processes / 4.2.2：
Analysis in the Time Domain / 4.2.3：
Statistical Analysis of Time Series / 4.2.4：
Prediction / 4.2.5：
GARCH Models for Changing Volatility / 4.3：
ARCH Processes / 4.3.1：
GARCH Processes / 4.3.2：
Simple Extensions of the GARCH Model / 4.3.3：
Fitting GARCH Models to Data / 4.3.4：
Volatility Models and Risk Estimation / 4.4：
Volatility Forecasting / 4.4.1：
Conditional Risk Measurement / 4.4.2：
Fundamentals of Multivariate Time Series / 4.4.3：
Multivariate ARMA Processes / 4.5.1：
Multivariate GARCH Processes / 4.6：
General Structure of Models / 4.6.1：
Models for Conditional Correlation / 4.6.2：
Models for Conditional Covariance / 4.6.3：
Fitting Multivariate GARCH Models / 4.6.4：
Dimension Reduction in MGARCH / 4.6.5：
MGARCH and Conditional Risk Measurement / 4.6.6：
Copulas and Dependence / 5：
Copulas / 5.1：
Basic Properties / 5.1.1：
Examples of Copulas / 5.1.2：
Meta Distributions / 5.1.3：
Simulation of Copulas and Meta Distributions / 5.1.4：
Further Properties of Copulas / 5.1.5：
Perfect Dependence / 5.1.6：
Dependence Measures / 5.2：
Linear Correlation / 5.2.1：
Rank Correlation / 5.2.2：
Coefficients of Tail Dependence / 5.2.3：
Normal Mixture Copulas / 5.3：
Tail Dependence / 5.3.1：
Rank Correlations / 5.3.2：
Skewed Normal Mixture Copulas / 5.3.3：
Grouped Normal Mixture Copulas / 5.3.4：
Archimedean Copulas / 5.4：
Bivariate Archimedean Copulas / 5.4.1：
Multivariate Archimedean Copulas / 5.4.2：
Non-exchangeable Archimedean Copulas / 5.4.3：
Fitting Copulas to Data / 5.5：
Method-of-Moments using Rank Correlation / 5.5.1：
Forming a Pseudo-Sample from the Copula / 5.5.2：
Maximum Likelihood Estimation / 5.5.3：
Aggregate Risk / 6：
Coherent Measures of Risk / 6.1：
The Axioms of Coherence / 6.1.1：
Coherent Risk Measures Based on Loss Distributions / 6.1.2：
Coherent Risk Measures as Generalized Scenarios / 6.1.4：
Mean-VaR Portfolio Optimization / 6.1.5：
Bounds for Aggregate Risks / 6.2：
The General Frechet Problem / 6.2.1：
The Case of VaR / 6.2.2：
Capital Allocation / 6.3：
The Allocation Problem / 6.3.1：
The Euler Principle and Examples / 6.3.2：
Economic Justification of the Euler Principle / 6.3.3：
Extreme Value Theory / 7：
Maxima / 7.1：
Generalized Extreme Value Distribution / 7.1.1：
Maximum Domains of Attraction / 7.1.2：
Maxima of Strictly Stationary Time Series / 7.1.3：
The Block Maxima Method / 7.1.4：
Threshold Exceedances / 7.2：
Generalized Pareto Distribution / 7.2.1：
Modelling Excess Losses / 7.2.2：
Modelling Tails and Measures of Tail Risk / 7.2.3：
The Hill Method / 7.2.4：
Simulation Study of EVT Quantile Estimators / 7.2.5：
Conditional EVT for Financial Time Series / 7.2.6：
Tails of Specific Models / 7.3：
Domain of Attraction of Frechet Distribution / 7.3.1：
Domain of Attraction of Gumbel Distribution / 7.3.2：
Mixture Models / 7.3.3：
Point Process Models / 7.4：
Threshold Exceedances for Strict White Noise / 7.4.1：
The POT Model / 7.4.2：
Self-Exciting Processes / 7.4.3：
A Self-Exciting POT Model / 7.4.4：
Multivariate Maxima / 7.5：
Multivariate Extreme Value Copulas / 7.5.1：
Copulas for Multivariate Minima / 7.5.2：
Copula Domains of Attraction / 7.5.3：
Modelling Multivariate Block Maxima / 7.5.4：
Multivariate Threshold Exceedances / 7.6：
Threshold Models Using EV Copulas / 7.6.1：
Fitting a Multivariate Tail Model / 7.6.2：
Threshold Copulas and Their Limits / 7.6.3：
Credit Risk Management / 8：
Introduction to Credit Risk Modelling / 8.1：
Credit Risk Models / 8.1.1：
Structural Models of Default / 8.1.2：
The Merton Model / 8.2.1：
Pricing in Merton's Model / 8.2.2：
The KMV Model / 8.2.3：
Models Based on Credit Migration / 8.2.4：
Multivariate Firm-Value Models / 8.2.5：
Threshold Models / 8.3：
Notation for One-Period Portfolio Models / 8.3.1：
Threshold Models and Copulas / 8.3.2：
Industry Examples / 8.3.3：
Models Based on Alternative Copulas / 8.3.4：
Model Risk Issues / 8.3.5：
The Mixture Model Approach / 8.4：
One-Factor Bernoulli Mixture Models / 8.4.1：
CreditRisk+ / 8.4.2：
Asymptotics for Large Portfolios / 8.4.3：
Threshold Models as Mixture Models / 8.4.4：
Model-Theoretic Aspects of Basel II / 8.4.5：
Monte Carlo Methods / 8.4.6：
Basics of Importance Sampling / 8.5.1：
Application to Bernoulli-Mixture Models / 8.5.2：
Statistical Inference for Mixture Models / 8.6：
Motivation / 8.6.1：
Exchangeable Bernoulli-Mixture Models / 8.6.2：
Mixture Models as GLMMs / 8.6.3：
One-Factor Model with Rating Effect / 8.6.4：
Dynamic Credit Risk Models / 9：
Credit Derivatives / 9.1：
Overview / 9.1.1：
Single-Name Credit Derivatives / 9.1.2：
Portfolio Credit Derivatives / 9.1.3：
Mathematical Tools / 9.2：
Random Times and Hazard Rates / 9.2.1：
Modelling Additional Information / 9.2.2：
Doubly Stochastic Random Times / 9.2.3：
Financial and Actuarial Pricing of Credit Risk / 9.3：
Physical and Risk-Neutral Probability Measure / 9.3.1：
Risk-Neutral Pricing and Market Completeness / 9.3.2：
Martingale Modelling / 9.3.3：
The Actuarial Approach to Credit Risk Pricing / 9.3.4：
Pricing with Doubly Stochastic Default Times / 9.4：
Recovery Payments of Corporate Bonds / 9.4.1：
The Model / 9.4.2：
Pricing Formulas / 9.4.3：
Applications / 9.4.4：
Affine Models / 9.5：
Basic Results / 9.5.1：
The CIR Square-Root Diffusion / 9.5.2：
Extensions / 9.5.3：
Conditionally Independent Defaults / 9.6：
Reduced-Form Models for Portfolio Credit Risk / 9.6.1：
Conditionally Independent Default Times / 9.6.2：
Examples and Applications / 9.6.3：
Copula Models / 9.7：
Definition and General Properties / 9.7.1：
Factor Copula Models / 9.7.2：
Default Contagion in Reduced-Form Models / 9.8：
Default Contagion and Default Dependence / 9.8.1：
Information-Based Default Contagion / 9.8.2：
Interacting Intensities / 9.8.3：
Operational Risk and Insurance Analytics / 10：
Operational Risk in Perspective / 10.1：
A New Risk Class / 10.1.1：
The Elementary Approaches / 10.1.2：
Advanced Measurement Approaches / 10.1.3：
Operational Loss Data / 10.1.4：
Elements of Insurance Analytics / 10.2：
The Case for Acturaial Methodology / 10.2.1：
The Total Loss Amount / 10.2.2：
Approximations and Panjer Recursion / 10.2.3：
Poisson Mixtures / 10.2.4：
Tails of Aggregate Loss Distributions / 10.2.5：
The Homogeneous Poisson Process / 10.2.6：
Processes Related to the Poisson Process / 10.2.7：
Appendix
Miscellaneous Definitions and Results / A.1：
Type of Distribution / A.1.1：
Generalized Inverses and Quantiles / A.1.2：
Karamata's Theorem / A.1.3：
Probability Distributions / A.2：
Beta / A.2.1：
Exponential / A.2.2：
F / A.2.3：
Gamma / A.2.4：
Generalized Inverse Gaussian / A.2.5：
Inverse Gamma / A.2.6：
Negative Binomial / A.2.7：
Pareto / A.2.8：
Stable / A.2.9：
Likelihood Inference / A.3：
Maximum Likelihood Estimators / A.3.1：
Asymptotic Results: Scalar Parameter / A.3.2：
Asymptotic Results: Vector of Parameters / A.3.3：
Wald Test and Confidence Intervals / A.3.4：
Likelihood Ratio Test and Confidence Intervals / A.3.5：
Akaike Information Criterion / A.3.6：
References
Index
Preface
Risk in Perspective / 1：
Risk / 1.1：
9.

図書
図書
9. Introduction to mathematical statistics. 6th ed (: International ed)
Robert V. Hogg, Joseph W. McKean, Allen T. Craig
出版情報: Upper Saddle River, N.J. : Pearson Prentice Hall, c2005  xiii, 704 p. ; 25 cm
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目次情報: 続きを見る
Probability and Distribution / 1：
Multivariate Distributions / 2：
Some Special Distributions / 3：
Unbiasedness, Consistency, and Limiting Distributions / 4：
Introduction to Inference / 5：
Maximum Likelihood Methods / 6：
Sufficiency / 7：
Optimal Tests of Hypotheses / 8：
Inferences about Normal Models / 9：
Nonparametric Statistics / 10：
Bayesian Statistics / 11：
Comparison of Least Squares and Robust Procedures for Linear Models / 12：
gularity Conditions / Appendix A：
R-Functions / Appendix B：
Regularity Conditions
Probability and Distribution / 1：
Multivariate Distributions / 2：
Some Special Distributions / 3：
10.

図書
図書
10. Monte Carlo statistical methods. 2nd ed
Christian P. Robert, George Casella
出版情報: New York : Springer, c2004  xxx, 645 p. ; 24 cm
シリーズ名: Springer texts in statistics
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