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1.

図書
図書
1. Introductory statistics. 4th ed
Sheldon M. Ross
出版情報: Amsterdam ; Tokyo : Elsevier , London : Academic Press, c2017  xxvii, 796 p. ; 25 cm
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2.

図書
図書
2. Applied statistics and probability for engineers. 6th edition., International student version (: pbk)
Douglas C. Montgomery, George C. Runger
出版情報: Hoboken, N.J. : J. Wiley, c2014  xvi, 765 p. ; 26 cm
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3.

図書
図書
3. Mathematical statistics : basic ideas and selected topics (v. 2)
Peter J. Bickel, Kjell A. Doksum
出版情報: Boca Raton : CRC Press, c2016  xix, 465 p. ; 26 cm
シリーズ名: Texts in statistical science
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4.

図書
図書
4. Statistical theory and methodology in science and engineering. 2nd ed
K.A. Brownlee
出版情報: New York : Wiley, c1965  xvi, 590 p. ; 24 cm
シリーズ名: A Wiley publication in applied statistics
Wiley publications in statistics ; Applied statistics
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5.

図書
図書
5. Statistical theory and inference in research
T.A. Bancroft, Chien-Pai Han
出版情報: New York : M. Dekker, c1981  xiv, 372 p. ; 24 cm
シリーズ名: Statistics : textbooks and monographs ; v. 39
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6.

図書
図書
6. Statistical and computational methods in data analysis
Siegmund Brandt
出版情報: Amsterdam : North-Holland Pub. Co. , New York : American Elsevier Pub. Co., 1970  xii, 322 p ; 23 cm
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7.

図書
図書
7. Bayesian inference in statistical analysis
George E.P. Box, George C. Tiao
出版情報: Reading, Mass. : Addison-Wesley, c1973  xviii, 588 p. ; 25 cm
シリーズ名: Addison-Wesley series in behavioral science : quantitative methods
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目次情報: 続きを見る
ature of Bayesian Inference
tandard Normal Theory Inference Problems
ayesian Assessment of Assumptions: Effect of Non-Normality on Inferences About a Population Mean with Generalizations
ayesian Assessment of Assumptions: Comparison of Variances
andom Effect Models
nalysis of Cross Classification Designs
nference About Means with Information from More than One Source: One-Way Classification and Block Designs
ome Aspects of Multivariate Analysis
stimation of Common Regression Coefficients
ransformation of Data
Tables
References
Indexes
ature of Bayesian Inference
tandard Normal Theory Inference Problems
ayesian Assessment of Assumptions: Effect of Non-Normality on Inferences About a Population Mean with Generalizations
8.

図書
図書
8. The theory of statistical inference
Shelemyahu Zacks
出版情報: New York : John Wiley & Sons Inc., c1971  xiii, 609 p ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics
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9.

図書
図書
9. Probability, statistics, and queueing theory : with computer science applications
Arnold O. Allen
出版情報: New York : Academic Press, 1978  xvi, 390 p. ; 24 cm
シリーズ名: Computer science and applied mathematics
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10.

図書
図書
10. Specification searches : ad hoc inference with nonexperimental data
Edward E. Leamer
出版情報: New York : Wiley, c1978  xiii, 370 p. ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics ; . Applied probability and statistics
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11.

図書
図書
11. Probability
Narayan C. Giri
出版情報: New York : M. Dekker, 1974  viii, 260 p. ; 24 cm
シリーズ名: Statistics : textbooks and monographs ; v. 7 . Introduction to probability and statistics ; pt. 1
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12.

図書
図書
12. Statistics
Narayan C. Giri
出版情報: New York : M. Dekker, c1975  viii, 314 p. ; 24 cm
シリーズ名: Statistics : textbooks and monographs ; v. 7 . Introduction to probability and statistics ; pt. 2
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13.

図書
図書
13. Statistical analysis of spherical data
N.I. Fisher, T. Lewis, B.J.J. Embleton
出版情報: Cambridge [Cambridgeshire] ; New York : Cambridge University Press, 1987  xiv, 329 p. ; 24 cm
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目次情報: 続きを見る
Preface
Introduction / 1：
Terminology and spherical coordinate systems / 2：
Descriptive and ancillary methods, and sampling problems / 3：
Models / 4：
Analysis of a single sample of unit vectors / 5：
Analysis of a single sample of undirected lines / 6：
Analysis of two or more samples of vectorial or axial data / 7：
Correlation, regression and temporal/spatial analysis / 8：
Appendces
References
Index
Preface
Introduction / 1：
Terminology and spherical coordinate systems / 2：
14.

図書
図書
14. Elementary decision theory
Herman Chernoff and Lincoln E. Moses
出版情報: New York : Wiley, c1959  xv, 364 p. ; 24 cm
シリーズ名: Wiley publications in statistics ; . Books of related interest
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15.

図書
図書
15. Introduction to statistical inference (: U.S. ; : Germany)
Jack Carl Kiefer ; edited by Gary Lorden
出版情報: New York ; Tokyo : Springer-Verlag, c1987  viii, 334 p. ; 25 cm
シリーズ名: Springer texts in statistics
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16.

図書
図書
16. Statistical and computational methods in data analysis. 2nd, rev. ed (: pbk ; : [hard] : North-Holland)
Siegmund Brandt
出版情報: Amsterdam : North-Holland Pub. Co. , New York : American Elsevier Pub. Co., 1976, c1970  xviii, 414 p. ; 23 cm
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17.

図書
図書
17. Modern concepts and theorems of mathematical statistics (: us ; : gw)
Edward B. Manoukian
出版情報: New York : Springer-Verlag, c1986  xvi, 156 p. ; 25 cm
シリーズ名: Springer series in statistics
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18.

図書
図書
18. Approximation theorems of mathematical statistics (: est.)
Robert J. Serfling
出版情報: New York : Wiley, c1980  xiv, 371 p. ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics ; . Probability and mathematical statistics
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目次情報: 続きを見る
Preliminary Tools and Foundations
The Basic Sample Statistics
Transformations of Given Statistics
Asymptotic Theory in Parametric Inference
U-Statistics
Von Mises Differentiable Statistical Functions
M-Estimates
L-Estimates
R-Estimates
Asymptotic Relative Efficiency
Appendix
References
Author Index
Subject Index
Preliminary Tools and Foundations
The Basic Sample Statistics
Transformations of Given Statistics
19.

図書
図書
19. Applied statistics : principles and examples (: pbk.)
D.R. Cox, E.J. Snell
出版情報: London ; New York : Chapman and Hall, 1981  viii, 189 p. ; 23 cm
シリーズ名: Science paperbacks ; 174
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20.

図書
図書
20. Mathematical basis of statistics
Jean-René Barra ; translation edited by Leon Herbach
出版情報: New York : Academic Press, 1981  xvi, 249 p. ; 24 cm
シリーズ名: Probability and mathematical statistics : a series of monographs and textbooks
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21.

図書
図書
21. Statistical inference for stochastic processes
Ishwar V. Basawa and B.L.S. Prakasa Rao
出版情報: London ; New York : Academic Press, c1980  xiv, 435 p. ; 24 cm
シリーズ名: Probability and mathematical statistics : a series of monographs and textbooks
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22.

図書
図書
22. Statistical analysis in chemistry and the chemical industry
Carl A. Bennett, Norman L. Franklin ; sponsored by the Committee on Applied Mathematical Statistics, the National Research Council
出版情報: New York : Wiley, c1954  xvi, 724 p. ; 24 cm
シリーズ名: Wiley publications in statistics ; . Applied statistics
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23.

図書
図書
23. Statistics: methods and analyses
[by] Lincoln L. Chao
出版情報: New York : McGraw-Hill, [1969]  xii, 512 p ; 24 cm
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24.

図書
図書
24. Simultaneous statistical inference. 2nd ed (: us ; : gw ; : pbk)
Rupert G. Miller, Jr
出版情報: New York : Springer, c1981  xvi, 299 p. ; 25 cm
シリーズ名: Springer series in statistics
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25.

図書
図書
25. Bayes theory (: us ; : gw)
J.A. Hartigan
出版情報: New York ; Tokyo : Springer-Verlag, c1983  xii, 145 p. ; 25 cm
シリーズ名: Springer series in statistics
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26.

図書
図書
26. Data analysis and regression : a second course in statistics
Frederick Mosteller, John W. Tukey
出版情報: Reading, Mass. : Addison-Wesley, c1977  xvii, 588 p. ; 25 cm
シリーズ名: Addison-Wesley series in behavioral science : quantitative methods
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目次情報: 続きを見る
Approaching Data Analysis / 1：
Indication and Indicators / 2：
Displays and Summaries for Batches / 3：
Straightening Curves and Plots / 4：
The Practice of Re-expression / 5：
Need We Reexpress? / 6：
Hunting Out the Real Uncertainty / 7：
A Method of Direct Assessment / 8：
Two-and More-way Tables / 9：
Robust and Resistant Measures of Location and Scale / 10：
Standardizing for Comparison / 11：
Regression for Fitting / 12：
Woes of Regression Coefficients / 13：
Mechanisms Usually Operating in Linear Fitting / 14：
Guided Regression / 15：
Examining Regression Residuals / 16：
Approaching Data Analysis / 1：
Indication and Indicators / 2：
Displays and Summaries for Batches / 3：
27.

図書
図書
27. Variational methods in statistics
Jagdish S. Rustagi
出版情報: New York : Academic Press, 1976  xiii, 236 p. ; 24 cm
シリーズ名: Mathematics in science and engineering : a series of monographs and textbooks ; v. 121
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28.

図書
図書
28. An introduction to genetic statistics
Oscar Kempthorne
出版情報: New York : Wiley, c1957  xvii, 545 p. ; 24 cm
シリーズ名: Wiley publications in statistics ; . Applied statistics
A Wiley publication in applied statistics
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29.

図書
図書
29. The statistical analysis of series of events
[by] D.R. Cox and P.A.W. Lewis
出版情報: London : Methuen, [1966]  viii, 285 p. ; 23 cm
シリーズ名: Methuen's monographs on applied probability and statistics
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30.

図書
図書
30. Factor analysis as a statistical method. 2nd ed
D.N. Lawley and A.E. Maxwell
出版情報: London : Butterworths, 1971  viii, 153 p. ; 23 cm
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31.

図書
図書
31. Likelihood : an account of the statistical concept of likelihood and its application to scientific inference
[by] A.W.F. Edwards
出版情報: Cambridge [England] : Cambridge University Press, 1972  xv, 235 p. ; 23 cm
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32.

図書
図書
32. Introduction to mathematical statistics (: gw ; : us ; : pbk)
Leopold Schmetterer ; translated from the German by Kenneth Wickwire
出版情報: Berlin ; New York : Springer-Verlag, 1974  vi, 502 p. ; 24 cm
シリーズ名: Die Grundlehren der mathematischen Wissenschaften ; Bd. 202
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33.

図書
図書
33. Mathematical statistics
B.L. van der Waerden
出版情報: Berlin ; New York : Springer-Verlag, 1969  xi, 367 p. ; 24 cm
シリーズ名: Die Grundlehren der mathematischen Wissenschaften ; Bd. 156
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34.

図書
図書
34. 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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目次情報: 続きを見る
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：
35.

図書
図書
35. Техника статистических вычислений
А.К. Митропольский
出版情報: Москва : Гос. изд-во физико-математической лит-ры, 1961  479 p. ; 22 cm
シリーズ名: Физико-математическая библиотека инженера
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36.

図書
図書
36. Nonparametric econometrics (: hardbound ; : pbk)
Adrian Pagan, Aman Ullah
出版情報: Cambridge, England ; New York : Cambridge University Press, 1999  xviii, 424 p. ; 24 cm
シリーズ名: Themes in modern econometrics
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目次情報: 続きを見る
Preface
Introduction / 1：
Methods of Density Estimation / 2：
Nonparametric Density Estimation / 2.1：
A "Local" Histogram Approach / 2.2.1：
A Formal Derivation of andfirac;[subscript 1] (x) / 2.2.2：
Rosenblatt-Parzen Kernel Estimator / 2.2.3：
The Nearest Neighborhood Estimator / 2.2.4：
Variable Window-Width Estimators / 2.2.5：
Series Estimators / 2.2.6：
Penalized Likelihood Estimators / 2.2.7：
The Local Log-Likelihood Estimators / 2.2.8：
Summary / 2.2.9：
Estimation of Derivatives of a Density / 2.3：
Finite-Sample Properties of the Kernel Estimator / 2.4：
The Exact Bias and Variance of the Estimator andfirac; / 2.4.1：
Approximations to the Bias and Variance and Choices of h and K / 2.4.2：
Reduction of Bias / 2.4.3：
Asymptotic Properties of the Kernel Density Estimator andfirac; with Independent Observations / 2.5：
Asymptotic Unbiasedness / 2.5.1：
Consistency / 2.5.2：
Asymptotic Normality / 2.5.3：
Small-Sample Confidence Intervals / 2.5.4：
Sampling Properties of the Kernel Density Estimator with Dependent Observations / 2.6：
Unbiasedness / 2.6.1：
Bibliographical Summary (Approximate and Asymptotic Results) / 2.6.2：
Choices of Window Width and Kernel: Further Discussion / 2.7：
Choice of h / 2.7.1：
Choice of Higher Order Kernels / 2.7.2：
Choice of h for Density Derivatives / 2.7.3：
Multivariate Density Estimation / 2.8：
Testing Hypotheses about Densities / 2.9：
Comparison with a Known Density Function / 2.9.1：
Testing for Symmetry / 2.9.2：
Comparison of Unknown Densities / 2.9.3：
Testing for Independence / 2.9.4：
Examples / 2.10：
Density of Stock Market Returns / 2.10.1：
Estimating the Dickey-Fuller Density / 2.10.2：
Conditional Moment Estimation / 3：
Estimating Conditional Moments by Kernel Methods / 3.1：
Parametric Estimation / 3.2.1：
Nonparametric Estimation: A "Local" Regression Approach / 3.2.2：
Kernel-Based Estimation: A Formal Derivation / 3.2.3：
A General Nonparametric Estimator of m(x) / 3.2.4：
Unifying Nonparametric Estimators / 3.2.5：
Estimation of Higher Order Conditional Moments / 3.2.6：
Finite-Sample Properties / 3.3：
Approximate Results: Stochastic x / 3.3.1：
The Local Linear Regression Estimator / 3.3.2：
Combining Parametric and Nonparametric Estimators / 3.3.3：
Asymptotic Properties / 3.4：
Asymptotic Properties of the Kernel Estimator with Independent Observations / 3.4.1：
Asymptotic Properties of the Kernel Estimator with Dependent Observations / 3.4.2：
Bibliographical Summary (Asymptotic Results) / 3.5：
Implementing the Kernel Estimator / 3.6：
Choice of Window Width / 3.6.1：
Robust Nonparametric Estimation of Moments / 3.7：
Estimating Conditional Moments by Series Methods / 3.8：
Asymptotic Properties of Series Estimators with Independent Observations / 3.9：
Asymptotic Properties of Series Estimators with Dependent Observations / 3.10：
Implementing the Estimator / 3.11：
Imposing Structure on the Conditional Moments / 3.12：
Generalized Additive Models / 3.12.1：
Projection Pursuit Regression / 3.12.2：
Neural Networks / 3.12.3：
Measuring the Affinity of Parametric and Nonparametric Models / 3.13：
A Model of Strike Duration / 3.14：
Earnings-Age Profiles / 3.14.2：
Review of Applied Work on Nonparametric Regression / 3.14.3：
Nonparametric Estimation of Derivatives / 4：
The Model and Partial Derivative Formulae / 4.1：
Estimation / 4.3：
Estimation of Partial Derivatives by Kernel Methods / 4.3.1：
Estimation of Partial Derivatives by Series Methods / 4.3.2：
Estimation of Average Derivatives / 4.3.3：
Local Linear Derivative Estimators / 4.3.4：
Pointwise Versus Average Derivatives / 4.3.5：
Restricted Estimation and Hypothesis Testing / 4.4：
Imposing Linear Equality Restriction on Partial Derivatives / 4.4.1：
Imposing Linear Inequality Restrictions / 4.4.2：
Hypothesis Testing / 4.4.3：
Asymptotic Properties of Partial Derivative Estimators / 4.5：
Asymptotic Properties of Kernel-Based Estimators / 4.5.1：
Series-Based Estimators / 4.5.2：
Higher Order Derivatives / 4.5.3：
Local Linear Estimators / 4.5.4：
Asymptotic Properties of Kernel-Based Average Derivative Estimators / 4.6：
Implementing the Derivative Estimators / 4.7：
Illustrative Examples / 4.8：
A Monte Carlo Experiment with a Production Function / 4.8.1：
Earnings-Age Relationship / 4.8.2：
Review of Applied Work / 4.8.3：
Semiparametric Estimation of Single-Equation Models / 5：
Semiparametric Estimation of the Linear Part of a Regression Model / 5.1：
General Results / 5.2.1：
Diagnostic Tests after Nonparametric Regression / 5.2.2：
Semiparametric Estimation of Some Macro Models / 5.2.3：
The Asymptotic Covariance Matrix of SP Estimators without Asymptotic Independence / 5.2.4：
Efficient Estimation of Semiparametric Models in the Presence of Heteroskedasticity of Unknown Form / 5.3：
Conditions for Adaptive Estimation / 5.4：
Efficient Estimation of Regression Parameters with Unknown Error Density / 5.5：
Efficient Estimation by Likelihood Approximation / 5.5.1：
Efficient Estimation by Kernel-Based Score Approximation / 5.5.2：
Efficient Estimation by Moment-Based Score Approximation / 5.5.3：
Estimation of Scale Parameters / 5.6：
Optimal Diagnostic Tests in Linear Models / 5.7：
Adaptive Estimation with Dependent Observations / 5.8：
M-Estimators / 5.9：
Diagnostic Tests with M-Estimators / 5.9.1：
Sequential M-Estimators / 5.9.3：
The Semiparametric Efficiency Bound for Moment-Based Estimators / 5.10：
Approximating the SP Efficiency Bound by a Conditional Moment Estimator / 5.10.1：
Applications / 5.11：
Semiparametric Estimation of a Heteroskedastic Model / 5.11.1：
Adaptive Estimation of a Model of House Prices / 5.11.2：
Review of Other Applications / 5.11.3：
Semiparametric and Nonparametric Estimation of Simultaneous Equation Models / 6：
Single-Equation Estimators / 6.1：
Rilstone's Semiparametric Two-Stage Least Squares Estimator / 6.2.1：
Systems Estimation / 6.3：
A Parametric Estimator / 6.3.1：
The SP3SLS Estimator / 6.3.2：
Newey's Estimator / 6.3.3：
Newey's Efficient Distribution-Free Estimators / 6.3.4：
Nonparametric Estimation / 6.4：
Identification / 6.5.1：
Nonparametric Two-Stage Least Squares (2SLS) Estimation / 6.5.2：
Semiparametric Estimation of Discrete Choice Models / 7：
Parametric Estimation of Binary Discrete Choice Models / 7.1：
Semiparametric Efficiency Bounds for Binary Discrete Choice Models / 7.3：
Semiparametric Estimation of Binary Discrete Choice Models / 7.4：
Ichimura's Estimator / 7.4.1：
Klein and Spady's Estimator / 7.4.2：
The SNP Maximum Likelihood Estimator / 7.4.3：
Local Maximum Likelihood Estimation / 7.4.4：
Alternative Consistent SP Estimators / 7.5：
Manski's Maximum Score Estimator / 7.5.1：
Horowitz's Smoothed Maximum Score Estimator / 7.5.2：
Han's Maximum Rank Correlation Estimator / 7.5.3：
Cosslett's Approximate MLE / 7.5.4：
An Iterative Least Squares Estimator / 7.5.5：
Derivative-Based Estimators / 7.5.6：
Models with Discrete Explanatory Variables / 7.5.7：
Multinomial Discrete Choice Models / 7.6：
Some Specification Tests for Discrete Choice Models / 7.7：
Semiparametric Estimation of Selectivity Models / 7.8：
Some Parametric Estimators / 8.1：
Some Sequential Semiparametric Estimators / 8.3：
Cosslett's Dummy Variable Method / 8.3.1：
Powell's Kernel Estimator / 8.3.2：
Newey's Series Estimator / 8.3.3：
Newey's GMM Estimator / 8.3.4：
Maximum Likelihood-Type Estimators / 8.4：
Gallant and Nychka's Estimator / 8.4.1：
Estimation of the Intercept in Selection Models / 8.4.2：
Applications of the Estimators / 8.6：
Conclusions / 8.7：
Semiparametric Estimation of Censored Regression Models / 9：
Semiparametric Efficiency Bounds for the Censored Regression Model / 9.1：
The Kaplan-Meier Estimator of the Distribution Function of a Censored Random Variable / 9.4：
Semiparametric Density-Based Estimators / 9.5：
The Semiparametric Generalized Least Squares Estimator (SGLS) / 9.5.1：
Estimators Replacing Part of the Sample / 9.5.2：
Maximum Likelihood Type Estimators / 9.5.3：
Semiparametric Nondensity-Based Estimators / 9.6：
Powell's Censored Least Absolute Deviation (CLAD) Estimator / 9.6.1：
Powell's (1986a) Censored Quantile Estimators / 9.6.2：
Powell's Symmetrically Censored Least Squares Estimators / 9.6.3：
Newey's Efficient Estimator under Conditional Symmetry / 9.6.4：
Comparative Studies of the Estimators / 9.7：
Retrospect and Prospect / 10：
Statistical Methods / A：
Probability Concepts / A.1：
Random Variable and Distribution Function / A.1.1：
Conditional Distribution and Independence / A.1.2：
Borel Measurable Functions / A.1.3：
Inequalities Involving Expectations / A.1.4：
Characteristic Function (c.f.) / A.1.5：
Results on Convergence / A.2：
Weak and Strong Convergence of Random Variables / A.2.1：
Laws of Large Numbers / A.2.2：
Convergence of Distribution Functions / A.2.3：
Central Limit Theorems / A.2.4：
Further Results on the Law of Large Numbers and Convergence in Moments and Distributions / A.2.5：
Convergence in Moments / A.2.6：
Some Probability Inequalities / A.3：
Order of Magnitudes (Small o and Large O) / A.4：
Asymptotic Theory for Dependent Observations / A.5：
Ergodicity / A.5.1：
Mixing Sequences / A.5.2：
Near-Epoch Dependent Sequences / A.5.3：
Martingale Differences and Mixingales / A.5.4：
Rosenblatt's (1970) Measure of Dependence [beta][subscript n] / A.5.5：
Stochastic Equicontinuity / A.5.6：
References
Index
Preface
Introduction / 1：
Methods of Density Estimation / 2：
37.

図書
図書
37. Numerical analysis for statisticians
Kenneth Lange
出版情報: New York : Springer, c1999  xv, 356 p. ; 24 cm
シリーズ名: Statistics and computing
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38.

図書
図書
38. Décompositions des lois de probabilités
par Y.-V. Linnik ; traduit par L. Gruel
出版情報: Paris : Gauthier-Villars, 1962  vi, 293 p. ; 24 cm
シリーズ名: Monographies internationales de mathématiques modernes ; 3
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39.

図書
図書
39. Applied statistics for engineers and scientists
Jay L. Devore, Nicholas R. Farnum
出版情報: Pacific Grove, Calif. : Duxbury Press, c1999  xiv, 577 p. ; 24 cm.
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40.

図書
図書
40. Statistical analysis in biology. 5th ed
by K. Mather ; with a foreword by R.A. Fisher
出版情報: London : Methuen, 1964  267 p. ; 22 cm
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41.

図書
図書
41. 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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42.

図書
図書
42. Model selection and inference : a practical information-theoretic approach (: hard)
Kenneth P. Burnham, David R. Anderson
出版情報: New York : Springer, c1998  xix, 353 p. ; 24 cm
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43.

図書
図書
43. 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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目次情報: 続きを見る
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
44.

図書
図書
44. Local stereology
Eva B. Vedel Jensen
出版情報: Singapore : World Scientific, c1998  xv, 247 p. ; 23 cm
シリーズ名: Advanced series on statistical science & applied probability ; vol. 5
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目次情報: 続きを見る
Preface
List of notation
Introduction to stereology / 1：
Sampling theory / 1.1：
Stereological estimation of number / 1.2：
Stereological estimation of volume / 1.3：
Length and surface area estimation under isotropy / 1.4：
Local stereology / 1.5：
Exercises / 1.6：
Bibliographical notes / 1.7：
The coarea formula / 2：
Hausdorff measures / 2.1：
The special case d = n / 2.2：
Hausdorff measures on affine subspaces / 2.4：
Polar decomposition of Lebesgue measure / 2.5：
Translative decompositions of Hausdorff measures / 2.6：
A transformation result for surface area measure / 2.7：
Simplices / 2.8：
Rotation invariant measures on L[superscript n subscript p] / 2.9：
Construction of rotation invariant measures on L[superscript n subscript p] / 3.1：
Crofton's formula / 3.2：
A result on projections / 3.3：
Pairs of subspaces / 3.4：
Random subspaces / 3.5：
Random grids / 3.6：
The classical Blaschke-Petkantschin formula / 3.7：
A local estimator of planar area / 4.1：
Decompositions involving lines in R[superscript n] / 4.2：
Proof of the classical Blaschke-Petkantschin formula / 4.3：
Local estimators of volume / 4.4：
Local integral geometric formulae for powers of volume / 4.5：
The generalized Blaschke-Petkantschin formula / 4.6：
A local estimator of length in R[superscript 2] / 5.1：
Prerequisites concerning G-factors / 5.2：
Decomposition of a single Hausdorff measure / 5.3：
Decomposition of a product of Hausdorff measures / 5.4：
Local estimators of d-dimensional Hausdorff measure / 5.5：
An alternative estimator of surface area [lambda superscript n-1 subscript n](X) / 5.6：
Local slice formulae / 5.7：
A local estimator of number in R[superscript 3] / 6.1：
A local slice formula for d-dimensional Hausdorff measure / 6.2：
Local slice estimators of d-dimensional Hausdorff measure / 6.3：
The case 0 [ d [ n / 6.4：
Some further developments for n = 3 / 6.6：
Design and implementation of local stereological experiments / 6.7：
Optical sectioning / 7.1：
Implementation of local designs / 7.2：
Local stereological estimators in use / 7.3：
Particle aggregates / 7.4：
Applications of local stereological methods / 7.5：
Systematic sampling along an axis / 7.6：
The circular case / 7.7：
Exercise / 7.8：
The model-based approach / 7.9：
Point processes in R[superscript n] / 8.1：
Marked point processes in R[superscript n] / 8.2：
A few results from invariant measure theory / 8.3：
Estimation of the K-function of the reference point process / 8.4：
Estimation of moments in the mark distribution / 8.5：
Perspectives and future trends / 8.6：
Mathematical and statistical aspects / 9.1：
Affine version of local stereology / 9.2：
Curvatures and other parameters / 9.3：
Future trends / 9.4：
Invariant measure theory / Appendix：
References
Subject index
Preface
List of notation
Introduction to stereology / 1：
45.

図書
図書
45. Selected papers of Hirotugu Akaike (: hbk)
Emanuel Parzen, Kunio Tanabe, Genshiro Kitagawa, editors
出版情報: New York : Springer, c1998  viii, 434 p. ; 24 cm
シリーズ名: Springer series in statistics
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46.

図書
図書
46. Метод наименьших квадратов и основы математико-статистической теории обработки наблюдений. изд. 2-е, доп. и исправленное
Ю.В. Линник
出版情報: Москва : Гос. изд. физико-математической литературы, 1962  349 p. ; 23 cm
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47.

図書
図書
47. Introduction to probability and statistics. 7th ed
William Mendenhall
出版情報: Boston : Duxbury Press, c1987  xvi, 783, 100 p. ; 25 cm
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目次情報: 続きを見る
Introduction: An Invitation to Statistics
The Population and the Sample
Descriptive and Inferential Statistics
Achieving the Objective of Inferential Statistics: The Necessary Steps
Describing Data with Graphs / 1：
Variables and Data / 1.1：
Types of Variables / 1.2：
Graphs for Categorical Data / 1.3：
Graphs for Quantitative Data / 1.4：
Relative Frequency Histograms / 1.5：
Describing Data with Numerical Measures / 2：
Describing a Set of Data with Numerical Measures / 2.1：
Measures of Center / 2.2：
Measures of Variability / 2.3：
On the Practical Significance of the Standard Deviation / 2.4：
A Check on the Calculation of s / 2.5：
Measures of Relative Standing / 2.6：
The Five-Number Summary and the Box Plot / 2.7：
Describing Bivariate Data / 3：
Bivariate Data / 3.1：
Graphs for Qualitative Variables / 3.2：
Scatterplots for Two Quantitative Variables / 3.3：
Numerical Measures for Quantitative Bivariate Data / 3.4：
Probability and Probability Distributions / 4：
The Role of Probability in Statistics / 4.1：
Events and the Sample Space / 4.2：
Calculating Probabilities Using Simple Events / 4.3：
Useful Counting Rules (Optional) / 4.4：
Event Relations and Probability Rules / 4.5：
Conditional Probability, Independence, and the Multiplicative Rule / 4.6：
Bayes' Rule (Optional) / 4.7：
Discrete Random Variables and Their Probability Distributions / 4.8：
Several Useful Discrete Distributions / 5：
Introduction / 5.1：
The Binomial Probability Distribution / 5.2：
The Poisson Probability Distribution / 5.3：
The Hypergeometric Probability Distribution / 5.4：
The Normal Probability Distribution / 6：
Probability Distributions for Continuous Random Variables / 6.1：
Tabulated Areas of the Normal Probability Distribution / 6.2：
The Normal Approximation to the Binomial Probability Distribution (Optional) / 6.4：
Sampling Distributions / 7：
Sampling Plans and Experimental Designs / 7.1：
Statistics and Sampling Distributions / 7.3：
The Central Limit Theorem / 7.4：
The Sampling Distribution of the Sample Mean / 7.5：
The Sampling Distribution of the Sample Proportion / 7.6：
A Sampling Application: Statistical Process Control (Optional) / 7.7：
Large-Sample Estimation / 8：
Where We've Been / 8.1：
Where We're Going--Statistical Inference / 8.2：
Types of Estimators / 8.3：
Point Estimation / 8.4：
Interval Estimation / 8.5：
Estimating the Difference between Two Population Means / 8.6：
Estimating the Difference between Two Binomial Proportions / 8.7：
One-Sided Confidence Bounds / 8.8：
Choosing the Sample Size / 8.9：
Large-Sample Tests of Hypotheses / 9：
Testing Hypotheses about Population Parameters / 9.1：
A Statistical Test of Hypothesis / 9.2：
A Large-Sample Test about a Population Mean / 9.3：
A Large-Sample Test of Hypothesis for the Difference between Two Population Means / 9.4：
A Large-Sample Test of Hypothesis for a Binomial Proportion / 9.5：
A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions / 9.6：
Some Comments on Testing Hypotheses / 9.7：
Inference from Small Samples / 10：
Student's t Distribution / 10.1：
Small-Sample Inferences Concerning a Population Mean / 10.3：
Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples / 10.4：
Small-Sample Inferences for the Difference between Two Means: A Paired-Difference Test / 10.5：
Inferences Concerning a Population Variance / 10.6：
Comparing Two Population Variances / 10.7：
Revisiting the Small-Sample Assumptions / 10.8：
The Analysis of Variance / 11：
The Design of an Experiment / 11.1：
What Is an Analysis of Variance? / 11.2：
The Assumptions for an Analysis of Variance / 11.3：
The Completely Randomized Design: A One-Way Classification / 11.4：
The Analysis of Variance for a Completely Randomized Design / 11.5：
Ranking Population Means / 11.6：
The Randomized Block Design: A Two-Way Classification / 11.7：
The Analysis of Variance for a Randomized Block Design / 11.8：
The a x b Factorial Experiment: A Two-Way Classification / 11.9：
The Analysis of Variance for an a x b Factorial Experiment / 11.10：
Revisiting the Analysis of Variance Assumptions / 11.11：
A Brief Summary / 11.12：
Linear Regression and Correlation / 12：
A Simple Linear Probabilistic Model / 12.1：
The Method of Least Squares / 12.3：
An Analysis of Variance for Linear Regression / 12.4：
Testing the Usefulness of the Linear Regression Model / 12.5：
Diagnostic Tools for Checking the Regression Assumptions / 12.6：
Estimation and Prediction Using the Fitted Line / 12.7：
Correlation Analysis / 12.8：
Multiple Regression Analysis / 13：
The Multiple Regression Model / 13.1：
A Multiple Regression Analysis / 13.3：
A Polynomial Regression Model / 13.4：
Using Quantitative and Qualitative Predictor Variables in a Regression Model / 13.5：
Testing Sets of Regression Coefficients / 13.6：
Interpreting Residual Plots / 13.7：
Stepwise Regression Analysis / 13.8：
Misinterpreting a Regression Analysis / 13.9：
Steps to Follow When Building a Multiple Regression Model / 13.10：
Analysis of Categorical Data / 14：
A Description of the Experiment / 14.1：
Pearson's Chi-Square Statistic / 14.2：
Testing Specified Cell Probabilities: The Goodness-of-Fit Test / 14.3：
Contingency Tables: A Two-Way Classification / 14.4：
Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals / 14.5：
The Equivalence of Statistical Tests / 14.6：
Other Applications of the Chi-Square Test / 14.7：
Nonparametric Statistics / 15：
The Wilcoxon Rank Sum Test: Independent Random Samples / 15.1：
The Sign Test for a Paired Experiment / 15.3：
A Comparison of Statistical Tests / 15.4：
The Wilcoxon Signed-Rank Test for a Paired Experiment / 15.5：
The Kruskal-Wallis H Test for Completely Randomized Designs / 15.6：
The Friedman F[subscript r] Test for Randomized Block Designs / 15.7：
Rank Correlation Coefficient / 15.8：
Summary / 15.9：
Appendix I
Cumulative Binomial Probabilities / Table 1：
Cumulative Poisson Probabilities / Table 2：
Areas under the Normal Curve / Table 3：
Critical Values of t / Table 4：
Critical Values of Chi-Square / Table 5：
Percentage Points of the F Distribution / Table 6：
Critical Values of T for the Wilcoxon Rank Sum Test, n[subscript 1] [less than or equal] n[subscript 2] / Table 7：
Critical Values of T for the Wilcoxon Signed-Rank Test, n = 5(1)50 / Table 8：
Critical Values of Spearman's Rank Correlation Coefficient for a One-Tailed Test / Table 9：
Random Numbers / Table 10：
Percentage Points of the Studentized Range, q[subscript [alpha](k, df) / Table 11：
Answers to Selected Exercises
Index
Credits
Introduction: An Invitation to Statistics
The Population and the Sample
Descriptive and Inferential Statistics
48.

図書
図書
48. Molecular statistics for students of chemistry
L. A. Woodward
出版情報: Oxford [Eng.] : Clarendon Press, 1975  x, 200 p. ; 23 cm
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49.

図書
図書
49. Applied statistical methods : for business, economics, and the social sciences
William L. Carlson, Betty Thorne
出版情報: Upper Saddle River, N.J. : Prentice Hall, c1997  xxiii, 1021 p. ; 24 cm
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目次情報: 続きを見る
Introduction / 1：
Describing the Data / 2：
Descriptive Relationships / 3：
Introduction to Probability / 4：
Discrete Random Variables and Probability Distribution Functions / 5：
Continuous Random Variables and Probability Density Functions / 6：
Two Random Variables / 7：
Sampling and Data Collection / 8：
Distribution of Sample Statistics / 9：
Estimation / 10：
Hypothesis Testing / 11：
Chi Square Tests / 12：
Analysis of Variance / 13：
Simple Least-Squares Regression / 14：
Multiple Regression / 15：
Multiple Regression Extensions / 16：
Time Series and Forecasting / 17：
Quality Assurance / 18：
Probability Tables / Appendix A：
Data Files for Problems and Examples / Appendix B：
Index
Introduction / 1：
Describing the Data / 2：
Descriptive Relationships / 3：
50.

図書
図書
50. Manufacturing analysis for productivity and quality/cost enhancement. 2nd ed
Norbert L. Enrick with the collaboration of Harry E. Mottley, Jr
出版情報: New York, N.Y. : Industrial Press, c1983  viii, 150 p. ; 29 cm
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