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Relevant bibliographies by topics / Infinitesimal jackknife

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Journal articles
Dissertations / Theses
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Academic literature on the topic 'Infinitesimal jackknife'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Infinitesimal jackknife"

1

Zhang, Guangjian, Kristopher J. Preacher, and Robert I. Jennrich. "The Infinitesimal Jackknife with Exploratory Factor Analysis." Psychometrika 77, no. 4 (2012): 634–48. http://dx.doi.org/10.1007/s11336-012-9281-5.

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2

Gottlieb, Alex D. "Asymptotic equivalence of the jackknife and infinitesimal jackknife variance estimators for some smooth statistics." Annals of the Institute of Statistical Mathematics 55, no. 3 (2003): 555–61. http://dx.doi.org/10.1007/bf02517807.

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3

Daudin, J. J., C. Duby, and P. Trécourt. "Pca stability studied by the bootstrap and the infinitesimal jackknife method." Statistics 20, no. 2 (1989): 255–70. http://dx.doi.org/10.1080/02331888908802168.

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Jennrich, Robert, and Albert Satorra. "The Infinitesimal Jackknife and Moment Structure Analysis Using Higher Order Moments." Psychometrika 81, no. 1 (2014): 90–101. http://dx.doi.org/10.1007/s11336-014-9426-9.

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Jennrich, Robert I. "Nonparametric Estimation of Standard Errors in Covariance Analysis Using the Infinitesimal Jackknife." Psychometrika 73, no. 4 (2008): 579–94. http://dx.doi.org/10.1007/s11336-008-9083-y.

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Zhang, Zheng, Xianjun Shi, Xiaogang Xiang, Chengyong Wang, Shiwu Xiao, and Xiaogang Su. "Bootstrap confidence intervals for the optimal cutoff point to bisect estimated probabilities from logistic regression." Statistical Methods in Medical Research 29, no. 6 (2019): 1514–26. http://dx.doi.org/10.1177/0962280219864998.

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To classify estimated probabilities from a logistic regression model into two groups (e.g., yes or no, disease or no disease), the optimal cutoff point or threshold is crucial. While various methods have been proposed for estimating such a threshold, statistical inference is not generally available. To tackle this issue, we put forward several bootstrap based methods, including the conventional nonparametric bootstrap standard errors and the quantile interval. Special emphasis is placed on a more precise bagging estimator of the optimal cutoff point, for which a confidence interval can be obta

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7

Gu, Fei, Hao Wu, Yiu-Fai Yung, and Jesse L. M. Wilkins. "Standard error estimates for rotated estimates of canonical correlation analysis: an implementation of the infinitesimal jackknife method." Behaviormetrika 48, no. 1 (2021): 143–68. http://dx.doi.org/10.1007/s41237-020-00123-7.

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8

Millar, Russell B. "Maximum Likelihood Estimation of Mixed Stock Fishery Composition." Canadian Journal of Fisheries and Aquatic Sciences 44, no. 3 (1987): 583–90. http://dx.doi.org/10.1139/f87-071.

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In this paper the maximum likelihood method of estimating stock composition is initially presented under the assumption that all of the sampled characteristics are of discrete type (electrophoretic, meristic). The extension to allow continuous data (morphometric) is seen to follow from just a slight change in notation. It is shown that the classification method with error correction is a special case of the maximum likelihood method. Results from finite mixture theory are used to show that, in practice, the method is well defined and that the maximum likelihood estimates of composition are uni

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Brokamp, Cole, MB Rao, Patrick Ryan, and Roman Jandarov. "A comparison of resampling and recursive partitioning methods in random forest for estimating the asymptotic variance using the infinitesimal jackknife." Stat 6, no. 1 (2017): 360–72. http://dx.doi.org/10.1002/sta4.162.

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Dissertations / Theses on the topic "Infinitesimal jackknife"

1

Brokamp, Richard C. "Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1463130851.

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Book chapters on the topic "Infinitesimal jackknife"

1

Jennrich, Robert, and Albert Satorra. "The Infinitesimal Jackknife and Analysis of Higher Order Moments." In Quantitative Psychology Research. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19977-1_16.

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