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Published **1972**
by M. Dekker in New York .

Written in English

- Jackknife (Statistics),
- Estimation theory.

**Edition Notes**

Bibliography: p. 303-306.

Statement | [by] H. L. Gray and W. R. Schucany. |

Series | Statistics: textbooks and monographs, v. 1 |

Contributions | Schucany, W. R., joint author. |

Classifications | |
---|---|

LC Classifications | QA276.8 .G7 |

The Physical Object | |

Pagination | x, 308 p. |

Number of Pages | 308 |

ID Numbers | |

Open Library | OL5223509M |

ISBN 10 | 0824712455 |

LC Control Number | 75179385 |

was inspired by the previous success of the Jackknife procedure.1 Imagine that a sample of nindependent, identically distributed observations from an unknown distribution have been gathered, and a mean of the sample, Y, has been Size: KB. Jackknife and bootstrap estimates of these quantities are introduced along with some heuristic justifications. Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core. The flexibility of the definition of the first-order generalized jackknife is exploited so that its relation to the method of statistical differentials can be seen. The estimators presented have the same bias reduction and asymptotic distributional properties as the usual generalized jackknife. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data : Springer-Verlag New York.

"This books breaks away form more theoretically burdensome texts, focusing on providing a set of useful tools that help readers understand the theoretical under pinning of statistical methodology."--SciTech Book News, March "This (hardback) book is one of the most up-to-date and easily understood texts in the field of mathematical statistics. fails for non-smooth statistics, such as the sample median. If µ^ n denotes the sample median in the univariate case, then in general, VarJ(^µn)=Var(µ^n)! µ 1 2 ´2 2 2 in distribution, where ´2 2 denotes a chi-square random variable with 2 degrees of freedom (see Efron , x). So in this case, the jackknife method does not lead. General Properties of Distributions; I am sorry, but I have not included this topic as yet on the Real Statistics website. I expect to be adding Baysian statistics topics in the future. Charles. When I use the jack knife procedure as described i get the same 95% CI as the excel addon “Analyse-it” but the 95% CI from the commercial. The jackknife is consistent for the sample means, sample variances, central and non-central t-statistics (with possibly non-normal populations), sample coefficient of variation, maximum likelihood estimators, least squares estimators, correlation coefficients and regression coefficients.

Additional Physical Format: Online version: Gray, Henry L. Generalized jackknife statistic. New York: M. Dekker, (OCoLC) Material Type. The jackknife only works well for linear statistics (e.g., mean). It fails to give accurate estimation for non-smooth (e.g., median) and nonlinear (e.g., correlation coefficient) cases. Thus improvements to this technique were developed. Delete-d jackknife. In statistics, the jackknife is a resampling technique especially useful for variance and bias estimation. The jackknife pre-dates other common resampling methods such as the bootstrap. The jackknife estimator of a parameter is found by systematically leaving out each observation from a dataset and calculating the estimate and then finding the average of these calculations. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.

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