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Relevant bibliographies by topics / Jackknife

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Academic literature on the topic 'Jackknife'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2024

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Journal articles on the topic "Jackknife"

1

Matyska, L., and J. Kovář. "Comparison of several non-linear-regression methods for fitting the Michaelis-Menten equation." Biochemical Journal 231, no. 1 (October 1, 1985): 171–77. http://dx.doi.org/10.1042/bj2310171.

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The known jackknife methods (i.e. standard jackknife, weighted jackknife, linear jackknife and weighted linear jackknife) for the determination of the parameters (as well as of their confidence regions) were tested and compared with the simple Marquardt's technique (comprising the calculation of confidence intervals from the variance-co-variance matrix). The simulated data corresponding to the Michaelis-Menten equation with defined structure and magnitude of error of the dependent variable were used for fitting. There were no essential differences between the results of both point and interval parameter estimations by the tested methods. Marquardt's procedure yielded slightly better results than the jackknives for five scattered data points (the use of this method is advisable for routine analyses). The classical jackknife was slightly superior to the other methods for 20 data points (this method can be recommended for very precise calculations if great numbers of data are available). The weighting does not seem to be necessary in this type of equation because the parameter estimates obtained with all methods with the use of constant weights were comparable with those calculated with the weights corresponding exactly to the real error structure whereas the relative weighting led to rather worse results.

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Sun, Yutao, and Geert Dhaene. "xtspj: A command for split-panel jackknife estimation." Stata Journal: Promoting communications on statistics and Stata 19, no. 2 (June 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, probit, logit, Poisson, exponential, gamma, Weibull, and negbin2 regressions. It also accommodates other models if the user specifies the log-likelihood function (and, possibly but not necessarily, the score function and the Hessian). xtspj is fast and memory efficient, and it allows large datasets. The data may be unbalanced. xtspj can also be used to compute uncorrected maximum-likelihood estimates of fixed-effects models for which no other xt (see [XT] xt) command exists.

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Fearn, Tom. "The Jackknife." NIR news 11, no. 5 (October 2000): 5–6. http://dx.doi.org/10.1255/nirn.580.

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Arrasyid, Arya Huda, Dwi Ispriyanti, and Abdul Hoyyi. "METODE MODIFIED JACKKNIFE RIDGE REGRESSION DALAM PENANGANAN MULTIKOLINIERITAS (STUDI KASUS INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH)." Jurnal Gaussian 10, no. 1 (February 28, 2021): 104–13. http://dx.doi.org/10.14710/j.gauss.v10i1.29922.

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The human development index is a value where the value showed the measure of living standards comparison in a region. The Human Development Index is influenced by several factors, one of them is the education factor that is the average years of schooling and expected years of schooling. A statistical method to find the correlation between the independent variable and the dependent variable can be conducted using the linear regression method. Linear regression requires several assumptions, one of which is the multicollinearity assumption. If the multicollinearity assumption is not fulfilled, another alternative is needed to estimate the regression parameters. One method that can be used to estimate regression parameters is the ridge regression method with an ordinary ridge regression estimator. Ordinary ridge regression then developed more into several methods, such as generalized ridge regression, jackknife ridge regression, and modified jackknife ridge regression method. The generalized Ridge Regression method causes a reduction to variance in linear regression, while the jackknife ridge regression method is obtained by resampling jackknife process on the generalized ridge regression method. Modified jackknife ridge regression is a combination of generalized ridge regression and jackknife ridge regression method. In this journal, the three ridge regression methods will be compared based on the Mean Squared Error obtained in each method. The results of this study indicate that the jackknife ridge regression method has the smallest MSE value. Keywords: Generalized Ridge Regression, Jackknife Ridge Regression, Modified Jackknife Ridge Regression, Multicolinearity

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Jing, Bing-Yi, Junqing Yuan, and Wang Zhou. "Jackknife Empirical Likelihood." Journal of the American Statistical Association 104, no. 487 (September 2009): 1224–32. http://dx.doi.org/10.1198/jasa.2009.tm08260.

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Hansen, Bruce E., and Jeffrey S. Racine. "Jackknife model averaging." Journal of Econometrics 167, no. 1 (March 2012): 38–46. http://dx.doi.org/10.1016/j.jeconom.2011.06.019.

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Brezinski, Claude. "Implementing the jackknife." Applied Mathematics and Computation 42, no. 2 (March 1991): 111–19. http://dx.doi.org/10.1016/0096-3003(91)90047-q.

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Balloun, Joseph L., and A. Ben Oumlil. "JACKKNIFE: a general-purpose package for generating multivariate jackknife analyses." Behavior Research Methods, Instruments, & Computers 18, no. 1 (January 1986): 47–49. http://dx.doi.org/10.3758/bf03200994.

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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 (November 1, 2014): 287–306. http://dx.doi.org/10.22237/jmasm/1414815240.

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Papalia, M. Fikar, Solimun Solimun, and Nurjannah Nurjannah. "COMPARISON OF RESAMPLING EFFICIENCY LEVELS OF JACKKNIFE AND DOUBLE JACKKNIFE IN PATH ANALYSIS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 2 (June 11, 2023): 0807–18. http://dx.doi.org/10.30598/barekengvol17iss2pp0807-0818.

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The assumption of normality is often not fulfilled, this causes the estimation of the resulting parameters to be less efficient. The problem with assuming that normality is not satisfied can be overcome by resampling. The use of resampling allows data to be applied free of distributional assumptions. In this study, a research simulation was carried out by applying Jackknife resampling and Double Jackknife resampling in path analysis with the assumption that the normality of the residuals was not fulfilled and the number of resampling was set at 100 with the degree of closeness level of relationship between variables consisting of low closeness, medium closeness, and high closeness. Based on the simulation results, resampling with a power of 100 can overcome the problem of unfulfilled normality assumptions. In addition, the comparison of the relative efficiency level of the resampling jackknife and double jackknife in the path analysis obtained by the resampling double jackknife has more efficiency than the resampling jackknife

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Dissertations / Theses on the topic "Jackknife"

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Dunn, Ashley L. "Jackknife stability of articulated tractor semitrailer vehicles with high-output brakes and jackknife detection on low coefficient surfaces." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1061328963.

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Dunn, Ashley Liston. "Jackknife stability of articulated tractor semitrailer vehicles with high-output brakes and jackknife detection on low coefficient surfaces." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1061328963.

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Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xxiv, 319 p.; also includes graphics. Includes abstract and vita. Advisors: Dennis Guenther and Georgio Rizzoni, Dept. of Mechanical Engineering. Includes bibliographical references (p. 314-319).

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Yang, Hanfang. "Jackknife Emperical Likelihood Method and its Applications." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/9.

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In this dissertation, we investigate jackknife empirical likelihood methods motivated by recent statistics research and other related fields. Computational intensity of empirical likelihood can be significantly reduced by using jackknife empirical likelihood methods without losing computational accuracy and stability. We demonstrate that proposed jackknife empirical likelihood methods are able to handle several challenging and open problems in terms of elegant asymptotic properties and accurate simulation result in finite samples. These interesting problems include ROC curves with missing data, the difference of two ROC curves in two dimensional correlated data, a novel inference for the partial AUC and the difference of two quantiles with one or two samples. In addition, empirical likelihood methodology can be successfully applied to the linear transformation model using adjusted estimation equations. The comprehensive simulation studies on coverage probabilities and average lengths for those topics demonstrate the proposed jackknife empirical likelihood methods have a good performance in finite samples under various settings. Moreover, some related and attractive real problems are studied to support our conclusions. In the end, we provide an extensive discussion about some interesting and feasible ideas based on our jackknife EL procedures for future studies.

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Ma, Genuo. "JACKKNIFE MODEL AVERAGING ON FUNCTIONAL LOGISTIC MODEL." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413059.

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Chen, Ying-Ju. "Jackknife Empirical Likelihood And Change Point Problems." Bowling Green State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1430823961.

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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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Zulj, Valentin. "On The Jackknife Averaging of Generalized Linear Models." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412831.

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Frequentist model averaging has started to grow in popularity, and it is considered a good alternative to model selection. It has recently been applied favourably to gen- eralized linear models, where it has mainly been purposed to aid the prediction of probabilities. The performance of averaging estimators has largely been compared to that of models selected using AIC or BIC, without much discussion of model screening. In this paper, we study the performance of model averaging in classification problems, and evaluate performances with reference to a single prediction model tuned using cross-validation. We discuss the concept of model screening and suggest two methods of constructing a candidate model set; averaging over the models that make up the LASSO regularization path, and the so called LASSO-GLM hybrid. By means of a Monte Carlo simulation study, we conclude that model averaging does not necessarily offer any improvement in classification rates. In terms of risk, however, we see that both methods of model screening are efficient, and their errors are more stable than those achieved by the cross-validated model of comparison.

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Ma, Zhengbo. "A New Jackknife Empirical Likelihood Method for U-Statistics." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/97.

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U-statistics generalizes the concept of mean of independent identically distributed (i.i.d.) random variables and is widely utilized in many estimating and testing problems. The standard empirical likelihood (EL) for U-statistics is computationally expensive because of its onlinear constraint. The jackknife empirical likelihood method largely relieves computation burden by circumventing the construction of the nonlinear constraint. In this thesis, we adopt a new jackknife empirical likelihood method to make inference for the general volume under the ROC surface (VUS), which is one typical kind of U-statistics. Monte Carlo simulations are conducted to show that the EL confidence intervals perform well in terms of the coverage probability and average length for various sample sizes.

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meng, xueping. "Jackknife Empirical Likelihood Inference for the Absolute Mean Deviation." Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/math_theses/132.

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In statistics it is of interest to find a better interval estimator of the absolute mean deviation. In this thesis, we focus on using the jackknife, the adjusted and the extended jackknife empirical likelihood methods to construct confidence intervals for the mean absolute deviation of a random variable. The empirical log-likelihood ratio statistics is derived whose asymptotic distribution is a standard chi-square distribution. The results of simulation study show the comparison of the average length and coverage probability by using jackknife empirical likelihood methods and normal approximation method. The proposed adjusted and extended jackknife empirical likelihood methods perform better than other methods for symmetric and skewed distributions. We use real data sets to illustrate the proposed jackknife empirical likelihood methods.

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Asterios, Geroukis. "Prediction of Linear Models: Application of Jackknife Model Averaging." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297671.

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When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesian Information Criterion and Mallows’s Cp. The results show that the Jackknife Model Averaging technique gives less prediction errors compared to the other methods of model prediction. This study concludes that the Jackknife Model Averaging technique might be a useful choice when predicting data.

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Books on the topic "Jackknife"

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Johnstone, William W. Jackknife. New York: Kensington Pub. Corp., 2008.

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Shao, Jun, and Dongsheng Tu. The Jackknife and Bootstrap. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0795-5.

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Shao, Jun. The jackknife and bootstrap. New York, NY, USA: Springer Verlag, 1995.

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Kott, Phillip S. Using the delete-a-group jackknife variance estimator in NASS surveys. Washington, DC: U.S. Dept. of Agriculture, National Agricultural Statistics Service, Research Division, 1998.

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Johnstone, William, and J. A. Johnstone. Jackknife. Kensington Publishing Corporation, 2008.

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Haywood, Cecil. Jackknife. BookSurge Publishing, 2002.

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Jackknife. New York: Kensington Publishing Corp., 2008.

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Murphy, Michelle. Jackknife & Light. A V E C Books, 1998.

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Nobody's Jackknife. West End Press, 2016.

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Shao, Jun, and Dongsheng Tu. Jackknife and Bootstrap. Springer London, Limited, 2012.

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Book chapters on the topic "Jackknife"

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Boos, Denni D., and L. A. Stefanski. "Jackknife." In Springer Texts in Statistics, 385–411. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4818-1_10.

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Maesono, Yoshihiko. "Jackknife." In International Encyclopedia of Statistical Science, 697–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_317.

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Efron, Bradley, and Robert J. Tibshirani. "The jackknife." In An Introduction to the Bootstrap, 141–52. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4541-9_11.

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Jiang, Jiming. "Jackknife and Bootstrap." In Springer Texts in Statistics, 471–521. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6827-2_14.

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Rizzo, Maria L. "Bootstrap and Jackknife." In Statistical Computing with R, 213–42. Second edition. | Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192760-8.

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Jiang, Jiming. "Jackknife and Bootstrap." In Springer Texts in Statistics, 507–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91695-4_14.

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Vexler, Albert, and Alan D. Hutson. "Jackknife and Bootstrap Methods." In Statistics in the Health Sciences, 259–304. Boca Raton, Florida : CRC Press, [2018]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b21899-11.

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Shao, Jun, and Dongsheng Tu. "Theory for the Jackknife." In The Jackknife and Bootstrap, 23–70. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0795-5_2.

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Shao, Jun, and Dongsheng Tu. "Introduction." In The Jackknife and Bootstrap, 1–22. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0795-5_1.

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Shao, Jun, and Dongsheng Tu. "Bayesian Bootstrap and Random Weighting." In The Jackknife and Bootstrap, 416–46. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0795-5_10.

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Conference papers on the topic "Jackknife"

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Taranta II, Eugene M., Amirreza Samiei, Mehran Maghoumi, Pooya Khaloo, Corey R. Pittman, and Joseph J. LaViola Jr. "Jackknife." In CHI '17: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3025453.3026002.

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Wang, Jin, J. Sunil Rao, and Jun Shao. "Weighted jackknife-after-bootstrap." In the 29th conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/268437.268486.

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Chakraborty, Dev P., and Kevin S. Berbaum. "Jackknife free-response ROC methodology." In Medical Imaging 2004, edited by Dev P. Chakraborty and Miguel P. Eckstein. SPIE, 2004. http://dx.doi.org/10.1117/12.533319.

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Maiboroda, Rostyslav, and Vitaliy MIroshnychenko. "Jackknife Estimator Consistency for Nonlinear Mixture." In 4th International Conference on Statistics: Theory and Applications (ICSTA'22). Avestia Publishing, 2022. http://dx.doi.org/10.11159/icsta22.149.

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Shao, Jun, and Kwok-Leung Tsui. "Form Tolerance Estimation Using Jackknife Methods." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0814.

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Abstract A coordinate measuring machine (CMM) is a computer controlled device that uses a programmable probe to obtain measurements on a part surface. Recently CMMs have become very popular for dimensional measurement in industry due to their flexibility, accuracy, and ease of automation. Despite the advantages offered by CMM’s, problems have emerged with their use because tolerance standards require knowledge of the entire surface while a CMM provides only a sample of points on the surface. These problems could be quite challenging, and both practitioners and researchers have shown great interest. Among these problems, estimating form tolerances for different part features is very important to practitioners. The least squares and minimum zone methods are the most commonly used methods for form tolerance estimation. Dowling et al. (1996a) show that these two methods give seriously biased estimates of the part deviation range when the sample size is small. This paper proposes several jackknife estimates that correct the bias of the least squares and minimum zone estimates. Based on a simulation study, it is found that the jackknife estimates effectively reduce the bias of the two common estimates in many situations, and thus reduce the chance of accepting bad parts in tolerance verification.

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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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Bhatti, Bilal Ahmad, and Robert Broadwater. "Solar Photovoltaic output prediction using Jackknife Regression." In 2018 North American Power Symposium (NAPS). IEEE, 2018. http://dx.doi.org/10.1109/naps.2018.8600676.

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Partani, Amit, David Morton, and Ivilina Popova. "Jackknife Estimators for Reducing Bias in Asset Allocation." In 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.323159.

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Kang, Jian, Qinghai Zhou, and Hanghang Tong. "JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539286.

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Fang, Loh Yue, Jayanthi Arasan, Habshah Midi, and Mohd Rizam Abu Bakar. "Jackknife and bootstrap inferential procedures for censored survival data." In THE 22ND NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM22): Strengthening Research and Collaboration of Mathematical Sciences in Malaysia. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4934631.

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Reports on the topic "Jackknife"

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

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Liu, Yong. Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method. Fort Belvoir, VA: Defense Technical Information Center, May 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. Cambridge, MA: National Bureau of Economic Research, April 2004. http://dx.doi.org/10.3386/w10447.

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Stewart, Shannon C. Supplement Analysis for the Watershed Management Program EIS (DOE/EIS-0265/SA-159) - Pine Hollow Watershed Enhancement – Jackknife Watershed Projects. Office of Scientific and Technical Information (OSTI), July 2004. http://dx.doi.org/10.2172/827559.

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Tukey, John W. Kinds of Bootstraps and Kinds of Jackknives, Discussed in Terms of a Year of Weather-Related Data. Fort Belvoir, VA: Defense Technical Information Center, April 1987. http://dx.doi.org/10.21236/ada184495.

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