1. |
Early Hawaiian Statistics |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 1-3
RobertC. Schmitt,
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摘要:
Primitive statistical reporting systems existed in Hawaii long before the earliest contact with the West in 1778. These systems continued, with modest changes introduced by early foreign visitors and residents, until 1820. The arrival of American missionaries in that year brought modern statistics to the Islands and by 1850 Hawaii enjoyed a surprisingly sophisticated system of statistical reporting and usage.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479294
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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2. |
The Evaluation of Medical Screening Procedures |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 4-11
JudithD. Goldberg,
JanetT. Wittes,
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摘要:
Criteria for assessing the effectiveness of a medical screening program are difficult to define; medical knowledge and screening procedures change rapidly, and self-selection at medical screens is unavoidable. This article discusses these and other basic issues in evaluation of medical screening programs with particular reference to results from the HIP breast cancer study. In addition, the article reviews various statistical models that describe the processes of disease and screening. The models are shown to be statistically indistinguishable in practice because of the small sample sizes typically available in medical screening trials. Finally the article suggests incorporating knowledge from clinical trials and from studies of robustness into statistical models designed to identify reasonable strategies for screening.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479295
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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3. |
Geometry of Ridge Regression Illustrated |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 12-15
BeneeF. Swindel,
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PDF (346KB)
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摘要:
For tutorial purposes ridge traces are displayed in estimation space for repeated samples from a completely known population. Figures given illustrate the initial advantages accruing to ridge-type shrinkage of the least squares coefficients, especially in some cases of near collinearity. The figures also show that other shrunken estimators may perform better or worse, depending on the parameters and design matrix; and they illustrate the problem of choosing a shrinkage parameter or stopping rule. Thus the figures help motivate results previously established algebraically.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479296
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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4. |
Some Computational and Model Equivalences in Analyses of Variance of Unequal-Subclass-Numbers Data |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 16-33
S.R. Searle,
F.M. Speed,
H.V. Henderson,
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摘要:
Available methodologies for calculating sums of squares in analyses of variance of unequal-subclass-numbers (unbalanced) data include (i) full rank reparameterized models, (ii) “indirect” methods, (iii) theR(· | ·) notation, (iv) weighted squares of means, and (v) numerator sums of squares for testing hypotheses. These techniques are described, and relationships between them explained and illustrated. Numerical illustrations are given in the Appendix.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479297
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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5. |
An Improved Method of Estimating an Integer-Parameter by Maximum Likelihood |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 34-37
RamC. Dahiya,
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摘要:
A simple graphical method is described for obtaining the maximum likelihood estimator of an integer-valued parameter. The method does not use calculus and is easy to comprehend. The use of this method is shown in the specific cases of the binomial, Poisson, and the exponential distributions. A numerical example is also provided for the comparison of this method with the method used previously.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479298
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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6. |
William Gemmell Cochran 1909–1980 |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 38-38
ArthurP. Dempster,
Frederick Mosteller,
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ISSN:0003-1305
DOI:10.1080/00031305.1981.10479299
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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7. |
Sidney Addelman 1932–1979 |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 39-39
M.M. Desu,
NormanC. Severe,
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ISSN:0003-1305
DOI:10.1080/00031305.1981.10479300
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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8. |
On the Determination and Use of Optimal Sample Sizes for Estimating the Difference in Means |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 40-42
DavidW. Pentico,
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PDF (240KB)
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摘要:
The determination of optimal sample sizes for estimating the difference between population means to a desired degree of confidence and precision is a question of economic significance. This question, however, is generally not discussed in statistics texts. Sample sizes to minimize linear sampling costs are proportional to the population standard deviations and inversely proportional to the square roots of the unit sampling costs. Sensitivity analysis shows that the impact of the use of equal rather than optimal sample sizes on the amount of sampling and its cost is not great as long as the unit costs and population variances are comparable.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479301
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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9. |
L'Hospital's Rule and the Central Limit Theorem |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 43-43
RobertM. Tardiff,
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摘要:
Beginning probability students are often confused by the use of Taylor polynomials in the proof of the central limit theorem. This article provides a proof of the central limit theorem based on L'Hospital's rule rather than on Taylor polynomials.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479302
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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10. |
Moments of Discrete Probability Distributions Derived Using Finite Difference Operators |
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The American Statistician,
Volume 35,
Issue 1,
1981,
Page 44-46
RobertF. Link,
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PDF (214KB)
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摘要:
Formulas for the moments of the better known probability distribution functions are available in the literature on the subject. Persons wishing to derive these formulas, however, may find standard methods to be quite laborious. For discrete probability functions, surprisingly compact and elegant derivations may be obtained by using finite difference operators. Examples of this approach are presented.
ISSN:0003-1305
DOI:10.1080/00031305.1981.10479303
出版商:Taylor & Francis Group
年代:1981
数据来源: Taylor
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