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Journal articles on the topic 'Jackknife'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2024

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Favole, Ginevra, Benjamin R. Granett, Javier Silva Lafaurie, and Domenico Sapone. "Does jackknife scale really matter for accurate large-scale structure covariances?" Monthly Notices of the Royal Astronomical Society 505, no. 4 (June 17, 2021): 5833–45. http://dx.doi.org/10.1093/mnras/stab1720.

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ABSTRACT The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings impact the precision of the covariance estimate on the correlation function multipoles and the error on the inferred baryon acoustic scale. We compare the measurement with the MultiDark Patchy mock galaxy catalogues, and we also validate it against a set of lognormal mocks with the same survey geometry. We build several jackknife configurations that vary in size and number of resamplings. We introduce the Hartlap factor in the covariance estimate that depends on the number of jackknife resamplings. We also find that it is useful to apply the tapering scheme to estimate the precision matrix from a limited number of resamplings. The results from CMASS and mock catalogues show that the error estimate of the baryon acoustic scale does not depend on the jackknife scale. For the shift parameter α, we find an average error of 1.6 per cent, 2.2 per cent and 1.2 per cent, respectively, from CMASS, Patchy, and lognormal jackknife covariances. Despite these uncertainties fluctuate significantly due to some structural limitations of the jackknife method, our α estimates are in reasonable agreement with published pre-reconstruction analyses. Jackknife methods will provide valuable and complementary covariance estimates for future large-scale structure surveys.

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12

Wang, Jia-Qiao, Jun Li, Yi-Jia Shih, Liang-Min Huang, Xin-Ruo Wang, and Ta-Jen Chu. "Sustainability Perspective of Minjiang Estuary Coastal Fisheries Management—Estimation of Fish Richness." Water 15, no. 14 (July 21, 2023): 2648. http://dx.doi.org/10.3390/w15142648.

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Species richness is the most basic concept of diversity and is crucial to biodiversity conservation and sustainable fisheries. To understand the fish species richness of the Minjiang Estuary and its adjacent waters, eight documents and surveyed data were collected and compared from 1990–2021. To obtain suitable analysis data, the content of the data was compared and evaluated. Explore the suitability of data based on several criteria. Among them, the bottom trawling survey carried out in 2006–2007, and non-parametric estimation methods such as Chao 2, Jackknife 1, Jackknife 2 and Bootstrap were used to estimate the fish species richness. The results of this case show that a total of 153 species of fish were caught in the trawling survey in the fourth quarter, belonging to 14 orders, 57 families and 101 genera. The 2006–2007 cruise is more complete for studying species richness. The Estimable expectations of fish species richness are: 250 (Chao 2), 204 (Jackknief 1), 241 (Jackknief 2) and 174 (Bootstrap). The number of fish species was significantly higher in summer and autumn than winter and spring. To manage fishery resources and sustainability in the sea area of Fujian Province, biological information and stock assessment are required. This meaningful information, especially for endemic and economically important species such as can set a baseline. Once species change exceeds the baseline range, it provides decision-making basis for marine biodiversity conservation and fisheries management.

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13

Yue, Jack C., Murray K. Clayton, and Chi-Ruei Hung. "Comparing Nonparametric Estimators for the Number of Shared Species in Two Populations." Diversity 14, no. 4 (March 26, 2022): 243. http://dx.doi.org/10.3390/d14040243.

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It is often of interest to biologists to evaluate whether two populations are alike with respect to a similarity index; assessing the numbers of shared species is one way to do this. In this study, we propose two Turing-type estimators for the probability of discovering new shared species and two jackknife-type estimators for the number of shared species in two populations. We use computer simulation and empirical data analysis to evaluate the proposed approach. The jackknife-type estimators provide stable and reliable estimates, for both the probability of discovering new shared species and the number of shared species. We also compare the jackknife-type estimates with that of using sample coverage to estimate the number of shared species. The estimate of using sample coverage has better performance in the case of even populations, while the jackknife-type estimates have smaller bias in the case of unbalanced populations. When combined with a stopping rule based on the probability of observing new shared species, confidence intervals based on the proposed jackknife-type estimators can provide better coverage probability for the true number of shared species. The jackknife-type estimates can provide coverage probability close to 0.95 in all examples.

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14

Zitzmann, Steffen, Sebastian Weirich, and Martin Hecht. "Accurate Standard Errors in Multilevel Modeling with Heteroscedasticity: A Computationally More Efficient Jackknife Technique." Psych 5, no. 3 (July 21, 2023): 757–69. http://dx.doi.org/10.3390/psych5030049.

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In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster.

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15

Fitrianto, Anwar, and Punitha Linganathan. "Comparisons between Resampling Techniques in Linear Regression: A Simulation Study." CAUCHY: Jurnal Matematika Murni dan Aplikasi 7, no. 3 (October 11, 2022): 345–53. http://dx.doi.org/10.18860/ca.v7i3.14550.

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The classic methods used in estimating the parameters in linear regression need to fulfill some assumptions. If the assumptions are not fulfilled, the conclusion is questionable. Resampling is one of the ways to avoid such problems. The study aims to compare resampling techniques in linear regression. The original data used in the study is clean, without any influential observations, outliers and leverage points. The ordinary least square method was used as the primary method to estimate the parameters and then compared with resampling techniques. The variance, p-value, bias, and standard error are used as a scale to estimate the best method among random bootstrap, residual bootstrap and delete-one Jackknife. After all the analysis took place, it was found that random bootstrap did not perform well while residual and delete-one Jackknife works quite well. Random bootstrap, residual bootstrap, and Jackknife estimate better than ordinary least square. Is was found that residual bootstrap works well in estimating the parameter in the small sample. At the same time, it is suggested to use Jackknife when the sample size is big because Jackknife is more accessible to apply than residual bootstrap and Jackknife works well when the sample size is big.

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16

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 (October 1, 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 determinations of isotopic enrichment of hydrogen and oxygen over time that are needed to achieve a given degree of accuracy in estimating energy expenditure. An example with two human subjects illustrates the methodology of the jackknife.

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17

Young, G. A., J. Shao, and D. Tu. "The Jackknife and Bootstrap." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 3 (1996): 631. http://dx.doi.org/10.2307/2983351.

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18

Singh, Kesar, Jun Shao, and Dongsheng Tu. "The Jackknife and Bootstrap." Journal of the American Statistical Association 92, no. 439 (September 1997): 1214. http://dx.doi.org/10.2307/2965588.

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19

Tauxe, Lisa, and Yves Gallet. "A jackknife for magnetostratigraphy." Geophysical Research Letters 18, no. 9 (September 1991): 1783–86. http://dx.doi.org/10.1029/91gl01223.

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20

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

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21

Hesterberg, Tim. "The Jackknife and Bootstrap." Technometrics 39, no. 4 (November 1997): 429. http://dx.doi.org/10.1080/00401706.1997.10485170.

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22

Cheng, Y., and Y. Zhao. "Bayesian jackknife empirical likelihood." Biometrika 106, no. 4 (July 13, 2019): 981–88. http://dx.doi.org/10.1093/biomet/asz031.

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Summary Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. Lazar (2003) showed that in Bayesian inference, if one replaces the usual likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of $U$-statistics. We propose the so-called Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows that the small-sample performance of the proposed method is better than its frequentist counterpart. Analysis of a case-control study for pancreatic cancer is used to illustrate the new approach.

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23

Shanmugam, Ramalingam. "The Jackknife and Bootstrap." Journal of Quality Technology 28, no. 4 (October 1996): 484–85. http://dx.doi.org/10.1080/00224065.1996.11979709.

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24

Gamage, Ramadha D. Piyadi, Ying-Ju Chen, and Wei Ning. "MODIFIED JACKKNIFE EMPIRICAL LIKELIHOOD." Advances and Applications in Statistics 60, no. 2 (February 15, 2020): 201–15. http://dx.doi.org/10.17654/as060020201.

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25

Weber, N. C. "The jackknife and heteroskedasticity." Economics Letters 20, no. 2 (January 1986): 161–63. http://dx.doi.org/10.1016/0165-1765(86)90165-5.

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26

Angrist, J. D., G. W. Imbens, and A. B. Krueger. "Jackknife instrumental variables estimation." Journal of Applied Econometrics 14, no. 1 (January 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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27

Rohma, Novia Nur. "PERBANDINGAN PENDUGAAN METODE ORDINARY KRIGING DAN METODE ORDINARY KRIGING DENGAN TEKNIK JACKKNIFE." MAp (Mathematics and Applications) Journal 4, no. 2 (February 6, 2023): 101–11. http://dx.doi.org/10.15548/map.v4i2.4736.

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Pada analisis spasial jika terdapat data yang memiliki distribusi tidak normal, maka akan menghasilkan prediksi yang kurang baik. Untuk mengatasi hal tersebut digunakan metode ordinary kriging dengan teknik jackknife. Dalam metode ordinary kriging dan ordinary kriging dengan teknik jackknife perlu memperhitungkan semivariogram. Hujan merupakan suatu proses jatuhnya air yang berasal dari awan ke bumi. Hujan di ukur memalui curah hujan. Tujuan penelitian ini adalah untuk membandingkan dua metode yaitu ordinary kriging dan ordinary kriging dengan teknik jackknife pada data berdistribusi normal dan data beristribusi tidak normal, serta menentukan semivariogram terbaik. Data yang digunakan adalah data curah hujan bulanan di Malang Raya periode Januari 2016 s/d Desember 2016. Dari dataset bulanan curah hujan, data yang berdistribusi normal pada bulan Januari, Februari, Maret, April, Mei, Juni, Agustus, September, Oktober, November dan Desember 2016, sedangkan data yang berdistribusi tidak normal pada bulan Juli. Pada data berdistribusi normal lebih cocok dianalisis dengan menggunakan ordinary kriging karena nilai RMSE relatif kecil dibanding ordinary kriging dengan teknik jackknife. Pada data tidak normal RMSE ordinary kriging dengan teknik jackknife lebih kecil dibanding ordinary kriging.

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Guelfi, Marcelo, and Carlos López-Vazquez. "Comparing the Thiessen’s Method against simpler alternatives using Monte Carlo Simulation." Revista Cartográfica, no. 96 (April 30, 2018): 125–38. http://dx.doi.org/10.35424/rcarto.i96.191.

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Estimating the expected value of a function over geographic areas is problem with a long history. In the beginning of the XX-th century the most common method was just the arithmetic mean of the field measurements ignoring data location. In 1911, Thiessen introduced a new weighting procedure measuring influence through an area and thus indirectly considering closeness between them. In another context, Quenouville created in 1949 the jackknife method which is used to estimate the bias and the standard deviation. In 1979 Efron invented the bootstrap method which, among other things, is useful to estimate the expected value and the confidence interval (CI) from a population. Although the Thiessen’s method has been used for more than 100 years, we were unable to find systematic analysis comparing its efficiency against the simple mean, or even to more recent methods like jackknife or boostrap. In this work we compared four methods to estimate de expected value. Sample mean, Thiessen, the so called here jackknifed Thiessen and bootstrap. All of them are feasible for routine use in a network of fixed locations. The comparison was made using the Friedman’s Test after a Monte Carlo simulation. Two cases were taken for study: one analytic with three arbitrary functions and the other using experimental data from daily rain measured with a satellite. The results show that Thiessen’s method is the best estimator in almost all the cases with a 95% of confidence interval. Unlike the others, the last two considered methods supply a suitable CI, but the one obtained through jackknifed Thiessen was even more accurate, opening the door for future work.

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Putra G, Aditio, Muhammad Arif Tiro, and Muhammad Kasim Aidid. "Metode Boostrap dan Jackknife dalam Mengestimasi Parameter Regresi Linear Ganda (Kasus: Data Kemiskinan Kota Makassar Tahun 2017)." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 2 (July 12, 2019): 32. http://dx.doi.org/10.35580/variansiunm12895.

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Abstrak Metode kuadrat terkecil merupakan metode standar untuk mengestimasi nilai parameter model regresi linear. Metode tersebut dibangun berdasarkan asumsi error bersifat identik dan independen, serta berdistribusi normal. Apabila asumsi tidak terpenuhi maka metode ini tidak akurat. Alternatif untuk mengatasi hal tersebut adalah dengan menggunakan metode resampling. Adapun metode resampling yang digunakan dalam penelitian ini yaitu metode bootstrap dan Jackknife. Terlebih dahulu dilakukan estimasi nilai parameter regresi untuk analisis data kemiskinan Kota Makassar Tahun 2017. Data tersebut merupakan data sekunder diperoleh dari BAPPEDA Kota Makassar. Dari uji asumsi klasik diperoleh bahwa model tidak bersifat homoskedastis dan residual tidak berdistribusi normal sehingga model regresi yang diperoleh tidak dapat dipertanggungjawabkan. Metode bootstrap dan jackknife yang dikenalkan disini menggunakan program R untuk mencari nilai bias dan nilai standar errornya. Estimasi parameter model regresi linear berganda dari metode resampling bootstrap dengan B=200 dan B=500 serta metode resampling jackknife Terhapus-1 diperoleh model regresi. Hasil yang didapat dalam penelitian ini, metode jackknife merupakan metode yang efisien dibandingkan dengan metode bootstrap, hal ini didukung dengan kecilnya tingkat standar error dan nilai biasnya yang dihasilkan. Kata Kunci: Regrei, Resampling, Bootsrap, JaccknifeAbstract. The Ordinary least squares method is a standard method for estimating the parameter values of a linear regression model. The method is built based on error assumptions that are identical and independent, and are normally distributed. If the assumptions are not met, this method is not accurate. The alternative to overcome this is to use the resampling method. The resampling method used in this study is bootstrap and jackknife methods. First, estimation of regression parameter values for analysis of poverty data in Makassar City in 2017. The data is secondary data obtained from the BAPPEDA of Makassar City. From the classic assumption test, it is obtained that the model is not homosexedastic and residual is not normally distributed so that the regression model obtained cannot be accounted for. Bootstrap and jackknife methods are introduced here using the R program to find the value of the bias and the standard error values. Parameter estimation of multiple linear regression models from Bootstrap resampling method with B= 200, B= 500 and jackknife deleted-1 resampling method obtained regression models. The results obtained in this study, Jackknife method is an efficient method compared with the bootstrap method, and this is supported by the small standard level error and bias in resulting value.Keywords: regression, resampling, bootstrap, jackknife.

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30

Robitzsch, Alexander. "Analytical Approximation of the Jackknife Linking Error in Item Response Models Utilizing a Taylor Expansion of the Log-Likelihood Function." AppliedMath 3, no. 1 (January 5, 2023): 49–59. http://dx.doi.org/10.3390/appliedmath3010004.

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Linking errors in item response models quantify the dependence on the chosen items in means, standard deviations, or other distribution parameters. The jackknife approach is frequently employed in the computation of the linking error. However, this jackknife linking error could be computationally tedious if many items were involved. In this article, we provide an analytical approximation of the jackknife linking error. The newly proposed approach turns out to be computationally much less demanding. Moreover, the new linking error approach performed satisfactorily for datasets with at least 20 items.

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Saied Ismaeel, Shelan, Habshah Midi, and Kurdistan M. Taher Omar. "A Remedial Measure of Multicollinearity in Multiple Linear Regression in the Presence of High Leverage Points." Sains Malaysiana 53, no. 4 (April 30, 2024): 907–20. http://dx.doi.org/10.17576/jsm-2024-5304-14.

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The ordinary least squares (OLS) is the widely used method in multiple linear regression model due to tradition and its optimal properties. Nonetheless, in the presence of multicollinearity, the OLS method is inefficient because the standard errors of its estimates become inflated. Many methods have been proposed to remedy this problem that include the Jackknife Ridge Regression (JAK). However, the performance of JAK is poor when multicollinearity and high leverage points (HLPs) which are outlying observations in the X- direction are present in the data. As a solution to this problem, Robust Jackknife Ridge MM (RJMM) and Robust Jackknife Ridge GM2 (RJGM2) estimators are put forward. Nevertheless, they are still not very efficient because they suffer from long computational running time, some elements of biased and do not have bounded influence property. This paper proposes a robust Jackknife ridge regression that integrates a generalized M estimator and fast improvised Gt (GM-FIMGT) estimator, in its establishment. We name this method the robust Jackknife ridge regression based on GM-FIMGT, denoted as RJFIMGT. The numerical results show that the proposed RJFIMGT method was found to be the best method as it has the least values of RMSE and bias compared to other methods in this study.

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32

Guelfi, Marcelo, and Carlos López-Vázquez. "Comparación del método de Thiessen con alternativas más simples mediante simulación de Monte Carlo." Revista Cartográfica, no. 91 (September 29, 2019): 143–57. http://dx.doi.org/10.35424/rcarto.i91.456.

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La estimación del valor esperado de una función sobre áreas geográficas es un problema que data de tiempo atrás. Hasta principios del siglo XX el método más común solía ser calcular la media aritmética de las medidas obtenidas en el campo, ignorando su posición geométrica. En 1911, Thiessen introdujo una nueva forma de cálculo que asignaba a cada punto de medición un peso relativo al área de influencia, que tenía en cuenta indirectamente la proximidad entre datos. En 1949, Quenouville crea, en otro contexto, el método de jackknife que se utiliza para estimar el sesgo y la desviación estándar. En 1979, Efron inventa el método de bootstrap que, entre otras cosas, es apropiado para estimar el valor esperado de una población así como su intervalo de confianza (IC). Si bien el método de Thiessen lleva usándose hace más de un siglo, no se han encontrado estudios sistemáticos que comparen su eficacia frente al método anterior ni frente a variantes posteriores como jackknife o bootstrap. Este trabajo consiste en comparar cuatro métodos para la estimación del valor esperado: el de la media aritmética, el de Thiessen, el aquí denominado jackknifed Thiessen y el de bootstrap. Todos ellos son aptos para aplicaciones repetitivas en una red de observación fija. La comparación se realizó mediante el Test de Friedman tras una simulación de Monte Carlo. Para los datos se consideran dos casos: uno analítico mediante el estudio de tres funciones arbitrarias, y otro experimental con datos de lluvia diaria medidos por satélite. Los resultados obtenidos muestran que el método Thiessen es el mejor estimador en prácticamente todos los casos con el 95% de nivel de confianza. Las últimas dos variantes tienen la virtud de suministrar un IC que se mostró adecuado, aunque jackknifed Thiessen resultó mucho más ajustado, abriendo así la puerta para

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Schreuder, H. T., H. G. Li, and C. T. Scott. "Jackknife and Bootstrap Estimation for Sampling with Partial Replacement." Forest Science 33, no. 3 (September 1, 1987): 676–89. http://dx.doi.org/10.1093/forestscience/33.3.676.

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Abstract Jackknife and bootstrap estimators and variance estimators were compared with a classical estimator and variance estimator for sampling with partial replacement (SPR) on two occasions. One hundred twenty plots were sampled at time 1. At time 2, 10, 20, or 30 plots were remeasured, and a new sample size of size 20 was also selected. The samples were drawn from three large samples of forest plots from the northeastern United States, which were treated as populations. Although variables are correlated on the two occasions (r = 0.648 - 0.891), the assumptions of linearity and homogeneity of variance are questionable. The classical estimator is generally preferable to the jackknife and bootstrap estimators when both estimation bias and efficiency are important in SPR sampling. The jackknife variance estimator is generally preferable if variance estimation bias and confidence limit coverage rates are taken into consideration, particularly for skewed populations with small sample sizes. Generally, these jackknife variance estimates are less stable than the classical variance estimates. For. Sci. 33(3):676-689.

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Buzas, J. S. "Fast Estimators of the Jackknife." American Statistician 51, no. 3 (August 1997): 235. http://dx.doi.org/10.2307/2684894.

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Tu, D., and A. J. Gross. "Bias reduction for jackknife skewness." Communications in Statistics - Theory and Methods 23, no. 8 (January 1994): 2323–41. http://dx.doi.org/10.1080/03610929408831389.

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Resnick, Barbara. "The Rough and Ready Jackknife." Nursing Research 45, no. 3 (May 1996): 185–88. http://dx.doi.org/10.1097/00006199-199605000-00011.

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BARRY, D. A. "AN APPROXIMATELY MINIMUM VARIANCE JACKKNIFE." Engineering Optimization 17, no. 4 (June 1991): 321–32. http://dx.doi.org/10.1080/03052159108941079.

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Buzas, J. S. "Fast Estimators of the Jackknife." American Statistician 51, no. 3 (August 1997): 235–40. http://dx.doi.org/10.1080/00031305.1997.10473969.

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Peng, Liang, and Jingping Yang. "Jackknife method for intermediate quantiles." Journal of Statistical Planning and Inference 139, no. 7 (July 2009): 2373–81. http://dx.doi.org/10.1016/j.jspi.2008.10.022.

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Berg, Bernd A. "Double jackknife bias-corrected estimators." Computer Physics Communications 69, no. 1 (February 1992): 7–14. http://dx.doi.org/10.1016/0010-4655(92)90124-h.

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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 (September 2003): 555–61. http://dx.doi.org/10.1007/bf02517807.

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González-Oreja, J. A., A. A. de la Fuente-Díaz-Ordaz, L. Hernández-Santín, D. Buzo-Franco, and C. Bonache-Regidor. "Evaluación de estimadores no paramétricos de la riqueza de especies. Un ejemplo con aves en áreas verdes de la ciudad de Puebla, México." Animal Biodiversity and Conservationa 33, no. 1 (2010): 31–45. http://dx.doi.org/10.32800/abc.2010.33.0031.

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Assessing non-parametric estimators of species richness. A case study with birds in green areas of the city of Puebla, Mexico Our objective was to evaluate the performance of non-parametric estimators of spe-cies richness with real data. During the 2003 breeding season, bird communities were sampled in two green areas in the city of Puebla (Mexico), and the corresponding sample-based rarefaction curves were obtained. Mean data were adjusted to two non-asymptotic and seven asymptotic accumulation functions, and the best model was selected by means of reliability criteria in information theory. The cumulative Weibull and the beta-P functions were the best-fit models. Bias, precision and accuracy of five non-parametric estimators of species richness (ICE, Chao2, Jackknife 1, Jackknife 2, and Bootstrap) were then assessed for increasing sampling efforts (1-53 sampling units) against the asymptote of the selected accumulation functions. All the non-parametric estimators here evaluated underestimated true richness most of the time, specially in one of the sites. However, after combining data from the two assemblages, only ICE, and Jackknife 1 and 2 exhibited bias below 10% with different sampling efforts, and only Jackknife 1 was globally accurate (scaled mean squared error x 100 < 5%, even with low sampling efforts, ca. 20% of the total). Therefore, we propose using the Jackknife 1 non-parametric estimator as a lower limit to measure bird species richness in urban sites similar to those in the present study.

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Zhou, Shu Wen, Si Qi Zhang, and Guang Yao Zhao. "Jackknife Control on Tractor Semi-Trailer during Emergency Braking." Advanced Materials Research 299-300 (July 2011): 1303–6. http://dx.doi.org/10.4028/www.scientific.net/amr.299-300.1303.

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Emergency braking on a low coefficient of friction or split-mu road surface, the semi-trailer may push the tractor from behind until it spins round and faces backwards, and a jackknife accident occurs. In this paper, the tractor semi-trailer kinematics was analyzed and a 3-dof of tractor semi-trailer model was used to design a state observer to estimate the articulation angle. To avoid a jackknife, the four-channel ABS which can produce maximum braking force will be switch to three-channel ABS according the estimated articulation angle. The virtual prototyping simulation results show that the jackknife control system can improve the tractor semi-trailer lateral stability under emergency braking and shorten the stop distance dramatically on split-mu road surface at high speed.

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Leng, Zhe, Yue Wang, Ming Xin, and Mark A. Minor. "The Effect of Sideslip on Jackknife Limits during Low Speed Trailer Operation." Robotics 11, no. 6 (November 22, 2022): 133. http://dx.doi.org/10.3390/robotics11060133.

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Jackknifing refers to the serious situation where a vehicle-trailer system enters a jackknife state and the vehicle and trailer eventually collide if trailer operation is not corrected. This paper considers low speed trailer maneuvering typical of trailer backing. Jackknife state limits can vary due to sideslip caused by physical interaction between the vehicle, trailer, and environment. Analysis of a kinematic model considers sideslip at the vehicle and trailer wheels. Results indicate that vehicle-trailer systems should be divided into three categories based on the ratio of hitch length and trailer tongue length, each with distinct behaviors. The Long Trailer category may have no jackknifing state while the other two categories always have states leading to jackknifing. It is found that jackknife limits, which are the boundaries between the jackknifing state and the recoverable regions, can be divided into safe and unsafe limits. The latter of which must be avoided. Simulations and physical experiments support these results and provide insight about the implications of vehicle and trailer states with slip that lead to jackknifing. Simulations also demonstrate the benefit of considering these new slip-based jackknife limits in trailer backing control.

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Li, Zhigang, Zhejie Ding, Yu Yu, and Pengjie Zhang. "The Kullback–Leibler Divergence and the Convergence Rate of Fast Covariance Matrix Estimators in Galaxy Clustering Analysis." Astrophysical Journal 965, no. 2 (April 1, 2024): 125. http://dx.doi.org/10.3847/1538-4357/ad3215.

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Abstract We present a method to quantify the convergence rate of the fast estimators of the covariance matrices in the large-scale structure analysis. Our method is based on the Kullback–Leibler (KL) divergence, which describes the relative entropy of two probability distributions. As a case study, we analyze the delete-d jackknife estimator for the covariance matrix of the galaxy correlation function. We introduce the information factor or the normalized KL divergence with the help of a set of baseline covariance matrices to diagnose the information contained in the jackknife covariance matrix. Using a set of quick particle mesh mock catalogs designed for the Baryon Oscillation Spectroscopic Survey DR11 CMASS galaxy survey, we find that the jackknife resampling method succeeds in recovering the covariance matrix with 10 times fewer simulation mocks than that of the baseline method at small scales (s ≤ 40 h −1 Mpc). However, the ability to reduce the number of mock catalogs is degraded at larger scales due to the increasing bias on the jackknife covariance matrix. Note that the analysis in this paper can be applied to any fast estimator of the covariance matrix for galaxy clustering measurements.

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حسين, سجى, and حنين يوسف. "مقارنة الطريقة المقترحة (AUGJRR) مع الطرائق المتحيزة لتقديرانحدار الحرف العامة بوجود التعدد الخطي." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 12, 2021): 69–78. http://dx.doi.org/10.55562/jrucs.v37i1.235.

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ان تقدير معلمات الااانموذج الخطي العام الذي يعاني من خرق في احدى فروضه وهو تعدد العلاقة الخطية (Multicollinearity) بين المتغيرات التوضيحية شبه التام يكون باستعمال طرائق تقدير انحدار الحرف العام والذي سيتركز عليه اهتمامنا في هذا البحث مثل:•Generalized Ridge Regression Estimator (GRRE،(•Modified Jackknife Ridge Regression (MJRRE(.•Generalized Jackknife Ridge Regression (GJRRE).•Generalized Liu Estimator (GLE).•Almost unbiased Generalized Liu (AUGLE(.•Generalized Ridge Regression Almost unbiased (AUGRRE).بالاضافة الى الطريقة المقترحة:•Almost unbiased Generalized Jackknife Ridge (AUGJRRE)حيث تم في هذا البحث اشتقاق طريقة (AUGJRR) لتقدير معلمات الااانموذج الذي يعاني من مشكلة التعدد الخطي وتمت مقارنة الطريقة المقترحة مع الطرائق المذكورة اعلاه بالاضافة الى طريقة(OLS). وكانت النتيجة بإن أفضل المقدرات هما المقدر (AUGLE) والمقدر المقترح (AUGJRRE)والمقدر (AUGRRE) حيث يمتلكون اقل متوسط مربعات خطأ (MSE) مقارنة مع مقدر المربعات الصغرى وبقية المقدرات المتحيزة الاخرى.

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

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Huitema, Bradley E., and Joseph W. Mckean. "Reduced Bias Autocorrelation Estimation:Three Jackknife Methods." Educational and Psychological Measurement 54, no. 3 (September 1994): 654–65. http://dx.doi.org/10.1177/0013164494054003008.

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Martin, Michael A., and Steven Roberts. "Jackknife-after-bootstrap regression influence diagnostics." Journal of Nonparametric Statistics 22, no. 2 (February 2010): 257–69. http://dx.doi.org/10.1080/10485250903287906.

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Shao, Jun. "Consistency of jackknife variance estimators jun." Statistics 22, no. 1 (January 1991): 49–57. http://dx.doi.org/10.1080/02331889108802282.

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