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Journal articles on the topic 'Statistical methods of evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Patil, Shruti, and David J. Lilja. "Statistical methods for computer performance evaluation." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 1 (2011): 98–106. http://dx.doi.org/10.1002/wics.192.

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2

Seongyeon Kim and Soo Hyun Jang. "Improvement of Course Evaluation Using Statistical Methods." Journal of Seokdang Academy ll, no. 50 (2011): 691–706. http://dx.doi.org/10.17842/jsa.2011..50.691.

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3

Yin, Shen, Xiangping Zhu, and Hamid Reza Karimi. "Quality Evaluation Based on Multivariate Statistical Methods." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/639652.

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Quality prediction models are constructed based on multivariate statistical methods, including ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). The prediction model constructed by MPLSR achieves superior results, compared with the other three methods from both aspects of fitting efficiency and prediction ability. Based on it, further research is dedicated to selecting key variables to directly predict the product quality with satisfactory performance. The prediction models presented are more efficient than tradition ones and can be useful to support human experts in the evaluation and classification of the product quality. The effectiveness of the quality prediction models is finally illustrated and verified based on the practical data set of the red wine.

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4

Olin, Bryan D., and William Q. Meeker. "Applications of Statistical Methods to Nondestructive Evaluation." Technometrics 38, no. 2 (1996): 95–112. http://dx.doi.org/10.1080/00401706.1996.10484451.

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5

Karimi, Sarvnaz, Jie Yin, and Jiri Baum. "Evaluation Methods for Statistically Dependent Text." Computational Linguistics 41, no. 3 (2015): 539–48. http://dx.doi.org/10.1162/coli_a_00230.

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In recent years, many studies have been published on data collected from social media, especially microblogs such as Twitter. However, rather few of these studies have considered evaluation methodologies that take into account the statistically dependent nature of such data, which breaks the theoretical conditions for using cross-validation. Despite concerns raised in the past about using cross-validation for data of similar characteristics, such as time series, some of these studies evaluate their work using standard k-fold cross-validation. Through experiments on Twitter data collected during a two-year period that includes disastrous events, we show that by ignoring the statistical dependence of the text messages published in social media, standard cross-validation can result in misleading conclusions in a machine learning task. We explore alternative evaluation methods that explicitly deal with statistical dependence in text. Our work also raises concerns for any other data for which similar conditions might hold.

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6

Berens, Alan P. "[Applications of Statistical Methods to Nondestructive Evaluation]: Discussion." Technometrics 38, no. 2 (1996): 113. http://dx.doi.org/10.2307/1270400.

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7

Coleman, David, and Philip Ramsey. "[Applications of Statistical Methods to Nondestructive Evaluation]: Discussion." Technometrics 38, no. 2 (1996): 117. http://dx.doi.org/10.2307/1270401.

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8

Spencer, Floyd W. "[Applications of Statistical Methods to Nondestructive Evaluation]: Discussion." Technometrics 38, no. 2 (1996): 122. http://dx.doi.org/10.2307/1270402.

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9

Sweeting, Trevor J. "[Applications of Statistical Methods to Nondestructive Evaluation]: Discussion." Technometrics 38, no. 2 (1996): 124. http://dx.doi.org/10.2307/1270403.

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10

Tucker, William T. "[Applications of Statistical Methods to Nondestructive Evaluation]: Discussion." Technometrics 38, no. 2 (1996): 126. http://dx.doi.org/10.2307/1270404.

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11

Olin, Bryan D., and William Q. Meeker. "[Applications of Statistical Methods to Nondestructive Evaluation]: Reply." Technometrics 38, no. 2 (1996): 128. http://dx.doi.org/10.2307/1270405.

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12

Kim, J. H., S. M. Kim, K. H. Baek, et al. "Evaluation of satellite-derived HCHO using statistical methods." Atmospheric Chemistry and Physics Discussions 11, no. 3 (2011): 8003–25. http://dx.doi.org/10.5194/acpd-11-8003-2011.

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Abstract. Most previous evaluations of satellite performance relied on pair wise comparisons with limited spatial and temporal coverage of surface-based measurements such as soundings and in-situ measurements. Especially, validation of satellite HCHO measurements is very difficult because ground-based HCHO measurements are extremely sparse. We use a new scientific approach, statistical analyses with empirical orthogonal function (EOF) and singular value decomposition (SVD), to provide three-dimensional results of comparisons with a global picture over long measurement periods. The EOF and SVD analyses with GOME, SCIAMACHY and OMI HCHO, and MOPITT CO show dipole distributions oscillating between northern and southern equatorial Africa with an annual cycle. This feature is exactly coincident with the spatial and temporal pattern of biomass burning occurring over tropical Africa. The double-peaked maximum seen in OMI HCHO is only marginally observed in SCIAMACHY and GOME HCHO seasonality over the northern tropical region during northern biomass-burning season. Spatial and temporal difference between two datasets may cause this discrepancy, but the detailed analysis for the cause requires an examination with a chemical model. The statistical analyses of all data indicate that biomass-burning activity over South America is responsible for the HCHO seasonality over that continent. We have not observed any evidence to support the influence of biogenic activity on HCHO over these regions; however, we find robust evidence that biomass burning is the strongest source of HCHO over tropical Africa and South America. We also found that these statistical tools are a very efficient method for evaluating satellite data.

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13

Kwon, M. J., B. M. Lee, J. Y. Bae, et al. "Statistical evaluation methods for identifying probable nephrotoxic biomarkers." Toxicology Letters 205 (August 2011): S96. http://dx.doi.org/10.1016/j.toxlet.2011.05.350.

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14

HURST, JANET B., and MICHAEL L. MILLARD. "Evaluation of alpha-SiC Sintering Using Statistical Methods." Journal of the American Ceramic Society 68, no. 7 (1985): C—178—C—181. http://dx.doi.org/10.1111/j.1151-2916.1985.tb10166.x.

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15

Mosinski, F., and T. Piotrowski. "New statistical methods for evaluation of DGA data." IEEE Transactions on Dielectrics and Electrical Insulation 10, no. 2 (2003): 260–65. http://dx.doi.org/10.1109/tdei.2003.1194108.

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16

Baumgarten, A. "STATISTICAL EVALUATION OF THE METHODS STANDARDIZED BY CEN." Acta Horticulturae, no. 548 (March 2001): 647–54. http://dx.doi.org/10.17660/actahortic.2001.548.80.

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17

Yang, Mark C. K., Ronald G. Marks, William B. Clark, and Ingvar Magnusson. "Evaluation of statistical methods for monitoring periodontal disease." Statistics in Medicine 10, no. 7 (1991): 1089–97. http://dx.doi.org/10.1002/sim.4780100709.

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18

NAKASONE, Yuji, Takashi TOIDA, Akiyoshi SHIN-YA, Yoshiko TOMITA, and Sachie KISHIDA. "0904 Clinical Evaluation of Crown by Statistical Methods." Proceedings of the JSME annual meeting 2007.6 (2007): 7–8. http://dx.doi.org/10.1299/jsmemecjo.2007.6.0_7.

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19

Jowitt, Ronald. "Sensory evaluation of food — Statistical methods and procedures." Journal of Food Engineering 6, no. 6 (1987): 478–79. http://dx.doi.org/10.1016/0260-8774(87)90009-4.

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20

Novák, J., H. Sůvová, and J. Vondráček. "Multivariate statistical methods as a tool of financial analysis of farm businesses." Agricultural Economics (Zemědělská ekonomika) 48, No. 1 (2012): 9–12. http://dx.doi.org/10.17221/5281-agricecon.

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The paper is focused on the evaluation of the possibilities of analysing the relations between economic and financial indicators of farm businesses by the application of multivariate statistical methods. It also indicates the possibilities of the construction of a general economic indicator of business effectiveness.

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21

Gang-Ling, Hou, Ge Bin, Sun Liang-Liang, and Xing Kai-Xin. "A study on wine sensory evaluation by the statistical analysis method." Czech Journal of Food Sciences 38, No. 1 (2020): 1–10. http://dx.doi.org/10.17221/438/2017-cjfs.

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In this paper, we construct a rating credibility model of red wine by the Analytic Hierarchy Process, achieve the classification of red grapes through the evaluation results of red wine and cluster analysis method and analyze the correlation of the physical and chemical indicators between red grapes and red wine. Thus, the paper demonstrates that aromatic substances play an important role in the quality of red wine, so we cannot evaluate the quality of wine only by the physical and chemical indicators of wine grapes and wine.

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22

Lamont, Andrea E., Robert S. Markle, Annie Wright, et al. "Innovative Methods in Evaluation." American Journal of Evaluation 39, no. 3 (2017): 364–82. http://dx.doi.org/10.1177/1098214017709736.

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Traditional methods of evaluation are limited in their ability to answer key questions often of interest to process evaluators, such as heterogeneity in the ways individuals adopt new programs. In this article, we demonstrate how a statistical approach, Latent Class Analysis, can help improve the quality of process evaluations and illustrate its use in an evaluation of an educational technology integration program in a large school district. In this illustration, we were interested in detecting variability in the ways teachers adopted the new program. We defined classes based on a set of innovative teaching strategies associated with educational technology. Results showed five distinct subgroups of teachers, based on level of program adoption. Results also demonstrate that specific school support strategies (professional development and personalized computing devices) facilitated program adoption. These findings can help to inform individualized support for teachers to optimize the ease and quality with which they are able to adopt new skills in the classroom.

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23

Parkin, T. B., J. J. Meisinger, S. T. Chester, J. L. Starr, and J. A. Robinson. "Evaluation of Statistical Estimation Methods for Lognormally Distributed Variables." Soil Science Society of America Journal 52, no. 2 (1988): 323–29. http://dx.doi.org/10.2136/sssaj1988.03615995005200020004x.

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24

Parkin, T. B., J. J. Meisinger, S. T. Chester, J. L. Starr, and J. A. Robinson. "Evaluation of Statistical Estimation Methods for Lognormally Distributed Variables." Soil Science Society of America Journal 52, no. 2 (1988): NP. http://dx.doi.org/10.2136/sssaj1988.03615995005200020062x.

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25

Chiaramonte de Castro, Bruno José, and Andre Bernardo. "EVALUATION OF CANE SUGAR PRODUCTION USING MULTIVARIATE STATISTICAL METHODS." Journal of Engineering and Exact Sciences 5, no. 3 (2019): 0228–37. http://dx.doi.org/10.18540/jcecvl5iss3pp0228-0237.

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26

Mikšı́k, Ivan, Adam Eckhardt, Tibor Cserháti, Esther Forgács, Josef Zicha, and Zdeněk Deyl. "Evaluation of peptide electropherograms by multivariate mathematical–statistical methods." Journal of Chromatography A 921, no. 1 (2001): 81–91. http://dx.doi.org/10.1016/s0021-9673(01)00897-4.

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27

Tsunoo, Yukiyasu, Ryoji Ohta, Hiroshi Miyauchi, and Katsuhiro Nakamura. "A cipher strength evaluation system based on statistical methods." Systems and Computers in Japan 34, no. 9 (2003): 83–92. http://dx.doi.org/10.1002/scj.10163.

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28

Pugh, G. Allen. "The evaluation of statistical process control methods by simulation." Computers & Industrial Engineering 15, no. 1-4 (1988): 360–63. http://dx.doi.org/10.1016/0360-8352(88)90112-x.

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29

Clemmens, A. J., and M. G. Bos. "Statistical methods for irrigation system water delivery performance evaluation." Irrigation and Drainage Systems 4, no. 4 (1990): 345–65. http://dx.doi.org/10.1007/bf01103713.

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30

Eidenberger, Horst. "Evaluation of content-based image descriptors by statistical methods." Multimedia Tools and Applications 35, no. 3 (2007): 241–58. http://dx.doi.org/10.1007/s11042-007-0106-y.

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31

Ataş, Gültekin, and Vehbi Cagri Gungor. "Performance evaluation of cloud computing platforms using statistical methods." Computers & Electrical Engineering 40, no. 5 (2014): 1636–49. http://dx.doi.org/10.1016/j.compeleceng.2014.03.017.

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32

Park, Gyu-Ro, In-Jung Kim, and Cheng-Lin Liu. "An evaluation of statistical methods in handwritten hangul recognition." International Journal on Document Analysis and Recognition (IJDAR) 16, no. 3 (2012): 273–83. http://dx.doi.org/10.1007/s10032-012-0191-y.

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33

Schmidt, M. E., and K. Steindorf. "Statistical Methods for the Validation of Questionnaires." Methods of Information in Medicine 45, no. 04 (2006): 409–13. http://dx.doi.org/10.1055/s-0038-1634096.

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Summary Objectives: Questionnaires used in epidemiological studies should be validated. However, unclarity exists about the appropriate statistical methods and interpretation of validation studies. Thus, we investigated the theory and practice of statistical evaluation approaches. Methods: Using three platforms, a literature review, own simulations, and a validation study performed by ourselves, we worked out relevant limitations, advantages, and new important aspects of evaluation methods. Results: Our systematic literature review, based on physical activity questionnaires, revealed that correlation coefficients are still the common approach in validation studies, found in 41 of 46 reviewed publications (89.1%). This practice has been criticized in the theoretically oriented literature for more than 20 years. Appropriate evaluation methods as recommended by Bland and Altman were found in only ten publications (21.7 %).We showed that serious bias in questionnaires can be revealed by Bland-Altman plots but may remain undetected by correlation coefficients. With our simulations we refuted the argument that correlation coefficients properly investigate whether a questionnaire ranks the subjects sufficiently well. Further, with Bland-Altman analyses we could evaluate differential errors with respect to case-control status in our validation study. Yet, this was not possible with correlation coefficients, because they generally do not identify systematic bias. In addition, we show a potential pitfall in the interpretation of Bland-Altman plots that might occur in specific rare instances. Conclusions: The commonly used correlation approach can yield misleading conclusions in validation studies. A more frequent and proper use of the Bland-Altman methods would be desirable to improve epidemiological data quality.

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34

Monachon, Mathilde, Magdalena Albelda-Berenguer, Tiziana Lombardo, et al. "Evaluation of Bio-Based Extraction Methods by Spectroscopic Methods." Minerals 10, no. 2 (2020): 203. http://dx.doi.org/10.3390/min10020203.

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New technologies are in development regarding the preservation of waterlogged archaeological wood items contaminated with Fe/S species. To this purpose, a bio-based treatment to extract these harmful species before further damages occur is presented. Thiobacillus denitrificans and desferoxamine were employed based on their specific properties to solubilize iron sulfides and uptake iron. The biological treatment was compared with oxidizing and complexing agents (sodium persulfate and ethylene diamine tetraacetate) traditionally used in conservation-restoration. Mock-ups of fresh balsa as well as fresh and archeological oak and pinewood were prepared to simulate degraded waterlogged wood by immersion in corrosive Fe/S solutions. The efficiency of both biological and chemical extraction methods was evaluated through ATR-FTIR and Raman spectroscopies and validated by statistical approach. Results showed that treatments did not affect the wood composition, meaning that no wood degradation was induced. However, the chemical method tended to bleach the samples and after treatment, reduced sulfur species were still identified by Raman analyses. Finally, statistical approaches allowed validating ATR-FTIR results.

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35

Niroumand, Hamed, M. F. M. Zain, and Maslina Jamil. "Statistical Methods for Comparison of Data Sets of Construction Methods and Building Evaluation." Procedia - Social and Behavioral Sciences 89 (October 2013): 218–21. http://dx.doi.org/10.1016/j.sbspro.2013.08.837.

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36

Xi, Xiaobo, Jiawen Xu, Jingyun Yuan, Yifu Zhang, Baofeng Zhang, and Ruihong Zhang. "Damage Evaluation in Tempered Vacuum Glazing via Multivariate Statistical Methods." Applied Sciences 11, no. 11 (2021): 4799. http://dx.doi.org/10.3390/app11114799.

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The performance of tempered vacuum glazing (TVG) strongly depends on the structural parameters and degree of damage of the products. In this paper, attention was paid to six performance indicators which had a major influence on the damage of TVG, and new evaluation parameters were derived from them using principal component analysis (PCA). In particular, hierarchical clustering analysis (HCA) based on Euclidean distance measurement enabled TVG products to be classified into three kinds. Considering the results of PCA, product quality classification was established according to the degree of damage. The evaluation method proposed in this work was found to be simple and reliable to provide references for damage detection of TVG.

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37

Bonato, Jasminka, Biserka Draščić Ban, and Antun Kraš. "Statistical Methods in Theory of Technical Systems." Journal of Maritime & Transportation Science 3, no. 3 (2020): 373–79. http://dx.doi.org/10.18048/2020.00.28.

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Today’s society is served by modern technology, accompanied by effects of insecurity and uncertainty, which are important and inevitable. Engineering analysis must include methods of assessing these impacts on the design, implementation and operation of technical systems. Mathematical statistics and probability theory provide the mathematical basis for modeling unreliability and analysis as well as their impact on already built technical systems. The aim of the paper is to introduce quantitative methods that enable systematic development of planned and constructed criteria, problem modeling and evaluation of economically technical optimum.

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38

Kovtun, N., and V. Babirad-Lazunin. "METHODOLOGICAL PRINCIPLES AND METHODS OF TERMS OF TRADE STATISTICAL EVALUATION." Bulletin of Taras Shevchenko National University of Kyiv Economics, no. 163 (2014): 26–31. http://dx.doi.org/10.17721/1728-2667.2014/163-10/5.

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39

Cao, Wenjun, Yunming Li, Danhong Liu, Changsheng Chen, and Yongyong Xu. "Statistical and Biological Evaluation of Different Gene Set Analysis Methods." Procedia Environmental Sciences 8 (2011): 693–99. http://dx.doi.org/10.1016/j.proenv.2011.10.106.

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40

Luo, Hengrui, and Shili Lin. "Evaluation of Classical Statistical Methods for Analyzing BS-Seq Data." OBM Genetics 2, no. 4 (2018): 1. http://dx.doi.org/10.21926/obm.genet.1804053.

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41

Dharmadhikari, Vineet S., R. O. Lynch, W. Brennan, W. Cronin, and R. Rastogi. "Physical vapor deposition equipment evaluation and characterization using statistical methods." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 8, no. 3 (1990): 1603–7. http://dx.doi.org/10.1116/1.576773.

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42

Medearis, Arnold L., Moraye B. Bear, Greigh I. Hirata, and Lawrence D. Platt. "Statistical Materials and Methods in the Evaluation of Fetal Growth." Ultrasound Quarterly 7, no. 2 (1989): 191. http://dx.doi.org/10.1097/00013644-198900720-00003.

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43

Medearis, Arnold L., Moraye B. Bear, Greigh I. Hirata, and Lawrence D. Platt. "Statistical Materials and Methods in the Evaluation of Fetal Growth." Ultrasound Quarterly 7, no. 2 (1989): 191. http://dx.doi.org/10.1097/00013644-198906000-00003.

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44

Hartwell, S. I., H. J. Jin, D. S. Cherry, and J. Cairns. "Evaluation of statistical methods for avoidance data of schooling fish." Hydrobiologia 131, no. 1 (1986): 63–76. http://dx.doi.org/10.1007/bf00008325.

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45

Almeida, Alexandre P. R. P., and Jinyuan Liu. "Statistical evaluation of design methods for micropiles in Ontario soils." DFI Journal - The Journal of the Deep Foundations Institute 12, no. 3 (2018): 133–46. http://dx.doi.org/10.1080/19375247.2019.1595994.

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46

Thiry, Nicolas, and Massimiliano Vasile. "Statistical multi-criteria evaluation of non-nuclear asteroid deflection methods." Acta Astronautica 140 (November 2017): 293–307. http://dx.doi.org/10.1016/j.actaastro.2017.08.021.

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47

Liang, Z., R. Jaszczak, and H. Hart. "Study and performance evaluation of statistical methods in image processing." Computers in Biology and Medicine 18, no. 6 (1988): 395–408. http://dx.doi.org/10.1016/0010-4825(88)90057-1.

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48

Wandke, Ernst, and Winfried Rasemann. "Design and evaluation of abrasion pot experiments using statistical methods." Wear 118, no. 3 (1987): 265–79. http://dx.doi.org/10.1016/0043-1648(87)90071-8.

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49

Murison, Bob, John Ayres, Leah Lane, and Derek Woodfield. "Statistical methods to address spatial variation in pasture evaluation trials." NZGA: Research and Practice Series 12 (January 1, 2006): 49–53. http://dx.doi.org/10.33584/rps.12.2006.3043.

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The primary breeding objective for white clover improvement in eastern Australia is more reliable persistence under summer moisture-stress while maintaining herbage production (especially winter growth). To identify elite germplasm, evaluation of candidate lines is done in situ, under field conditions for which the prospective cultivar is targeted. These conditions (e.g. moisture stress, nutrient infertility, grass competition) may impose spatial influences within the field site that as artefacts mask or alter the uniform expression of response to environment. A significant component of artefact variation which affects interpretation of the data is the spatial variation due to plot position. Failure to correct for this may give unreliable comparisons as some lines inadvertently (through randomisation) are allocated to favourable or unfavourable plots. Our methodology was developed at two sites (Glen Innes , Armidale in northern New South Wales, Australia) in an experiment which sought to identify superior lines among 20 candidate entries. Cultivars of reputed stability and spanning the expected performance range, are replicated at regular intervals through the field site. The difference amongst replicates within cultivar (that is a reduced G X E) is used to estimate spatial trends. Elite lines are identified from temporal profiles of plant performance related to the breeding objectives. We describe (i) design and analysis which allows estimation of the spatial effects, and (ii) the temporal profiling of plant performance. These statistical procedures are seminal in identifying superior breeding lines that subsequently proceed to cultivar development.

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50

Frisén, M. "Evaluations of methods for statistical surveillance." Statistics in Medicine 11, no. 11 (1992): 1489–502. http://dx.doi.org/10.1002/sim.4780111107.

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