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図書
図書
1. Super crunchers : why thinking-by-numbers is the new way to be smart (: paperback)
Ian Ayres
出版情報: New York : Bantam Books, 2008  307 p. ; 21 cm
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Introduction: The Rise of the Super Crunchers
Who's Doing Your Thinking for You? / 1：
Creating Your Own Data with the Flip of a Coin / 2：
Government by Chance / 3：
How Should Physicians Treat Evidence-Based Medicine? / 4：
Experts Versus Equations / 5：
Why Now? / 6：
Are We Having Fun Yet? / 7：
The Future of Intuition (and Expertise) / 8：
Afterword: Continuing Notes on the Revolution
Acknowledgments
Notes
Index
Introduction: The Rise of the Super Crunchers
Who's Doing Your Thinking for You? / 1：
Creating Your Own Data with the Flip of a Coin / 2：
2.

図書
図書
2. Introduction to distance sampling : estimating abundance of biological populations (: hbk ; : Pbk)
S.T. Buckland ... [et al.]
出版情報: New York : Oxford University Press, 2001  xv, 432 p. ; 24 cm
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Introductory concepts / 1：
Introduction / 1.1：
Distance sampling methods / 1.2：
Quadrat sampling / 1.2.1：
Strip transect sampling / 1.2.2：
Line transect sampling / 1.2.3：
Point counts / 1.2.4：
Point transect sampling / 1.2.5：
Trapping webs / 1.2.6：
Cue counting / 1.2.7：
Dung counts / 1.2.8：
Related techniques / 1.2.9：
The detection function / 1.3：
Range of applications / 1.4：
Objects of interest / 1.4.1：
Method of transect coverage / 1.4.2：
Clustered populations / 1.4.3：
Types of data / 1.5：
Ungrouped data / 1.5.1：
Grouped data / 1.5.2：
Data truncation / 1.5.3：
Units of measurement / 1.5.4：
Ancillary data / 1.5.5：
Known constants and parameters / 1.6：
Known constants / 1.6.1：
Parameters / 1.6.2：
Assumptions / 1.7：
Fundamental concept / 1.8：
Detection of objects / 1.9：
Cue production / 1.9.1：
Observer effectiveness / 1.9.2：
Environment / 1.9.3：
History of methods / 1.10：
Line transects / 1.10.1：
Point transects / 1.10.2：
Program Distance / 1.11：
Assumptions and modelling philosophy / 2：
Assumption 1: objects on the line or point are detected with certainty / 2.1：
Assumption 2: objects are detected at their initial location / 2.1.2：
Assumption 3: measurements are exact / 2.1.3：
Other assumptions / 2.1.4：
Fundamental models / 2.2：
Summary / 2.2.1：
Philosophy and strategy / 2.3：
Model robustness / 2.3.1：
Shape criterion / 2.3.2：
Efficiency / 2.3.3：
Model fit / 2.3.4：
Test power / 2.3.5：
Robust models / 2.4：
Some analysis guidelines / 2.5：
Exploratory phase / 2.5.1：
Model selection / 2.5.2：
Final analysis and inference / 2.5.3：
Statistical theory / 3：
General formula / 3.1：
Standard distance sampling / 3.1.1：
Distance sampling with multipliers / 3.1.2：
The key function formulation for distance data / 3.2：
Maximum likelihood methods / 3.3：
Special cases / 3.3.1：
The half-normal detection function / 3.3.4：
Constrained maximum likelihood estimation / 3.3.5：
Choice of model / 3.4：
Criteria for robust estimation / 3.4.1：
Akaike's Information Criterion / 3.4.2：
The likelihood ratio test / 3.4.3：
Goodness of fit / 3.4.4：
Estimation for clustered populations / 3.5：
Truncation / 3.5.1：
Stratification by cluster size / 3.5.2：
Weighted average of cluster sizes / 3.5.3：
Regression estimators / 3.5.4：
Use of covariates / 3.5.5：
Replacing clusters by individual objects / 3.5.6：
Density, variance and interval estimation / 3.6：
Basic formulae / 3.6.1：
Replicate lines or points / 3.6.2：
The jackknife / 3.6.3：
The bootstrap / 3.6.4：
Estimating change in density / 3.6.5：
A finite population correction factor / 3.6.6：
Stratification and covariates / 3.7：
Stratification / 3.7.1：
Covariates / 3.7.2：
Efficient simulation of distance data / 3.8：
The general approach / 3.8.1：
The simulated line transect data of Chapter 4 / 3.8.2：
The simulated size-biased point transect data of Chapter 5 / 3.8.3：
Discussion / 3.8.4：
Exercises / 3.9：
Example data / 4：
Right-truncation / 4.3：
Left-truncation / 4.3.2：
Estimating the variance in sample size / 4.4：
Analysis of grouped or ungrouped data / 4.5：
The models / 4.6：
Likelihood ratio tests / 4.6.2：
Estimation of density and measures of precision / 4.6.4：
The standard analysis / 4.7.1：
Ignoring information from replicate lines / 4.7.2：
Bootstrap variances and confidence intervals / 4.7.3：
Satterthwaite degrees of freedom for confidence intervals / 4.7.4：
Estimation when the objects are in clusters / 4.8：
Observed cluster size independent of distance / 4.8.1：
Observed cluster size dependent on distance / 4.8.2：
Independence / 4.9：
Detection on the line / 4.9.2：
Movement prior to detection / 4.9.3：
Inaccuracy in distance measurements / 4.9.4：
Standard method with additional truncation / 4.10：
Replacement of clusters by individuals / 5.8.2：
Regression estimator / 5.8.3：
Related methods / 5.9：
Dung and nest surveys / 6.1：
Background / 6.2.1：
Field methods / 6.2.2：
Analysis / 6.2.3：
Line transect surveys for objects that are not continuously available for detection / 6.2.4：
Periods of detectability interspersed with periods of unavailability / 6.3.1：
Objects that give discrete cues / 6.3.2：
Density estimation / 6.4：
Example / 6.4.3：
Distance sampling surveys for fast-moving objects / 6.5：
Line transect surveys / 6.5.1：
Point transect surveys / 6.5.2：
Other models / 6.6：
Binomial models / 6.6.1：
Estimators based on the empirical cdf / 6.6.2：
Estimators based on shape restrictions / 6.6.3：
Kernel estimators / 6.6.4：
Hazard-rate models / 6.6.5：
Distance sampling surveys when the observed area is incompletely covered / 6.7：
Survey design and field methods / 6.8：
Estimation of density / 6.8.2：
Monte Carlo simulations / 6.8.4：
A simple example / 6.8.5：
Darkling beetle surveys / 6.8.6：
Point-to-object and nearest neighbour methods / 6.9：
Study design and field methods / 6.10：
Survey design / 7.1：
Transect layout / 7.2.1：
Sample size / 7.2.2：
Survey protocol and searching behaviour / 7.3：
Data measurement and recording / 7.3.1：
Distance measurement / 7.4.1：
Angle measurement / 7.4.2：
Distance measurement error / 7.4.3：
Cluster size / 7.4.4：
Line length measurement / 7.4.5：
Data recording / 7.4.6：
Training observers / 7.5：
Aerial surveys / 7.6：
Aircraft and survey characteristics / 7.6.1：
Search and survey protocol / 7.6.2：
Marine shipboard surveys / 7.6.3：
Vessel and survey characteristics / 7.7.1：
Land-based surveys / 7.7.2：
Surveys of small objects / 7.8.1：
Stratification by habitat / 7.8.2：
Permanent transects and repeat transects / 7.8.3：
Cut transects / 7.8.4：
Roads, tracks and paths as transects / 7.8.5：
Spotlight and thermal imager surveys / 7.8.6：
Objects detected away from the line / 7.8.7：
Bird surveys / 7.8.8：
Surveys in riparian habitats / 7.8.9：
Special circumstances / 7.9：
Multi-species surveys / 7.9.1：
Surveys of animals that occur at high densities / 7.9.2：
One-sided transects / 7.9.3：
Uneven terrain and contour transects / 7.9.4：
Uncertain detection on the trackline / 7.9.5：
Field comparisons between line transects, point transects and mapping censuses / 7.10：
Breeding birds in Californian coastal scrub / 7.10.1：
Breeding birds in Sierran subalpine forest / 7.10.2：
Bobolink surveys in New York state / 7.10.3：
Breeding birds in Californian oak-pine woodlands / 7.10.4：
Breeding birds along the Colorado River / 7.10.5：
Birds of Miller Sands Island, Oregon / 7.10.6：
Concluding remarks / 7.10.7：
Illustrative examples / 7.11：
Lake Huron brick data / 8.1：
Wooden stake data / 8.3：
Studies of nest density / 8.4：
Spatial distribution of duck nests / 8.4.1：
Nest detection in differing habitat types / 8.4.2：
Models for the detection function g(x) / 8.4.4：
Estimating trend in nest numbers / 8.4.5：
Fin whale abundance in the North Atlantic / 8.5：
House wren densities in South Platte River bottomland / 8.6：
Songbird point transect surveys in Arapaho NWR / 8.7：
Assessing the effects of habitat on density / 8.8：
Bibliography
Common and scientific names of plants and animals
Glossary of notation and abbreviations
Index
Introductory concepts / 1：
Introduction / 1.1：
Distance sampling methods / 1.2：
3.

図書
図書
3. Directional statistics
Kanti V. Mardia, Peter E. Jupp
出版情報: Chichester : Wiley, c2000  xxi, 429 p. ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics
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I: Circular data
Summary Statistics / 2：
Basic Concepts and Models / 3：
Fundamental Theorems and Distribution Theory / 4：
Point Estimation / 5：
Tests of Uniformity and Tests of Goodness-of-Fit / 6：
Tests on von Mises Distributions / 7：
-parametric Methods / 8：
Distributions on Spheres / 9：
Inference on Spheres / 10：
Correlation and Regression / 11：
Modern Methodology / 12：
General Sample Spaces / 13：
Shape Analysis / 14：
I: Circular data
Summary Statistics / 2：
Basic Concepts and Models / 3：
4.

電子ブック
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4. Introduction to empirical processes and semiparametric inference (: electronic bk)
Michael R. Kosorok
出版情報: [Berlin] : SpringerLink, [20--]  1 online resource (xiv, 483 p.)
シリーズ名: Springer series in statistics
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Preface
Overview / I：
Introduction / 1：
An Overview of Empirical Processes / 2：
The Main Features / 2.1：
Empirical Process Techniques / 2.2：
Stochastic Convergence / 2.2.1：
Entropy for Glivenko-Cantelli and Donsker Theorems / 2.2.2：
Bootstrapping Empirical Processes / 2.2.3：
The Functional Delta Method / 2.2.4：
Z-Estimators / 2.2.5：
M-Estimators / 2.2.6：
Other Topics / 2.3：
Exercises / 2.4：
Notes / 2.5：
Overview of Semiparametric Inference / 3：
Semiparametric Models and Efficiency / 3.1：
Score Functions and Estimating Equations / 3.2：
Maximum Likelihood Estimation / 3.3：
Case Studies I / 3.4：
Linear Regression / 4.1：
Mean Zero Residuals / 4.1.1：
Median Zero Residuals / 4.1.2：
Counting Process Regression / 4.2：
The General Case / 4.2.1：
The Cox Model / 4.2.2：
The Kaplan-Meier Estimator / 4.3：
Efficient Estimating Equations for Regression / 4.4：
Simple Linear Regression / 4.4.1：
A Poisson Mixture Regression Model / 4.4.2：
Partly Linear Logistic Regression / 4.5：
Empirical Processes / 4.6：
Introduction to Empirical Processes / 5：
Preliminaries for Empirical Processes / 6：
Metric Spaces / 6.1：
Outer Expectation / 6.2：
Linear Operators and Functional Differentiation / 6.3：
Proofs / 6.4：
Stochastic Processes in Metric Spaces / 6.5：
Weak Convergence / 7.2：
General Theory / 7.2.1：
Spaces of Bounded Functions / 7.2.2：
Other Modes of Convergence / 7.3：
Empirical Process Methods / 7.4：
Maximal Inequalities / 8.1：
Orlicz Norms and Maxima / 8.1.1：
Maximal Inequalities for Processes / 8.1.2：
The Symmetrization Inequality and Measurability / 8.2：
Glivenko-Cantelli Results / 8.3：
Donsker Results / 8.4：
Entropy Calculations / 8.5：
Uniform Entropy / 9.1：
VC-Classes / 9.1.1：
BUEI Classes / 9.1.2：
Bracketing Entropy / 9.2：
Glivenko-Cantelli Preservation / 9.3：
Donsker Preservation / 9.4：
The Bootstrap for Donsker Classes / 9.5：
An Unconditional Multiplier Central Limit Theorem / 10.1.1：
Conditional Multiplier Central Limit Theorems / 10.1.2：
Bootstrap Central Limit Theorems / 10.1.3：
Continuous Mapping Results / 10.1.4：
The Bootstrap for Glivenko-Cantelli Classes / 10.2：
A Simple Z-Estimator Master Theorem / 10.3：
Additional Empirical Process Results / 10.4：
Bounding Moments and Tail Probabilities / 11.1：
Sequences of Functions / 11.2：
Contiguous Alternatives / 11.3：
Sums of Independent but not Identically Distributed Stochastic Processes / 11.4：
Central Limit Theorems / 11.4.1：
Bootstrap Results / 11.4.2：
Function Classes Changing with n / 11.5：
Dependent Observations / 11.6：
Main Results and Proofs / 11.7：
Examples / 12.2：
Composition / 12.2.1：
Integration / 12.2.2：
Product Integration / 12.2.3：
Inversion / 12.2.4：
Other Mappings / 12.2.5：
Consistency / 12.3：
The General Setting / 13.2：
Using Donsker Classes / 13.2.2：
A Master Theorem and the Bootstrap / 13.2.3：
Using the Delta Method / 13.3：
The Argmax Theorem / 13.4：
Rate of Convergence / 14.2：
Regular Euclidean M-Estimators / 14.4：
Non-Regular Examples / 14.5：
A Change-Point Model / 14.5.1：
Monotone Density Estimation / 14.5.2：
Case Studies II / 14.6：
Partly Linear Logistic Regression Revisited / 15.1：
The Two-Parameter Cox Score Process / 15.2：
The Proportional Odds Model Under Right Censoring / 15.3：
Nonparametric Maximum Likelihood Estimation / 15.3.1：
Existence / 15.3.2：
Score and Information Operators / 15.3.3：
Weak Convergence and Bootstrap Validity / 15.3.5：
Testing for a Change-point / 15.4：
Large p Small n Asymptotics for Microarrays / 15.5：
Assessing P-Value Approximations / 15.5.1：
Consistency of Marginal Empirical Distribution Functions / 15.5.2：
Inference for Marginal Sample Means / 15.5.3：
Semiparametric Inference / 15.6：
Introduction to Semiparametric Inference / 16：
Preliminaries for Semiparametric Inference / 17：
Projections / 17.1：
Hilbert Spaces / 17.2：
More on Banach Spaces / 17.3：
Tangent Sets and Regularity / 17.4：
Efficiency / 18.2：
Optimality of Tests / 18.3：
Efficient Inference for Finite-Dimensional Parameters / 18.4：
Efficient Score Equations / 19.1：
Profile Likelihood and Least-Favorable Submodels / 19.2：
The Cox Model for Right Censored Data / 19.2.1：
The Proportional Odds Model for Right Censored Data / 19.2.2：
The Cox Model for Current Status Data / 19.2.3：
Inference / 19.2.4：
Quadratic Expansion of the Profile Likelihood / 19.3.1：
The Profile Sampler / 19.3.2：
The Penalized Profile Sampler / 19.3.3：
Other Methods / 19.3.4：
Efficient Inference for Infinite-Dimensional Parameters / 19.4：
Semiparametric Maximum Likelihood Estimation / 20.1：
Weighted and Nonparametric Bootstraps / 20.2：
The Piggyback Bootstrap / 20.2.2：
Semiparametric M-Estimation / 20.2.3：
Semiparametric M-estimators / 21.1：
Motivating Examples / 21.1.1：
General Scheme for Semiparametric M-Estimators / 21.1.2：
Consistency and Rate of Convergence / 21.1.3：
[radical]n Consistency and Asymptotic Normality / 21.1.4：
Weighted M-Estimators and the Weighted Bootstrap / 21.2：
Entropy Control / 21.3：
Examples Continued / 21.4：
Cox Model with Current Status Data (Example 1, Continued) / 21.4.1：
Binary Regression Under Misspecified Link Function (Example 2, Continued) / 21.4.2：
Mixture Models (Example 3, Continued) / 21.4.3：
Penalized M-estimation / 21.5：
Two Other Examples / 21.5.1：
Case Studies III / 21.6：
The Proportional Odds Model Under Right Censoring Revisited / 22.1：
Efficient Linear Regression / 22.2：
Temporal Process Regression / 22.3：
A Partly Linear Model for Repeated Measures / 22.4：
References / 22.5：
Author Index
List of symbols
Subject Index
Preface
Overview / I：
Introduction / 1：
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