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Relevant bibliographies by topics / Statistical methods of evaluation

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Academic literature on the topic 'Statistical methods of evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Statistical methods of evaluation"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Statistical methods of evaluation"

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Cameron, Maxwell Hugh 1943. "Statistical evaluation of road trauma countermeasures." Monash University, Dept. of Mathematics and Statistics, 2000. http://arrow.monash.edu.au/hdl/1959.1/7943.

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Chung, Yuk-ka, and 鍾玉嘉. "On the evaluation and statistical analysis of forensic evidence in DNAmixtures." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45983586.

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Choy, Yan-tsun, and 蔡恩浚. "Statistical evaluation of mixed DNA stains." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664287.

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Li, Longzhuang. "Statistical methods for performance evaluation and their applications /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3060118.

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Bujatzeck, Baldur. "Statistical evaluation of water quality measurements." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0017/MQ44134.pdf.

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Knopp, Jeremy Scott. "Modern Statistical Methods and Uncertainty Quantification for Evaluating Reliability of Nondestructive Evaluation Systems." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1395942220.

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Ackerberg, Björn. "Application of some statistical methods for evaluation of groundwater observations." Licentiate thesis, KTH, Land and Water Resources Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1531.

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<p>With the objective of reviewing different statisticalmethods for evaluation of groundwater data and the design of agroundwater observation network, a comprehensive literaturesurvey was performed. The literature survey focuses on spatialstatistics (geostatistics) but also includes methods toevaluate time-series of groundwater data and the determinationof the sampling frequency. A method is developed which providesa means of quantifying the accuracy of an existing groundwatermonitoring network with regards to spatial interpolation andthe locations of the corresponding observation points. Thespatial interpolation method of ordinary kriging was used. Aresult from ordinary kriging was estimated (interpolated)levels at unmeasured points, but also a kriging variance. Thekriging variance can be interpreted as a measure of theestimation accuracy and used as a criterion for network design.Design of a monitoring network for groundwater levels in anarea includes the selection of: - the number of observationpoints and - the spatial locations of observation points.The method was applied to design a monitoring network inan area in a glaciofluvial deposit, the Nybro esker, which isthe main aquifer for the water supply of the Kalmar-Nybroregion in the southeast of Sweden. This thesis shows that it ispossible to quantify the accuracy of an existing observationnetwork using the average kriging variance as a measure ofaccuracy. It is also possible to describe how this krigingvariance changes (increases) when the observation network isreduced. By using this variance is it possible to rank thedifferent points in the network as to their relativeimportance. It is thus possible to identify the points, whichare to be removed when the observation network is reduced, onepoint at a time. This study shows that a monitoring network inthe study area could be reduced by 35% while the increase inaverage estimation (kriging) variance is only about 10%.Although the method is applied to groundwater levels in aglaciofluvial deposit, it is applicable also to other variablesthat can be considered regionalized and to other geologicalenvironments.</p>

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Elia, Eleni. "Statistical methods in prognostic factor research : application, development and evaluation." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7259/.

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In patients with a particular disease or health condition, prognostic factors are characteristics (such as age, biomarkers) that are associated with different risks of a future clinical outcome. Research is needed to identify prognostic factors, but current evidence suggests that primary research is of low quality and poorly/selectively reported, which limits subsequent systematic reviews and meta-analysis. This thesis aims to improve prognostic factor research, through the application, development and evaluation of statistical methods to quantify the effect of potential prognostic factors. Firstly, I conduct a new prognostic factor study in pregnant women. The findings suggest that the albumin/creatinine ratio (ACR) is an independent prognostic factor for neonatal and, in particular, maternal composite adverse outcomes; thus ACR may enhance individualised risk prediction and clinical decision-making. Then, a literature review is performed to flag challenges in conducting meta-analysis of prognostic factor studies in the same clinical area. Many issues are identified, especially between-study heterogeneity and potential bias in the thresholds (cut-off points) used to dichotomise continuous factors, and the set of adjustment factors. Subsequent chapters aim to tackle these issues by proposing novel multivariate meta-analysis methods to ‘borrow strength’ across correlated thresholds and/or adjustment factors. These are applied to a variety of examples, and evaluated through simulation, which show how the approach can reduce bias and improve precision of meta-analysis results, compared to traditional univariate methods. In particular, the percentage reduction in the variance is of a similar magnitude to the percentage of data missing at random.

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Nanzad, Bolorchimeg. "EVALUATION OF STATISTICAL METHODS FOR MODELING HISTORICAL RESOURCE PRODUCTION AND FORECASTING." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2192.

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This master’s thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed “cycle-jumping” wherein overlap of multiple cycles is limited. The model is designed in a way that each cycle is described by the same three parameters as conventional multicyclic Hubbert model and every two cycles are connected with a transition width. Transition width indicates the shift from one cycle to the next and is described as weighted coaddition of neighboring two cycles. It is determined by three parameters: transition year, transition width, and γ parameter for weighting. The cycle-jumping method provides superior model compared to the conventional multicyclic Hubbert model and reflects historical production behavior more reasonably and practically, by better modeling of the effects of technological transitions and socioeconomic factors that affect historical resource production behavior by explicitly considering the form of the transitions between production cycles.

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Matysek, Paul Frank. "An evaluation of regional stream sediment data by advanced statistical procedures." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/24860.

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This study was directed towards the development of rigorous, systematic, computer-assisted statistical procedures for the interpretation of quantitative and qualitative data commonly encountered in practical exploration-oriented surveys. A suite of data analysis tools were developed to evaluate the quality of geochemical data sets, to investigate the value and utilization of categorical field data, and to recognize and rank anomalous samples. Data obtained from regional stream sediment surveys as undertaken by the British Columbia Ministry of Energy, Mines and Petroleum Resources in southern British Columbia were examined as a case history. A procedure based on a statistical analysis of field-site duplicates was developed to evaluate the quality of regional geochemical silt data. The technique determines: (1) whether differences in metal concentrations between sample sites reflect a real trend related to geological and geochemical features and not merely a consequence of sampling and analytical error, and (2) absolute precision estimates at any particular accumulation across a metal's concentration range. Results for metals Zn, Cu, Ni, Co, Fe and Mn indicated that combined variability due to local and procedural error averaged less than 5% of the total error and that precision estimates at the 95th percentile concentration value averaged less than 6.0%. Results presented indicate duplicates are more in accord with splits of individual samples (analytical duplicates) rather than separate field-site duplicates. This type of systematic approach provides a basis for interpreting geochemical trends within the survey area, while simultaneously allowing evaluation of the method of sampling and laboratory analysis. A procedure utilizing Duncan's Multiple Range Test examined the relationships between metal concentrations and class-interval and categorical observations of the drainage catchment, sample site and sediment sample. Results show that, many field observations can be systematically related to metal content of drainage sediments. Some elements are more susceptible than others to environmental factors and some factors influence few or many elements. For example, in sediments derived from granites there are significant relationships between bank type and concentration of 8 elements (Zn, Cu, Ni, Pb, Co, Fe, Mn and Hg). In contrast, the texture of these sediments, using estimates of fines contents as an index, did not significantly affect the concentration of any of the elements studied. In general, results indicate that groups of environmental factors acting collectively are more important than any single factor in determining background metal contents of drainage sediments. A procedure utilizing both a graphical and multiple regression approach was developed to identify and characterize anomalous samples. The procedure determines multivariate models based on background metal values which are used to describe very general geochemical relations of no interest for prospecting purposes. These models are then applied to sample subsets selected on the basis of factor/s known to strongly influence geochemical results. Individual samples are characterized after comparisons with relevant determined threshold levels and background multielemenmodels. One hundred and fifteen anomalous samples for zinc from seven provenance groups draining 1259 sample sites were identified and characterized by this procedure. Forty three of these samples had zinc concentrations greater than its calculated provenance threshold, while 72 of these anomalous samples were identified solely because their individual metal associations were significantly different than their provenance multivariate background model. The method provides a means to reduce the effects of background variations while simultaneously identifying and characterizing anomalous samples. The data analysis tools described here allow extraction of useful information from regional geochemical data, and as a result provide and effective means of defining problems of geological interest that warrant further investigation.<br>Science, Faculty of<br>Earth, Ocean and Atmospheric Sciences, Department of<br>Graduate

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Books on the topic "Statistical methods of evaluation"

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Kempton, R. A., P. N. Fox, and M. Cerezo, eds. Statistical Methods for Plant Variety Evaluation. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1503-9.

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Goovaerts, Pierre. Geostatistics for natural resources evaluation. Oxford University Press, 1997.

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Mandel, John. Measurement evaluation. U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Mandel, John. Measurement evaluation. U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Mandel, John. Measurement evaluation. National Bureau of Standards, 1986.

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Schuckers, Michael E. Computational methods in biometric authentication: Statistical methods for performance evaluation. Springer, 2010.

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Clark, Isobel. Statistical tables for mineral reserve evaluation. Geostokos, 1987.

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Vincenza, Capursi, and SpringerLink (Online service), eds. Statistical Methods for the Evaluation of University Systems. Springer-Verlag Berlin Heidelberg, 2011.

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Sensory evaluation of food: Statistical methods and procedures. M. Dekker, 1986.

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Attanasio, Massimo, and Vincenza Capursi, eds. Statistical Methods for the Evaluation of University Systems. Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2375-2.

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Book chapters on the topic "Statistical methods of evaluation"

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Hothorn, Ludwig A. "Statistical Evaluation Methods in Toxicology." In Regulatory Toxicology. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35374-1_44.

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Hothorn, Ludwig A. "Statistical Evaluation Methods in Toxicology." In Regulatory Toxicology. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-36206-4_44-2.

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Hothorn, Ludwig A. "Statistical Evaluation Methods in Toxicology." In Regulatory Toxicology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57499-4_44.

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Thomson, Blaise. "Evaluation." In Statistical Methods for Spoken Dialogue Management. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4923-1_6.

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Harris-Kojetin, Brian A., and James M. Dahlhamer. "Using Field Tests to Evaluate Federal Statistical Survey Questionnaires." In Question Evaluation Methods. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118037003.ch19.

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Berghaus, Donald. "Statistical Evaluation for Data Quality." In Numerical Methods for Experimental Mechanics. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1473-2_1.

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Conigliani, Caterina, Andrea Manca, and Andrea Tancredi. "Statistical Methods for Healthcare Economic Evaluation." In Statistical Methods in Healthcare. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119940012.ch18.

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Musonda, Patrick. "Statistical Issues in Vaccine Safety Evaluation." In Statistical Methods in Healthcare. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119940012.ch17.

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Rosenkranz, G., and R. Uhl. "Statistical Evaluation of Offspring Parameters in Embryotoxicity Studies." In Statistical Methods in Toxicology. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-48736-1_12.

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Dobbin, Kevin K. "Statistical Design and Evaluation of Biomarker Studies." In Methods in Molecular Biology. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-727-3_36.

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Conference papers on the topic "Statistical methods of evaluation"

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Kitchenham, Barbara. "Robust statistical methods." In EASE '15: 19th International Conference on Evaluation and Assessment in Software Engineering. ACM, 2015. http://dx.doi.org/10.1145/2745802.2747956.

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Koh, Y. M., and W. Q. Meeker. "Methods for planning a statistical POD study." In REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION: VOLUME 32. AIP, 2013. http://dx.doi.org/10.1063/1.4789249.

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Lei Liu, Yujian An, Zeyang Fan, and Wei Sun. "Quality evaluation based on multivariate statistical forecasting methods." In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7745033.

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"STATISTICAL METHODS FOR THE EVALUATION OF INDEXING PHRASES." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003054801410149.

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Graham, Yvette, Barry Haddow, and Philipp Koehn. "Statistical Power and Translationese in Machine Translation Evaluation." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.6.

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Santoso, S., D. D. Sabin, and M. F. McGranaghan. "Evaluation of harmonic trends using statistical process control methods." In Exposition. IEEE, 2008. http://dx.doi.org/10.1109/tdc.2008.4517137.

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Arhipova, Irina, and Liga Paura. "APPLICATION OF STATISTICAL METHODS FOR STUDY PROGRAMS EFFICIENCY EVALUATION." In 13th International Technology, Education and Development Conference. IATED, 2019. http://dx.doi.org/10.21125/inted.2019.0768.

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Laino, David, and Kirk Pierce. "Evaluating statistical loads extrapolation methods." In 2000 ASME Wind Energy Symposium. American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-64.

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Zhou, Zhongxing, Lingxiao Wang, and Lin Zhang. "Noise and Resolution Performance Evaluation for Statistical and Non-Statistical Iterative CBCT Reconstruction Methods." In the 3rd International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3278229.3278246.

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Lin, Fang, Ali Ahmadi, Kannan Sekar, Yan Pan, and Ke Huang. "IC layout weak point effectiveness evaluation based on statistical methods." In 2018 IEEE 36th VLSI Test Symposium (VTS). IEEE, 2018. http://dx.doi.org/10.1109/vts.2018.8368655.

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Reports on the topic "Statistical methods of evaluation"

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Flanberg, Stephen E. Statistical Evaluation and Modeling of Experimental Methods to Measure Deception. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440838.

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Wurtz, Ron E. Statistical Methods for Improved Evaluation of Environmental Sample Data quarterly FY18Q2. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1432977.

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Gilbert, R. O., and J. C. Simpson. Statistical methods for evaluating the attainment of cleanup standards. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/6637176.

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Chou, Charissa J. Evaluation of an Alternative Statistical Method for Analysis of RCRA Groundwater Monitoring Data at the Hanford Site. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/15010617.

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Gilbert, R. O., and J. C. Simpson. Statistical methods for evaluating the attainment of cleanup standards. Volume 3, Reference-based standards for soils and solid media, Revision 1. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10122249.

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Azarm, M. A., F. Hsu, G. Martinez-Guridi, and W. E. Vesely. Methods for dependency estimation and system unavailability evaluation based on failure data statistics. Volume 1, Summary report. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10175230.

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Azarm, M. A., F. Hsu, G. Martinez-Guridi, and W. E. Vesely. Methods for dependency estimation and system unavailability evaluation based on failure data statistics. Volume 2, Detailed description and applications. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10170419.

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Black, Paul E. Formal methods for statistical software. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.ir.8274.

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Banks, David, William Dashiell, Leonard Gallagher, Charles Hagwood, Raghu Kacker, and Lynne Rosenthal. Software testing by statistical methods:. National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6129.

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Nelson, David O. Statistical methods in physical mapping. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/95181.

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