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1.

図書
図書
1. 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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2.

図書
図書
2. 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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3.

図書
図書
3. 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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4.

図書
図書
4. 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：
5.

図書
図書
5. 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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6.

図書
図書
6. 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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7.

図書
図書
7. 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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8.

図書
図書
8. 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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9.

図書
図書
9. 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：
10.

図書
図書
10. 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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