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Journal articles on the topic 'Mean performance'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

REDD, MARY LOU, and JUDITH W. ALEXANDER. "Does Certification Mean Better Performance?" Nursing Management (Springhouse) 28, no. 2 (1997): 45. http://dx.doi.org/10.1097/00006247-199702000-00013.

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2

Sharkey, Liz. "What does performance management mean?" Five to Seven 1, no. 2 (2001): 13–15. http://dx.doi.org/10.12968/ftse.2001.1.2.16960.

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3

Bhawsar, Subham, and Aastha Hajari. "Performance Analysis of K-mean Clustering Map for Different Nodes." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 1215–17. http://dx.doi.org/10.31142/ijtsrd18859.

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4

Bhat, S. S., and R. Vidya. "Performance of Ridge Estimators Based on Weighted Geometric Mean and Harmonic Mean." Journal of Scientific Research 12, no. 1 (2020): 1–13. http://dx.doi.org/10.3329/jsr.v12i1.40525.

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Ordinary least squares estimator (OLS) becomes unstable if there is a linear dependence between any two predictors. When such situation arises ridge estimator will yield more stable estimates to the regression coefficients than OLS estimator. Here we suggest two modified ridge estimators based on weights, where weights being the first two largest eigen values. We compare their MSE with some of the existing ridge estimators which are defined in the literature. Performance of the suggested estimators is evaluated empirically for a wide range of degree of multicollinearity. Simulation study indic

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Pillow, J. Jane. "Oscillatory Ventilator Performance: What Does It Mean?" Neonatology 108, no. 3 (2015): 229–32. http://dx.doi.org/10.1159/000438693.

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6

Breuer, Wolfgang, and Marc Gürtler. "Performance evaluation and preferences beyond mean-variance." Financial Markets and Portfolio Management 17, no. 2 (2003): 213–33. http://dx.doi.org/10.1007/s11408-003-0204-4.

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7

Fredrick, M. Njoka, and M. Kariuki Jeniffer. "Agronomic Performance of Single Crosses of Maize in Kiambu and Embu Counties of Kenya." Journal of Applied Life Sciences International 12, no. 1 (2017): 1–8. https://doi.org/10.9734/JALSI/2017/30932.

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<strong>Aims:</strong> This study was conducted to determine the agronomic performance of respective maize single crosses in different environments in varying soil and climatic zones in Kenya. <strong>Methodology:</strong> The trials were conducted at experimental stations of Kenya Agricultural and Livestock Research Organization (KALRO), Muguga South and KALRO Embu in Kiambu and Embu counties of Kenya respectively. The germplasm used in this study were 36 single crosses among 18 inbred lines of maize. The experiment was laid out in a 6 x 6 lattice randomized complete block design (RCBD) with

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8

Reb, Jochen. "EVALUATING DYNAMIC PERFORMANCE: EFFECTS OF TREND, MEAN, AND VARIATION OF PERFORMANCE ON PERFORMANCE RATINGS." Academy of Management Proceedings 2005, no. 1 (2005): J1—J6. http://dx.doi.org/10.5465/ambpp.2005.18778894.

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9

Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to t

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10

Margo Zeqo, Lorena, Eljona Milo Tasho, and Denisa Kafazi. "Bootstrapping the Seasonal Means and the Overall Mean in Rainfall Time Series." Interdisciplinary Journal of Research and Development 10, no. 1 (2023): 51. http://dx.doi.org/10.56345/ijrdv10n108.

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Several bootstrap methods have been studied nowadays for estimating the parameters in periodic data and it is important to consider the periodicity present in choosing the right bootstrap method. In this study we conducted a comparison of the performance of several block bootstrap methods designed for dependent data in the case of a real data time series with periodic structure such as the rainfall time series that is a time series with meteorological data collected monthly in a region in Albania. R programming language is used to perform the bootstrap and to obtain the results. We proposed a

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11

Santiago, Rafael De, and Javier Estrada. "Geometric Mean Maximization: Expected, Observed, and Simulated Performance." Journal of Investing 22, no. 2 (2013): 109–19. http://dx.doi.org/10.3905/joi.2013.22.2.106.

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12

Eliazar, Iddo, and Shlomi Reuveni. "Mean-performance of sharp restart I: statistical roadmap." Journal of Physics A: Mathematical and Theoretical 53, no. 40 (2020): 405004. http://dx.doi.org/10.1088/1751-8121/abae8c.

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13

Das, Rabindra Nath. "High-Performance Concrete Compressive Strength’s Mean-Variance Models." Journal of Materials in Civil Engineering 29, no. 5 (2017): 05016003. http://dx.doi.org/10.1061/(asce)mt.1943-5533.0001795.

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14

Aungier, R. H. "Mean Streamline Aerodynamic Performance Analysis of Centrifugal Compressors." Journal of Turbomachinery 117, no. 3 (1995): 360–66. http://dx.doi.org/10.1115/1.2835669.

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Aerodynamic Performance prediction models for centrifugal compressor impellers are presented. In combination with similar procedures for stationary components, previously published in the open literature, a comprehensive mean streamline performance analysis for centrifugal compressor stages is provided. The accuracy and versatility of the overall analysis is demonstrated for several centrifugal compressor stages of various types, including comparison with intrastage component performance data. Detailed validation of the analysis against experimental data has been accomplished for over a hundre

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15

Ritcey, James. "Performance Analysis of the Censored Mean-Level Detector." IEEE Transactions on Aerospace and Electronic Systems AES-22, no. 4 (1986): 443–54. http://dx.doi.org/10.1109/taes.1986.310780.

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16

Hyun-Chool Shin, Woo-Jin Song, and A. H. Sayed. "Mean-square performance of data-reusing adaptive algorithms." IEEE Signal Processing Letters 12, no. 12 (2005): 851–54. http://dx.doi.org/10.1109/lsp.2005.859500.

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17

Chevalier, Roger, Joe Harless, and Roger Kaufman. "What ISPI and performance improvement mean to me." Performance Improvement 51, no. 1 (2012): 16. http://dx.doi.org/10.1002/pfi.21236.

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18

Toenniges, Lisa, Darlene Van Tiem, and Robert Foshay. "What ISPI and performance improvement mean to me." Performance Improvement 51, no. 5 (2012): 5–6. http://dx.doi.org/10.1002/pfi.21261.

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19

Lane, Miki, and Clare Carey. "What ISPI and performance improvement mean to me." Performance Improvement 51, no. 6 (2012): 8. http://dx.doi.org/10.1002/pfi.21272.

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20

Rasile, Pat. "What ISPI and performance improvement mean to me." Performance Improvement 51, no. 9 (2012): 5. http://dx.doi.org/10.1002/pfi.21301.

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21

Vangavolu,, Sai Vinod. "Optimizing MongoDB Schemas for High-Performance MEAN Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (2025): 3061–68. https://doi.org/10.61841/turcomat.v11i3.15236.

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MongoDB, a document-oriented NoSQL database, is crucial in modern web applications, particularly in the MEAN (MongoDB, Express.js, Angular, Node.js) stack. The challenge with optimizing MongoDB schemas exists because of the no-schema database design approach combined with changing workload requirements. This article investigates the optimal approaches to creating and optimizing MongoDB schema designs. It focuses on normalization and denormalization decisions using shard and replication systems with workload-related optimizations and scale-up capabilities. The article evaluates modern AI schema

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22

NALINI, Sai Vinod Vangavolu. "Optimizing MongoDB Schemas for High-Performance MEAN Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (2020): 3061–68. https://doi.org/10.61841/turcomat.v11i3.15237.

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MongoDB, a document-oriented NoSQL database, is crucial in modern web applications, particularly in the MEAN (MongoDB, Express.js, Angular, Node.js) stack. The challenge with optimizing MongoDB schemas exists because of the no-schema database design approach combined with changing workload requirements. This article investigates the optimal approaches to creating and optimizing MongoDB schema designs. It focuses on normalization and denormalization decisions using shard and replication systems with workload-related optimizations and scale-up capabilities. The article evaluates modern AI schema

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23

Stein, Roberto, and Orlando E. Contreras-Pacheco. "Optimizing the performance of mean-variance portfolios in various markets: an “old-school” approach." Investment Management and Financial Innovations 15, no. 1 (2018): 190–207. http://dx.doi.org/10.21511/imfi.15(1).2018.17.

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The authors study the performance of mean-variance optimized (MVO) equity portfolios for retail investors in various markets in the U.S. and around the world. Actively managed equity mutual funds have relatively high fees and tend to underperform their benchmark. Index funds such as exchange traded funds still charge appreciable fees, and only deliver the performance of the benchmark. The authors find that MVO portfolios are relatively easy to manage by a retail investor, and that they tend to outperform their benchmark or, at worst, equal its performance, even after adjusting for risk. Moreov

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24

K.Sarode, Tanuja, and Vaishali D. Sakpal. "Performance Comparison of Transforms using Row Mean and Column Mean for Hand Gesture Recognition." International Journal of Computer Applications 78, no. 9 (2013): 1–5. http://dx.doi.org/10.5120/13514-1245.

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25

Akhmad, Arsandi, Lukas Lukas, and Bagus Mahawan. "Improving Performance Loan Fraud Model Prediction Using Mean Decrease Accuracy and Mean Decrease Gini." ACMIT Proceedings 6, no. 1 (2021): 36–41. http://dx.doi.org/10.33555/acmit.v6i1.94.

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The purpose of this research is to develop a fraud detection model on loan transactions at failed banks in the context of deposit and deposit guarantees mandated to the Indonesia Deposit Insurance Corporation (IDIC). The data used in this study is the data of a bank in the Jakarta area that had liquidated at the end of 2015. Meanwhile, data on loan transaction ranges ranged from 2010 to 2015. This research also focuses on improving the performance of detection models by using feature selection. With the feature selection, it expected that the impact of the reduced performance of the model expo

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26

Gubu, La, Edi Cahyono, Arman, Herdi Budiman, and Muh Kabil Djafar. "CLUSTER ANALYSIS FOR MEAN-VARIANCE PORTFOLIO SELECTION: A COMPARISON BETWEEN K-MEANS AND K-MEDOIDS CLUSTERING." Jurnal Riset dan Aplikasi Matematika (JRAM) 7, no. 2 (2023): 104–15. https://doi.org/10.26740/jram.v7n2.p104-115.

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This paper presents the Mean-Variance (MV) portfolio selection using cluster analysis. Stocks are categorized into various clusters using K-Means and K-Medoids clustering. Based on the Sharpe ratio, a stock from each cluster is chosen to represent that cluster. Stocks with the greatest Sharpe ratio are those that are chosen for each cluster. With the guidance of the MV portfolio model, the optimum portfolio is identified. When there are many stocks included in the formation of the portfolio, we may efficiently create the optimal portfolio using this method. For the empirical study, the daily r

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27

Subham, Bhawsar, and Hajari Aastha. "Performance Analysis of K mean Clustering Map for Different Nodes." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 1215–17. https://doi.org/10.31142/ijtsrd18859.

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WSNs is a group of inexpensive and autonomous sensor nodes interconnected and work together to monitor various environmental conditions such as humidity, temperature, pressure, and other climate changes. Wireless sensor nodes are randomly installed and communicate themselves through the wireless communication medium. In this paper we compared the simulation results of the K mean techniques for different numbers of nodes like 50, 100 and 150. Subham Bhawsar | Aastha Hajari "Performance Analysis of K-mean Clustering Map for Different Nodes" Published in International Journal of Trend i

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28

Ng, How Y., and Slawomir W. Hermanowicz. "MEMBRANE BIOREACTOR PERFORMANCE AT SHORT MEAN CELL RESIDENCE TIMES." Proceedings of the Water Environment Federation 2003, no. 5 (2003): 144–55. http://dx.doi.org/10.2175/193864703784606675.

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29

deVilliers, Johann. "Beyond Mean–Variance: Performance Measurement in a Nonsymmetrical World." CFA Digest 29, no. 3 (1999): 86–88. http://dx.doi.org/10.2469/dig.v29.n3.537.

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30

Sun, Zeyang, and Yingsong Li. "Nonnegative least mean mixed-norm algorithm: Analysis and performance." Digital Signal Processing 118 (November 2021): 103215. http://dx.doi.org/10.1016/j.dsp.2021.103215.

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31

Feuer, A. "Performance analysis of the block least mean square algorithm." IEEE Transactions on Circuits and Systems 32, no. 9 (1985): 960–63. http://dx.doi.org/10.1109/tcs.1985.1085808.

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32

Costa, M. H., J. C. M. Bermudez, and N. J. Bershad. "The performance surface in filtered nonlinear mean-square estimation." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 50, no. 3 (2003): 445–47. http://dx.doi.org/10.1109/tcsi.2003.808845.

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33

Green, T. J., and J. H. Shapiro. "Performance of split-window multipass-mean noise spectral estimators." IEEE Transactions on Aerospace and Electronic Systems 36, no. 4 (2000): 1360–70. http://dx.doi.org/10.1109/7.892683.

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34

Lippi, Giuseppe, Gian Luca Salvagno, Elisa Danese, et al. "Mean Platelet Volume (MPV) Predicts Middle Distance Running Performance." PLoS ONE 9, no. 11 (2014): e112892. http://dx.doi.org/10.1371/journal.pone.0112892.

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35

Bowden, Roger J. "The ordered mean difference as a portfolio performance measure." Journal of Empirical Finance 7, no. 2 (2000): 195–223. http://dx.doi.org/10.1016/s0927-5398(00)00015-3.

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36

Gueymard, C. "Prediction and Performance Assessment of Mean Hourly Global Radiation." Solar Energy 68, no. 3 (2000): 285–303. http://dx.doi.org/10.1016/s0038-092x(99)00070-5.

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37

Joro, Tarja, and Paul Na. "Portfolio performance evaluation in a mean–variance–skewness framework." European Journal of Operational Research 175, no. 1 (2006): 446–61. http://dx.doi.org/10.1016/j.ejor.2005.05.006.

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38

Bernardo, Rex. "Weighted vs. Unweighted Mean Performance of Varieties across Environments." Crop Science 32, no. 2 (1992): 490–92. http://dx.doi.org/10.2135/cropsci1992.0011183x003200020041x.

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39

Tay, Y. C., Rajan Suri, and Nathan Goodman. "A mean value performance model for locking in databases." Journal of the ACM 32, no. 3 (1985): 618–51. http://dx.doi.org/10.1145/3828.3831.

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40

Mon-Williams, M., R. Raw, R. Allen, and R. Wilkie. "Does better performance mean better learning in visuomotor tasks?" Journal of Vision 13, no. 9 (2013): 743. http://dx.doi.org/10.1167/13.9.743.

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41

Tefera, AA. "Mean performance and genetic variability of maize single crosses." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 41, no. 1 (2022): 12–21. http://dx.doi.org/10.36303/satnt.2022.41.1.864e.

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42

Qing, Zhu, Jingen Ni, Jie Chen, and H. C. So. "Diffusion least mean kurtosis algorithm and its performance analysis." Information Sciences 638 (August 2023): 118982. http://dx.doi.org/10.1016/j.ins.2023.118982.

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43

Kaur, Sandeep, and Shikha Chawla. "Evaluation of Performance of Fuzzy C Means and Mean Shift based Segmentation for Multi-Spectral Images." International Journal of Computer Applications 120, no. 16 (2015): 25–28. http://dx.doi.org/10.5120/21312-4287.

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44

Sahoo, Nirupama, and Saradaprasan Jena. "Improved Product-type Estimator for Estimating Population Mean using Harmonic Mean." INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 20, no. 01 (2024): 195. http://dx.doi.org/10.59467/ijass.2024.20.195.

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In this paper, we have developed a new product-type estimator using harmonic mean of known auxiliary variable. The bias and mean square error of proposed newly product -type estimator has been derived up to first order of approximation. An empirical study has been carried out to show the performance of proposed estimator along with existing estimators. It is observed that the proposed product-type estimator is more efficient than the competing estimators. For theoretical support a general study is also carried out using normal and Weibull distribution.. KEYWORDS :Bias, Mean square error, Produ

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45

Hollan, Michael A., and Brian C. Ancell. "Ensemble Mean Storm-Scale Performance in the Presence of Nonlinearity." Monthly Weather Review 143, no. 12 (2015): 5115–33. http://dx.doi.org/10.1175/mwr-d-14-00417.1.

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Abstract The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12- and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Shoul

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46

Purdy, Barry M., Michael R. Langemeier, and Allen M. Featherstone. "Financial Performance, Risk, and Specialization." Journal of Agricultural and Applied Economics 29, no. 1 (1997): 149–61. http://dx.doi.org/10.1017/s107407080000763x.

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AbstractA sample of Kansas farms was used to examine the impact of risk and specialization on mean financial performance. Mean financial performance was hypothesized to be influenced by risk, age of the operator, percentage of acres owned, financial efficiency, leverage, specialization, and farm size. Risk, age of operator, financial efficiency, and farm size had the largest impacts on mean financial performance. Specializing in swine, dairy, or crop production increased mean financial performance, while specializing in beef production decreased mean financial performance. Farms with both crop

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47

Robitzsch, Alexander. "Comparing Different Specifications of Mean–Geometric Mean Linking." Foundations 5, no. 2 (2025): 20. https://doi.org/10.3390/foundations5020020.

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Mean–geometric mean (MGM) linking compares group differences on a latent variable θ within the two-parameter logistic (2PL) item response theory model. This article investigates three specifications of MGM linking that differ in the weighting of item difficulty differences: unweighted (UW), discrimination-weighted (DW), and precision-weighted (PW). These methods are evaluated under conditions where random DIF effects are present in either item difficulties or item intercepts. The three estimators are analyzed both analytically and through a simulation study. The PW method outperforms the other

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48

Solomon, Joshua A., and Michael J. Morgan. "Calculation Efficiencies for Mean Numerosity." Psychological Science 29, no. 11 (2018): 1824–31. http://dx.doi.org/10.1177/0956797618790545.

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Relative numerosity is traditionally studied using texture pairs. Observers must decide which member of each pair has the greater total number of texture elements. In the present experiment, textures were segregated into nonoverlapping “sectors” containing between zero and four elements, and our observers were asked to select the texture containing the greater average number of texture elements (per sector). If observers were more sensitive to total numerosity than average numerosity, their performance (quantified by the just-noticeable Weber fraction) should have been better when the two text

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49

Vanti, Eka Nur, and Epha Diana Supandi. "Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance." Jurnal Fourier 9, no. 1 (2020): 25–34. http://dx.doi.org/10.14421/fourier.2020.91.25-34.

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Penelitian ini membahas tentang pembentukan portofolio optimal menggunakan model Mean Absolute Deviation (MAD) dan model Conditional Mean Variance (CMV). Pada model MAD risiko portofolio diukur menggunakan rata–rata deviasi standar sehingga portofolio optimal dapat diperoleh dengan menggunakan pemrograman linear. Sedangkan portofolio model CMV, rata–rata return diestimasi menggunakan model Autoregressive (AR) dan risiko (variansi) diestimasi menggunakan model GARCH. Selanjutnya kedua model portofolio diterapkan dalam membentuk portofolio optimal pada saham–saham yang terdaftar dalam Indeks Sah

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50

Solinap, Elmer. "Motivational Factors among Grade Six Maguindanaoan Learners in Mathematics at Sampao Elementary School." Psychology and Education: A Multidisciplinary Journal 21, no. 9 (2024): 950–66. https://doi.org/10.5281/zenodo.12699844.

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This study determined that the mathematics performance of the Grade 6 Maguindanaon learner was influenced by motivational factors. It adopted a descriptive-correlation research design with 15 learners and 15 parent participants. The study shared that Grade 6 Maguindanaon learners’ academic performance was generally Fair. Pearson R showed that the teacher factor affects academic performance. Parent and Peer factors do not affect their academic performance.  Results also showed that the attitude of learners significantly affects their academic performance, but study habits do not

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