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Smoothing methods in statistics

Smoothing methods in statistics (9회 대출)

자료유형
단행본
개인저자
Simonoff, Jeffrey S.
서명 / 저자사항
Smoothing methods in statistics / Jeffrey S. Simonoff.
발행사항
New York :   Springer ,   c1996.  
형태사항
xii, 338 p. : ill. ; 24 cm.
총서사항
Springer series in statistics
ISBN
0387947167 (hard : alk. paper)
서지주기
Includes bibliographical references (p. [290]-320) and indexes.
일반주제명
Smoothing (Statistics)
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008 960229s1996 nyua b 001 0 eng
010 ▼a 96011742
020 ▼a 0387947167 (hard : alk. paper)
035 ▼a KRIC02366299
040 ▼a 221016 ▼c 221016 ▼d 211009
050 0 0 ▼a QA278 ▼b .S526 1996
082 0 0 ▼a 519.5/36 ▼2 20
090 ▼a 519.536 ▼b S599s
100 1 ▼a Simonoff, Jeffrey S.
245 1 0 ▼a Smoothing methods in statistics / ▼c Jeffrey S. Simonoff.
260 ▼a New York : ▼b Springer , ▼c c1996.
300 ▼a xii, 338 p. : ▼b ill. ; ▼c 24 cm.
440 0 ▼a Springer series in statistics
504 ▼a Includes bibliographical references (p. [290]-320) and indexes.
650 0 ▼a Smoothing (Statistics)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고7층/ 청구기호 519.536 S599s 등록번호 111314339 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

1. Introduction.- 1.1 Smoothing Methods: a Nonparametric/Parametric Compromise.- 1.2 Uses of Smoothing Methods.- 1.3 Outline of the Chapters.- Background material.- Computational issues.- Exercises.- 2. Simple Univariate Density Estimation.- 2.1 The Histogram.- 2.2 The Frequency Polygon.- 2.3 Varying the Bin Width.- 2.4 The Effectiveness of Simple Density Estimators.- Background material.- Computational issues.- Exercises.- 3. Smoother Univariate Density Estimation.- 3.1 Kernel Density Estimation.- 3.2 Problems with Kernel Density Estimation.- 3.3 Adjustments and Improvements to Kernel Density Estimation.- 3.4 Local Likelihood Estimation.- 3.5 Roughness Penalty and Spline-Based Methods.- 3.6 Comparison of Univariate Density Estimators.- Background material.- Computational issues.- Exercises.- 4. Multivariate Density Estimation.- 4.1 Simple Density Estimation Methods.- 4.2 Kernel Density Estimation.- 4.3 Other Estimators.- 4.4 Dimension Reduction and Projection Pursuit.- 4.5 The State of Multivariate Density Estimation.- Background material.- Computational issues.- Exercises.- 5. Nonparametrie Regression.- 5.1 Scatter Plot Smoothing and Kernel Regression.- 5.2 Local Polynomial Regression.- 5.3 Bandwidth Selection.- 5.4 Locally Varying the Bandwidth.- 5.5 Outliers and Autocorrelation.- 5.6 Spline Smoothing.- 5.7 Multiple Predictors and Additive Models.- 5.8 Comparing Nonparametric Regression Methods.- Background material.- Computational issues.- Exercises.- 6. Smoothing Ordered Categorical Data.- 6.1 Smoothing and Ordered Categorical Data.- 6.2 Smoothing Sparse Multinomials.- 6.3 Smoothing Sparse Contingency Tables.- 6.4 Categorical Data, Regression, and Density Estimation.- Background material.- Computational issues.- Exercises.- 7. Further Applications of Smoothing.- 7.1 Discriminant Analysis.- 7.2 Goodness-of-Fit Tests.- 7.3 Smoothing-Based Parametric Estimation.- 7.4 The Smoothed Bootstrap.- Background material.- Computational issues.- Exercises.- Appendices.- A. Descriptions of the Data Sets.- B. More on Computational Issues.- References.- Author Index.


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