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Journal articles on the topic 'Median-Of-Mean'

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Author: Grafiati

Published: 1 June 2024

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1

Griffin, Lewis D. "Mean, median and mode filtering of images." Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 456, no. 2004 (December 8, 2000): 2995–3004. http://dx.doi.org/10.1098/rspa.2000.0650.

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2

Hoseana, Jonathan, and Franco Vivaldi. "Geometrical properties of the mean-median map." Journal of Computational Dynamics 7, no. 1 (2020): 83–121. http://dx.doi.org/10.3934/jcd.2020004.

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3

Bakker, Arthur, and Koeno P. E. Gravemeijer. "An Historical Phenomenology of Mean and Median." Educational Studies in Mathematics 62, no. 2 (June 2006): 149–68. http://dx.doi.org/10.1007/s10649-006-7099-8.

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4

Kaur, Amanpreet, and Ravneet Kaur Sidhu. "Edge Preservation of Enhanced Fuzzy Median Mean Filter Using Decision Based Median Filter." International Journal on Soft Computing 7, no. 1 (February 29, 2016): 17–26. http://dx.doi.org/10.5121/ijsc.2016.7102.

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5

Teng, Guangqiang, Yanpeng Li, Boping Tian, and Jie Li. "Sharper Concentration Inequalities for Median-of-Mean Processes." Mathematics 11, no. 17 (August 30, 2023): 3730. http://dx.doi.org/10.3390/math11173730.

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The Median-of-Mean (MoM) estimation is an efficient statistical method for handling data with contamination. In this paper, we propose a variance-dependent MoM estimation method using the tail probability of a binomial distribution. The bound of this method is better than the classical Hoeffding method under mild conditions. This method is then used to study the concentration of variance-dependent MoM empirical processes and sub-Gaussian intrinsic moment norm. Finally, we give the bound of the variance-dependent MoM estimator with distribution-free contaminated data.

6

Yusuf, Ajibola Yahya, Ahmed Audu, and Mojeed Abiodun Yunusa. "POWER MEDIAN-BASED ESTIMATORS OF FINITE POPULATION MEAN." FUDMA JOURNAL OF SCIENCES 8, no. 2 (April 30, 2024): 296–300. http://dx.doi.org/10.33003/fjs-2024-0802-2291.

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In this paper, median based mean estimators for estimating finite population mean are proposed. The proposed estimators were obtained by transforming estimators in literature utilizing mean of auxiliary variable into median based estimators with the aim of obtaining estimators with higher efficiency. The mean square error of the proposed estimators was obtained up to the first order of approximation using Taylor series approach and the optimum values of the unknown of the estimators were obtained by means of partial derivative of the mean square error and equating to zero. A Numerical study was carried out to support the fact that the proposed estimators are more efficient as compared to the existing ones, as the proposed estimators have the least mean squared error at optimum values of the unknown constants and have higher percentage relative efficiency (PRE). This implies that the proposed estimators are more efficient than the traditional ones considered in the study.

7

Sarkar, Jyotirmoy, and Mamunur Rashid. "Visualizing Mean, Median, Mean Deviation, and Standard Deviation of a Set of Numbers." American Statistician 70, no. 3 (July 2, 2016): 304–12. http://dx.doi.org/10.1080/00031305.2016.1165734.

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8

Smith, Woollcott, and Milton Parnes. "Mean Streets: The Median of a Size-Biased Sample and the Population Mean." American Statistician 48, no. 2 (May 1994): 106. http://dx.doi.org/10.2307/2684257.

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9

Smith, Woollcott, and Milton Parnes. "Mean Streets: The Median of a Size-Biased Sample and the Population Mean." American Statistician 48, no. 2 (May 1994): 106–10. http://dx.doi.org/10.1080/00031305.1994.10476034.

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10

Basu, S., and A. DasGupta. "The Mean, Median, and Mode of Unimodal Distributions:A Characterization." Theory of Probability & Its Applications 41, no. 2 (January 1997): 210–23. http://dx.doi.org/10.1137/s0040585x97975447.

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11

Jacobbe, Tim. "ELEMENTARY SCHOOL TEACHERS’ UNDERSTANDING OF THE MEAN AND MEDIAN." International Journal of Science and Mathematics Education 10, no. 5 (October 28, 2011): 1143–61. http://dx.doi.org/10.1007/s10763-011-9321-0.

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12

Abdullahi, Umar K., Fidelis I. Ugwuowo, and Nuanpan Lawson. "Power ratio cum median-based ratio estimator of finite population mean with known population median." Statistics in Transition new series 24, no. 5 (December 7, 2023): 35–44. http://dx.doi.org/10.59170/stattrans-2023-062.

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The search for an efficient estimator of the finite population mean has been a critical problem to the sample survey research community. This study is motivated by the fact that the conducted literature review showed that no research has developed such an average ratio estimator of the population mean that would utilize both the population and the sample medians of study variable, as well as the Srivastava (1967) estimator at a time. In this paper we proposed the power ratio cum median-based ratio estimator of the finite population mean, which is a function of two ratio estimators in the form of an average. The estimator assumes the population to be homogeneous and skewed. The properties (i.e. the Bias and the Mean Squared Error – MSE) of the proposed estimator were derived alongside its asymptotically optimum MSE. We demonstrated the efficiency of the proposed estimator jointly with its efficiency conditions by comparing it to selected estimators described in the literature. Empirically, a real-life dataset from the literature and a simulation study from two skewed distributions (Gamma and Weibull) were used to examine the efficiency gain. The empirical analysis and simulation study demonstrated that the efficiency gain is significant. Hence, the practical application of the proposed estimator is recommended, especially in socio-economic surveys.

13

Ben Salah, Hanene, Mohamed Chaouch, Ali Gannoun, Christian de Peretti, and Abdelwahed Trabelsi. "Mean and median-based nonparametric estimation of returns in mean-downside risk portfolio frontier." Annals of Operations Research 262, no. 2 (June 23, 2016): 653–81. http://dx.doi.org/10.1007/s10479-016-2235-z.

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14

Fisher, D. S., J. C. Burns, and K. R. Pond. "Estimation of Mean and Median Particle Size of Ruminant Digesta." Journal of Dairy Science 71, no. 2 (February 1988): 518–24. http://dx.doi.org/10.3168/jds.s0022-0302(88)79583-1.

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15

Subramani, J., and G. Kumarapandiyan. "Estimation of Population Mean Using Known Correlation Coefficient And Median." Journal of Statistical Theory and Applications 13, no. 4 (2014): 333. http://dx.doi.org/10.2991/jsta.2014.13.4.6.

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16

Басу, С., S. Basu, Anirban Dasgupta, and Anirban Dasgupta. "The mean, median, and mode of unimodal distributions: a characterization." Teoriya Veroyatnostei i ee Primeneniya 41, no. 2 (1996): 336–52. http://dx.doi.org/10.4213/tvp2942.

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17

O'Cinneide, Colm Art. "The Mean Is within One Standard Deviation of Any Median." American Statistician 44, no. 4 (November 1990): 292. http://dx.doi.org/10.2307/2684351.

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18

Gordienko, Evgueni, Andrey Novikov, and J. Ruiz de Chávez. "Note on Qualitative Robustness of Multivariate Sample Mean and Median." Journal of Probability and Statistics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/208950.

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It is known that the robustness properties of estimators depend on the choice of a metric in the space of distributions. We introduce a version of Hampel's qualitative robustness that takes into account then-asymptotic normality of estimators inRk, and examine such robustness of two standard location estimators inℝk. For this purpose, we use certain combination of the Kantorovich and Zolotarev metrics rather than the usual Prokhorov type metric. This choice of the metric is explained by an intention to expose a (theoretical) situation where the robustness properties of sample mean andL1-sample median are in reverse to the usual ones. Using the mentioned probability metrics we show the qualitative robustness of the sample multivariate mean and prove the inequality which provides a quantitative measure of robustness. On the other hand, we show thatL1-sample median could not be “qualitatively robust” with respect to the same distance between the distributions.

19

O'cinneide, Colm Art. "The Mean is within One Standard Deviation of Any Median." American Statistician 44, no. 4 (November 1990): 292–93. http://dx.doi.org/10.1080/00031305.1990.10475743.

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20

Groth, Randall E. "Characteristics of teachers' conversations about teaching mean, median, and mode." Teaching and Teacher Education 25, no. 5 (July 2009): 707–16. http://dx.doi.org/10.1016/j.tate.2008.11.005.

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21

Feng, Renjie, and Steve Zelditch. "Median and mean of the supremum ofL2normalized random holomorphic fields." Journal of Functional Analysis 266, no. 8 (April 2014): 5085–107. http://dx.doi.org/10.1016/j.jfa.2014.02.012.

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22

Dokshitzer, Yu L., V. A. Khoze, C. P. Fong, and B. R. Webber. "Mean, median and mode of hadron spectrum in QCD jets." Physics Letters B 273, no. 3 (December 1991): 319–22. http://dx.doi.org/10.1016/0370-2693(91)91691-n.

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23

Claudia, Marin, Domenico Leogrande, and Fabio Manca. "The Mean Deviation from the Median of the Dagum Distribution." Applied Mathematics 11, no. 10 (2020): 951–56. http://dx.doi.org/10.4236/am.2020.1110062.

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24

Olasunkanmi Muili, Jamiu, and A. Audu. "Estimation of Finite Population Mean of Median Based Using Power Transformation." Oriental Journal of Physical Sciences 6, no. 1-2 (February 28, 2022): 26–31. http://dx.doi.org/10.13005/ojps06.01-02.05.

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Abstract:

This paper deals with the assessment of finite population mean. An estimator is suggested for estimation of finite population mean of study variable. The purpose of this study is to evolve a ratio-type estimator to enhance the proficiency of the existing estimators considered in the study in sample random sampling without replacement using information of auxiliary variable. Expressions of the bias and mean square error (MSE) of the proposed estimator was derived by Taylor series method. The efficiency conditions under which the proposed ratio-type estimator is better than sample mean, ratio estimator, and other estimators considered in this study have been established. Theoretical and empirical findings are incentive and brace the robustness of the proposed estimator for mean estimation. The empirical results shown that the suggested estimator is more efficient than the sample mean, ratio estimator and other estimators.

25

Hoseana, Jonathan, and Franco Vivaldi. "On the unboundedness of the transit time of mean-median orbits." Journal of Difference Equations and Applications 26, no. 9-10 (October 2, 2020): 1398–421. http://dx.doi.org/10.1080/10236198.2020.1842385.

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26

Lamichhane, R., S. Singh, and N. Diawara. "Improved estimation of population mean using known median of auxiliary variable." Communications in Statistics - Simulation and Computation 46, no. 4 (December 18, 2016): 2821–28. http://dx.doi.org/10.1080/03610918.2015.1062102.

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27

Eyo, Effiong Eyo, and E. I. Enang. "Calibration Ratio Estimators of Population Mean Using Median of Auxiliary Variable." Journal of Modeling and Simulation of Materials 5, no. 1 (December 29, 2022): 21–30. http://dx.doi.org/10.21467/jmsm.5.1.21-30.

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In this study we propose a calibration ratio estimator and a calibration separate ratio-product estimator of population mean of study variable under stratified sampling using the median of auxiliary variable. The calibration estimator used calibrated weight determined to minimize a chi-square distance measure subject to a set of constraint related to the auxiliary variable in other to increase precision of the estimators. The median of the auxiliary variable was used in defining the calibration constraints. The variances of the proposed estimators were also obtained. An empirical study to ascertain the performance of these estimators using simulated data under underlying distribution assumption of Student-T distribution, Cauchy distribution, Lognormal distribution, and Standard normal distribution with varying sample sizes of 10%, 20%, and 25% were carried out. The result of simulation reveals that when the underlying distribution is Student-T, at 10% sample size, the efficiency performance of the proposed calibration separate ratio-product estimator is better than other competing estimators. As the sample size is increased to 20% and 25%, the efficiency performance of the existing stratified ratio estimator and existing calibration ratio estimator respectively become better than the other estimators. Under the skewed distributions (Cauchy and Lognormal) and the standard normal distribution, it is observed that the proposed calibration ratio estimator is better than other competing estimators in terms of efficiency, consistency and reliability. The result also reveals that under the lognormal distribution, the conventional stratified ratio estimator and the conventional calibration ratio estimator give the same result.

28

Sharma, Dinesh K., Dharmendra Yadav, and S. K. Yadav. "A NEW GENERALIZED MEDIAN BASED ESTIMATOR OF THE FINITE POPULATION MEAN." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10046753.

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29

Yadav, S. K., Dinesh K. Sharma, and Dharmendra K. Yadav. "A new generalised median-based estimator of the finite population mean." International Journal of Operational Research 43, no. 4 (2022): 498. http://dx.doi.org/10.1504/ijor.2022.10047301.

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30

Yadav, Dharmendra K., Dinesh K. Sharma, and S. K. Yadav. "A new generalised median-based estimator of the finite population mean." International Journal of Operational Research 43, no. 4 (2022): 498. http://dx.doi.org/10.1504/ijor.2022.122863.

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31

Davies, E. R. "The Relative Effects of Median and Mean Filters on Noisy Signals." Journal of Modern Optics 39, no. 1 (January 1992): 103–13. http://dx.doi.org/10.1080/713823346.

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32

Zieliński, R. "Robustness of sample mean and sample median under restrictions on outliers." Applicationes Mathematicae 19, no. 2 (1987): 239–40. http://dx.doi.org/10.4064/am-19-2-239-240.

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33

Coifman, Benjamin, Sudha Dhoorjaty, and Zu-Hsu Lee. "Estimating median velocity instead of mean velocity at single loop detectors." Transportation Research Part C: Emerging Technologies 11, no. 3-4 (June 2003): 211–22. http://dx.doi.org/10.1016/s0968-090x(03)00025-1.

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34

Khan, Zahid, Muhammad Ismail, and Hani Samawi. "Mixture ranked set sampling for estimation of population mean and median." Journal of Statistical Computation and Simulation 90, no. 4 (November 18, 2019): 573–85. http://dx.doi.org/10.1080/00949655.2019.1691553.

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35

Das, Ananya, Timothy A. Lin, Christine Lin, Tomer Meirson, Zachary R. McCaw, Lu Tian, and Ethan B. Ludmir. "Assessment of Median and Mean Survival Time in Cancer Clinical Trials." JAMA Network Open 6, no. 4 (April 3, 2023): e236498. http://dx.doi.org/10.1001/jamanetworkopen.2023.6498.

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36

Joice, G. Baby. "A Comparative of Analysis of Image Restoration Using Mean and Median Filtering." International Journal for Research in Applied Science and Engineering Technology V, no. II (February 28, 2017): 193–97. http://dx.doi.org/10.22214/ijraset.2017.2031.

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37

Subramani, J., and G. Kumarapandiyan. "Estimation of Population Mean Using Known Median and Co-Efficent of Skewness." American Journal of Mathematics and Statistics 2, no. 5 (December 1, 2012): 101–7. http://dx.doi.org/10.5923/j.ajms.20120205.01.

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38

García-Pérez, Alfonso. "A New Estimator: Median of the Distribution of the Mean in Robustness." Mathematics 11, no. 12 (June 14, 2023): 2694. http://dx.doi.org/10.3390/math11122694.

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In some statistical methods, the statistical information is provided in terms of the values used by classical estimators, such as the sample mean and sample variance. These estimations are used in a second stage, usually in a classical manner, to be combined into a single value, as a weighted mean. Moreover, in many applied studies, the results are given in these terms, i.e., as summary data. In all of these cases, the individual observations are unknown; therefore, computing the usual robustness estimators with them to replace classical non-robust estimations by robust ones is not possible. In this paper, the use of the median of the distribution Fx¯ of the sample mean is proposed, assuming a location-scale contaminated normal model, where the parameters of Fx¯ are estimated with the classical estimations provided in the first stage. The estimator so defined is called median of the distribution of the mean, MdM. This new estimator is applied in Mendelian randomization, defining the new robust inverse weighted estimator, RIVW.

39

Subramani, J. "A new median based ratio estimator for estimation of the finite population mean." Statistics in Transition new series 17, no. 4 (December 1, 2016): 591–604. http://dx.doi.org/10.59170/stattrans-2016-033.

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The present paper deals with a new median based ratio estimator for the estimation of finite population means in the absence of an auxiliary variable. The bias and mean squared error of the proposed median based ratio estimator are obtained. The performance of the median based ratio estimator is compared with that of the SRSWOR sample mean, ratio estimator and linear regression estimator for certain natural population. It is shown from the numerical comparisons that the proposed median based ratio estimator outperforms the SRSWOR sample mean, ratio estimator and also the linear regression estimator.

40

Yunusa, Mojeed Abiodun, Jamiu Olasunkanmi Muili, Ahmed Audu, and Ran Vijay Kumar Singh. "A Sine Type Median Based Estimator for the Estimation of Population Mean." Oriental Journal of Physical Sciences 8, no. 1 (July 15, 2023): 21–26. http://dx.doi.org/10.13005/ojps08.01.05.

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In the literature, there are numerous estimators for estimating population means when auxiliary information is provided. Subramani suggested ratio median based estimator when the median of the study variable is available and the regression estimator was shown to be significantly less efficient than the estimator. In this research, we suggested an estimator for the population mean of the studied variable based on a sine type median. Using Taylor series expansion, the bias and mean square error of the estimator were obtained up to the first order of approximation. The condition under which the proposed estimator is more efficient than the existing estimators was established. An empirical investigation was done to compare the suggested estimator's efficiency to that of the existing estimators, and the numerical findings showed that the proposed estimator is more efficient.

41

Filho, Alberto Cargnelutti, Cleiton Antonio Wartha, Jéssica Andiara Kleinpaul, Ismael Mario Marcio Neu, and Daniela Lixinski Silveira. "Sample Size to Estimate the Mean and Median of Traits in Canola." Journal of Agricultural Science 10, no. 11 (October 15, 2018): 123. http://dx.doi.org/10.5539/jas.v10n11p123.

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The aim of this study was to determine the sample size (i.e., number of plants) required to estimate the mean and median of canola (Brassica napus L.) traits of the Hyola 61, Hyola 76, and Hyola 433 hybrids with precision levels. At 124 days after sowing, 225 plants of each hybrid were randomly collected. In each plant, morphological (plant height) and productive traits (number of siliques, fresh matter of siliques, fresh matter of aerial part without siliques, fresh matter of aerial part, dry matter of siliques, dry matter of aerial part without siliques, and dry matter of aerial part) were measured. For each trait, measures of central tendency, variability, skewness, and kurtosis were calculated. Sample size was determined by resampling with replacement of 10,000 resamples. The sample size required for the estimation of measures of central tendency (mean and median) varies between traits and hybrids. Productive traits required larger sample sizes in relation to the morphological traits. Larger sample sizes are required for the hybrids Hyola 433, Hyola 61, and Hyola 76, in this sequence. In order to estimate the mean of canola traits of the Hyola 61, Hyola 76 e Hyola 433 hybrids with the amplitude of the confidence interval of 95% equal to 30% of the estimated mean, 208 plants are required. Whereas 661 plants are necessary to estimate the median with the same precision.

42

Cargnelutti Filho, A., B. M. Alves, G. O. Santos, C. A. Wartha, J. A. Kleinpaul, and D. L. Silveira. "Sample size to estimate the mean and median of traits in flax." Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences 13, no. 1 (March 28, 2018): 1–7. http://dx.doi.org/10.5039/agraria.v13i1a5492.

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43

Niwitpong, Sa-aat, and Rada Somkhuean. "Confidence Intervals for the Median of Lognormal Distribution with Restricted Parameter Mean." International Journal of Mathematics Trends and Technology 29, no. 1 (January 25, 2016): 21–27. http://dx.doi.org/10.14445/22315373/ijmtt-v29p504.

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44

Groth, Randall E., and Jennifer A. Bergner. "Preservice Elementary Teachers' Conceptual and Procedural Knowledge of Mean, Median, and Mode." Mathematical Thinking and Learning 8, no. 1 (January 2006): 37–63. http://dx.doi.org/10.1207/s15327833mtl0801_3.

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45

Ahmad, Rizwan, Deepti S. Vikram, Lee C. Potter, and Periannan Kuppusamy. "Estimation of mean and median pO2 values for a composite EPR spectrum." Journal of Magnetic Resonance 192, no. 2 (June 2008): 269–74. http://dx.doi.org/10.1016/j.jmr.2008.03.002.

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46

Sun, Manman. "Comparison of processing results of median filter and mean filter on Gaussian noise." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 779–85. http://dx.doi.org/10.54254/2755-2721/5/20230702.

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In people's daily lives, if the image is polluted with noise, it will become very blurred, so it is pretty necessary to use filtering to denoise the image and get a clearer image to assist people in work and study. The purpose of this article is by comparing the denoising result of the Median filter and Mean filter on Gaussian noise, and the filtering method which is more suitable for reducing Gaussian noise is found. Matlab is a very accurate and reliable scientific calculation standard software tool, which is very common in people's lives. The Median filter is a kind of nonlinear filter. It is so virtual at decreasing impulse noise. The common basic theory of the Median filter is to supersede the gray value of pixels with the median of the gray value in a neighborhood of the pixels, and not use the average proportion. The Mean filter is a sort of plain sliding-window space filter that takes the place of the central value with the mean of all the pixel values in the plain window. This essay takes advantage of Matlab to complete the process of adding Gaussian noise in photos, reducing Gaussian noise, and calculating the PSNR. The processing result of two types of noise reducers on Gaussian noise is compared. And by observing the image clarity, PSNR and MSE evaluation techniques to find which is a better filter to decrease Gaussian noise.

47

Chauhan, Vimal. "Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 301–13. http://dx.doi.org/10.22214/ijraset.2021.37965.

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Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

48

Ibrahim, Imanurfatiehah, Hamzah Arof, Mahazani Mohamad, and Mohamad Sofian Abu Talip. "Noise filtering of Hyperspectral Data of Oil Palms by Median Mean Projection Filtering." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 643–47. http://dx.doi.org/10.5954/icarob.2023.os26-1.

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49

Mary Odilya Teena, A., A. Karunamurthy, and D. Suresh Kumar. "Comparison of Median and Mean Filer for Investigating of Colour Images using MATLAB." Quing: International Journal of Innovative Research in Science and Engineering 1, no. 1 (March 30, 2022): 6–13. http://dx.doi.org/10.54368/qijirse.1.1.0003.

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50

Tang, Anan, Philippe Castagliola, Jinsheng Sun, and Xuelong Hu. "Optimal design of the adaptive EWMA chart for the mean based on median run length and expected median run length." Quality Technology & Quantitative Management 16, no. 4 (April 18, 2018): 439–58. http://dx.doi.org/10.1080/16843703.2018.1460908.

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