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Relevant bibliographies by topics / Estimation jackknife

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Academic literature on the topic 'Estimation jackknife'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Estimation jackknife"

1

Sun, Yutao, and Geert Dhaene. "xtspj: A command for split-panel jackknife estimation." Stata Journal: Promoting communications on statistics and Stata 19, no. 2 (2019): 335–74. http://dx.doi.org/10.1177/1536867x19854016.

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In this article, we present a new command, xtspj, that corrects for incidental parameter bias in panel-data models with fixed effects. The correction removes the first-order bias term of the maximum likelihood estimate using the split-panel jackknife method. Two variants are implemented: the jackknifed maximum-likelihood estimate and the jackknifed log-likelihood function (with corresponding maximizer). The model may be nonlinear or dynamic, and the covariates may be predetermined instead of strictly exogenous. xtspj implements the split-panel jackknife for fixed-effects versions of linear, pr

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Kézdi, Gábor, Jinyong Hahn, and Gary Solon. "Jackknife minimum distance estimation." Economics Letters 76, no. 1 (2002): 35–45. http://dx.doi.org/10.1016/s0165-1765(02)00016-2.

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Angrist, J. D., G. W. Imbens, and A. B. Krueger. "Jackknife instrumental variables estimation." Journal of Applied Econometrics 14, no. 1 (1999): 57–67. http://dx.doi.org/10.1002/(sici)1099-1255(199901/02)14:1<57::aid-jae501>3.0.co;2-g.

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Adewara, Johnson A., and Ugochukwu A. Mbata. "Survival Estimation Using Bootstrap, Jackknife and K-Repeated Jackknife Methods." Journal of Modern Applied Statistical Methods 13, no. 2 (2014): 287–306. http://dx.doi.org/10.22237/jmasm/1414815240.

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WALTHER, B. A., and S. MORAND. "Comparative performance of species richness estimation methods." Parasitology 116, no. 4 (1998): 395–405. http://dx.doi.org/10.1017/s0031182097002230.

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In most real-world contexts the sampling effort needed to attain an accurate estimate of total species richness is excessive. Therefore, methods to estimate total species richness from incomplete collections need to be developed and tested. Using real and computer-simulated parasite data sets, the performances of 9 species richness estimation methods were compared. For all data sets, each estimation method was used to calculate the projected species richness at increasing levels of sampling effort. The performance of each method was evaluated by calculating the bias and precision of its estima

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Broemeling, L. D., and R. R. Wolfe. "Measuring intrasubject variability: use of the jacknife in doubly labeled water experiments." Journal of Applied Physiology 75, no. 4 (1993): 1507–12. http://dx.doi.org/10.1152/jappl.1993.75.4.1507.

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The doubly labeled water technique measures energy expenditure; however, very little has appeared in the literature regarding estimation of the intrasubject variation. By use of a statistical resampling procedure called the jackknife, the standard deviation of the determination of energy expenditure in each subject is evaluated. Jackknife methods exploit the regression techniques that are already used with the doubly labeled water technique and are very easy to implement. Estimates of sample sizes for future experiments can easily be done with the jackknife. These formulas give the number of d

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Kudeláš, Jaromír. "The linear model with variance-covariance components and jackknife estimation." Applications of Mathematics 39, no. 2 (1994): 111–25. http://dx.doi.org/10.21136/am.1994.134248.

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8

Chambers, Marcus J., and Maria Kyriacou. "Jackknife estimation with a unit root." Statistics & Probability Letters 83, no. 7 (2013): 1677–82. http://dx.doi.org/10.1016/j.spl.2013.03.016.

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9

Chaudhry, Nazir Ahmed. "The weighted jackknife for ratio estimation." Communications in Statistics - Theory and Methods 19, no. 9 (1990): 3283–313. http://dx.doi.org/10.1080/03610929008830382.

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Poi, Brian P. "Jackknife Instrumental Variables Estimation in Stata." Stata Journal: Promoting communications on statistics and Stata 6, no. 3 (2006): 364–76. http://dx.doi.org/10.1177/1536867x0600600305.

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Dissertations / Theses on the topic "Estimation jackknife"

1

Kyriacou, Maria. "jackknife estimation and inference in non-stationary autoregression." Thesis, University of Essex, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536965.

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Zanuto, Elaine L. (Elaine Louise) Carleton University Dissertation Mathematics. "Jackknife variance estimation under imputation for missing survey data." Ottawa, 1993.

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3

Martin, Estelle. "Estimation par intervalles au moyen du jackknife et de méthodes asymptotiques /." [S.l.] : [s.n.], 2000. http://library.epfl.ch/theses/?nr=2237.

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Adisoma, Gatut Suryoprapto. "The application of the jackknife in geostatistical resource estimation: Robust estimator and its measure of uncertainty." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186547.

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The application of the jackknife in geostatistical resource estimation (in conjunction with kriging) is shown to yield two significant contributions. The first one is a robust new estimator, called jackknife kriging, which retains ordinary kriging's simplicity and global unbiasedness while at the same time reduces its local bias and oversmoothing tendency. The second contribution is the ability, through the jackknife standard deviation, to set a confidence limit for a reserve estimate of a general shape. Jackknifing the ordinary kriging estimate maximizes sample utilization, as well as informa

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Jung, Aekyung. "Interval Estimation for the Correlation Coefficient." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/109.

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The correlation coefficient (CC) is a standard measure of the linear association between two random variables. The CC plays a significant role in many quantitative researches. In a bivariate normal distribution, there are many types of interval estimation for CC, such as z-transformation and maximum likelihood estimation based methods. However, when the underlying bivariate distribution is unknown, the construction of confidence intervals for the CC is still not well-developed. In this thesis, we discuss various interval estimation methods for the CC. We propose a generalized confidence interv

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Ahmed, Mohamed Hafez Soliman. "Statistiques réduisant le biais et modèles de rééchantillonnage complet et incomplet." Paris 6, 1986. http://www.theses.fr/1986PA066442.

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Durot, Cécile. "Asymptotique fine pour l'estimateur isotonique en régression et méthodes de jackknife : applications à la comparaison de courbes de croissance." Paris 11, 1997. http://www.theses.fr/1997PA112007.

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Dunning, Allison. "Comparing Bootstrap and Jackknife Variance Estimation Methods for Area Under the ROC Curve Using One-Stage Cluster Survey Data." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1849.

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The purpose of this research is to examine the bootstrap and jackknife as methods for estimating the variance of the AUC from a study using a complex sampling design and to determine which characteristics of the sampling design effects this estimation. Data from a one-stage cluster sampling design of 10 clusters was examined. Factors included three true AUCs (.60, .75, and .90), three prevalence levels (50/50, 70/30, 90/10) (non-disease/disease), and finally three number of clusters sampled (2, 5, or 7). A simulated sample was constructed for each of the 27 combinations of AUC, prevalence

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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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Aubry, Philippe. "Le traitement des variables régionalisées en écologie : apports de la géomatique et de la géostatistique." Lyon 1, 2000. http://www.theses.fr/2000LYO10013.

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Face à la contradiction consistant à traiter les variables régionalisées écologiques sans tenir compte de leurs propriétés spatiales, nous développons des méthodes géomatiques, utilisant des techniques informatiques, et géostatiques, appliquant la théorie des fonctions aléatoires. Après avoir introduit des éléments de géomatique et de géostatique, et précisé la nature spécifique de l'autocorrélation spatiale, nous introduisons les inférences design-basedet model-based. Á l'aide de fonctions aléatoires, nous étudions l'efficacité de l'échantillonnage, optimisons les prédicteurs et calculons des

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Books on the topic "Estimation jackknife"

1

Shao, Jun. The jackknife and bootstrap. Springer Verlag, 1995.

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The Jackknife and Bootstrap. 2nd ed. Springer, 1996.

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The Jackknife and Bootstrap. Springer, 2011.

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4

Shao, Jun. The Jackknife and Bootstrap. Springer, 2012.

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5

Dunn, Jennifer Louise. IRT parameter estimation: Can the jackknife improve accuracy? 2004.

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Dolatabadi, Mohammad. On the jackknife variance estimation with imputed data sets. 2005.

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7

Efron, Bradley. The Jackknife, the Bootstrap, and Other Resampling Plans (CBMS-NSF Regional Conference Series in Applied Mathematics). Society for Industrial Mathematics, 1987.

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Book chapters on the topic "Estimation jackknife"

1

Gong, Gail. "Appendix B: Cross-Validation, the Jackknife, and the Bootstrap: Excess Error Estimation in Forward Logistic Regression." In Common Errors in Statistics (and How to Avoid Them). John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0471998524.app2.

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Jeske, Daniel R. "Jackknife Bias Correction of a Clock Offset Estimator." In Advances in Mathematical and Statistical Modeling. Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4626-4_18.

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Righi, Paolo, Stefano Falorsi, and Andrea Fasulo. "A Modified Extended Delete a Group Jackknife Variance Estimator Under Random Hot Deck Imputation in Business Surveys." In Contributions to Statistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05320-2_14.

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Peddada, Shyamal D. "21 Jackknife variance estimation and bias reduction." In Handbook of Statistics. Elsevier, 1993. http://dx.doi.org/10.1016/s0169-7161(05)80145-2.

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Henderson, Peter A. "Introduction to the Study of Animals." In Southwood's Ecological Methods. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862277.003.0001.

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Because the objective of a study will largely determine the methods used, it is essential to define the objectives at the outset. Very broadly, studies may be defined as either extensive and intensive. Extensive studies are carried out over larger areas or longer time periods than intensive studies, and are frequently used to provide information on distribution and abundance for conservation or management programmes. Intensive studies involve the repeated observation of the population of an animal. The different types of population estimates—absolute, relative, and intensity—are described. The estimation of error and confidence intervals, including jackknife and bootstrap techniques, is described.

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Singh, Sarjinder, Stephen A. Sedory, Maria del Mar Rueda, Antonio Arcos, and Raghunath Arnab. "Tuning of jackknife estimator." In A New Concept for Tuning Design Weights in Survey Sampling. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-08-100594-1.00002-4.

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7

"Appendix B: Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals." In Statistical Methods in Diagnostic Medicine. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470906514.app2.

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Conference papers on the topic "Estimation jackknife"

1

Bargawa, Waterman Sulistyana. "Mineral resource estimation using weighted jackknife kriging." In PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON SYNCHROTRON RADIATION INSTRUMENTATION – SRI2015. Author(s), 2016. http://dx.doi.org/10.1063/1.4958541.

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Oshiro, Kevin, Changlong Wu, and Narayana P. Santhanam. "Jackknife estimation for Markov processes with no mixing constraints." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8007084.

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Jekel, Charles F., and Vicente Romero. "Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation." In ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5127.

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Abstract Tolerance Interval Equivalent Normal (TI-EN) and Superdistribution (SD) sparse-sample uncertainty quantification (UQ) methods are used for conservative estimation of small tail probabilities. These methods are used to estimate the probability of a response laying beyond a specified threshold with limited data. The study focused on sparse-sample regimes ranging from N = 2 to 20 samples, because this is reflective of most experimental and some expensive computational situations. A tail probability magnitude of 10−4 was examined on four different distribution shapes, in order to be relev

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Reports on the topic "Estimation jackknife"

1

Angrist, Joshua, Guido Imbens, and Alan Krueger. Jackknife Instrumental Variables Estimation. National Bureau of Economic Research, 1995. http://dx.doi.org/10.3386/t0172.

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2

Liu, Yong. Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264960.

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Basak, Gopal, Ravi Jagannathan, and Tongshu Ma. A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10447.

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