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A New Conservative Approach for statistical data analysis in Surveying for Trace Elements in Solid Waste Ponds

APPLIED SCIENCES-BASEL 2025年第8期15卷 4246-4246页

作者： Timis, Andrei-Lucian Pencea, Ion Priceputu, Adrian Ungureanu, Constantin Karas, Zbynek Niculescu, Florentina Turcu, Ramona-Nicoleta Iacob, Gheorghe Marcu, Dragos Florin Macovei, Alexandru Constantin Natl Univ Sci & Technol POLITEHN Bucharest Mat Sci & Engn Fac Doctoral Sch 313 Splaiul IndependenteiJ Bldg RO-060042 Bucharest Romania Tech Univ Civil Engn Bucharest Dept Geotech & Fdn Engn RO-020396 Bucharest Romania Univ Bucharest Fac Geol & Geophys Dept Geol Engn RO-030018 Bucharest Romania DestroKladno Sro Inst Astron Sykorice 216 Zbecno 27024 Czech Republic Natl Univ Sci & Technol Politehn Bucharest Fac Mat Sci & Engn 313 Splaiul Independentei RO-060042 Bucharest Romania

Solid waste treatment and resourceization critically depend on waste characterization. Heavy metals and critical raw materials are found as trace elements in solid waste dumps, and their reliable quantification plays a critical role for decision risk regarding effective waste management. The reliable quantification of trace elements is a very challenging issue. Hence, in this study, a new conservative approach for data analysis in screening for trace elements in waste dumps is presented. We propose a theoretical model for statistical data interpretation to overcome the drawbacks of conventional approaches based on unproven hypotheses, such as binomial, Poisson, or Gaussian distributions of the particles carrying the analyte. Our model addresses concentration values close to the limit of quantification (LOQ) of an analytical method. This model fills the gap of data analysis when a set of analytical results are uniformly distributed. Our approach deals with results reported as being lower than the LOQ. The model was applied on XRFS results from studies carried out on tailings to emphasize the differences among classic, robust, and conservative data analyses. Classical analyses overestimate the concentration values and underestimate the associated uncertainties increasing the decision risk. This study demonstrates that a conservative approach is mandatory when screening for trace elements if the concentration values are uniformly distributed. The proposed model can be applied to any solid waste dump, regardless of the analytical method used for trace element screening.

关键词： trace elements critical raw materials heavy metals chemical analysis statistical data analysis uncertainty decision risk secondary resource solid waste surveying

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statistical data analysis for investigating Japanese government subsidy policy for private universities

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HIGHER EDUCATION 2008年第4期55卷 407-423页

作者： Sanusi, Anwar Oyama, Tatsuo Natl Grad Inst Policy Studies Minato Ku Tokyo 1068677 Japan

This paper aims at investigating Japanese government's subsidy policy for private universities by applying statistical approaches to various types of quantitative data. Firstly, we briefly describe the history of the government subsidy policy to identify the dominant factors behind it and also explain the structure and mechanism of this subsidy. Secondly, by using 2005 data, we try to find influential factors in allocating both general subsidies and special subsidies for private universities. Thirdly, using the data in two periods given as 1975-1979 and 2000-2004, we try to elucidate the structural properties of the subsidy policy by applying correlative rank analyses approaches in order to measure the "dominance power" of the top-ranking subsidy-recipient schools. The results show that the number of faculty members is the most influential for general subsidies, while the number of students is the most dominant variable for special subsidies.

关键词： correlative rank analysis dominance power government subsidy policy multivariate regression model private universities statistical data analysis

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Combining statistical data analysis techniques to extract topical keyword classes from corpora

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INTELLIGENT data analysis 2005年第1期9卷 105-127页

作者： Rossignol, Mathias Sebillot, Pascale Irisa F-35042 Rennes France

We present an unsupervised method for the generation from a textual corpus of sets of keywords, that is, words whose occurrences in a text are strongly connected with the presence of a given topic. Each of these classes is associated with one of the main topics of the corpus, and can be used to detect the presence of that topic in any of its paragraphs, by a simple keyword co-occurrence criterion. The classes are extracted from the textual data in a fully automatic way, without requiring any a priori linguistic knowledge or making any assumptions about the topics to search for. The algorithms we have developed allow us to yield satisfactory and directly usable results despite the amount of noise inherent in textual data. That goal is reached thanks to a combination of several data analysis techniques. On a corpus of archives from the French monthly newspaper Le Monde Diplomatique, we obtain 40 classes of about 30 words each that accurately characterize precise topics, and allow us to detect their occurrences with a precision and recall of 85% and 65% respectively.

关键词： topic detection topic characterization statistical data analysis unsupervised classification

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Nonparametric statistical data analysis of fingerprint minutiae exchange with two-finger fusion

Nonparametric statistical data analysis of fingerprint minut...

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Conference on Biometric Technology for Human Identification IV

作者： Wu, Jin Chu Garris, Michael D. NIST Image Grp Informat Access Div Informat Technol Lab Gaithersburg MD 20899 USA

ISBN: (纸本)9780819466617

A nonparametric inferential statistical data analysis is presented. The utility of this method is demonstrated through analyzing results from minutiae exchange with two-finger fusion. The analysis focused on high-accuracy vendors and two modes of matching standard fingerprint templates: 1) Native Matching - where the same vendor generates the templates and the matcher, and 2) Scenario 1 Interoperability - where vendor A's enrollment template is matched to vendor B's authentication template using vendor B's matcher. The purpose of this analysis is to make inferences about the underlying population from sample data, which provide insights at an aggregate level. This is very different from the data analysis presented in the MINEX04 report in which vendors are individually ranked and compared. Using the nonparametnic bootstrap bias-corrected and accelerated (BC,) method, 95% confidence intervals are computed for each mean error rate. Nonparametric significance tests are then applied to further determine if the difference between two underlying populations is real or by chance with a certain probability. Results from this method show that at a greater-than-95 % confidence level there is a significant degradation in accuracy of Scenario I Interoperability with respect to Native Matching. The difference of error rates can reach on average a two-fold increase in False Non-Match Rate. Additionally, it is proved why two-finger fusion using the sum rule is more accurate than single-finger matching under the same conditions. Results of a simulation are also presented to show the significance of the confidence intervals derived from the small size of samples, such as six error rates in some of our cases.

关键词： bootstrap fingerprint matching inferential interoperability minutiae exchange nonparametric significance test standard templates statistical data analysis

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Ten questions concerning statistical data analysis in human-centric buildings research: A focus on thermal comfort investigations

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BUILDING AND ENVIRONMENT 2024年 264卷

作者： Favero, Matteo Carlucci, Salvatore Chinazzo, Giorgia Moller, Jan Kloppenborg Schweiker, Marcel Vellei, Marika Sonta, Andrew Ecole Polytech Fed Lausanne EPFL Sch Architecture Civil & Environm Engn ENAC ETHOS Lab Lausanne Switzerland Univ Insubria Dept Theoret & Appl Sci Varese Italy Northwestern Univ Dept Civil & Environm Engn Evanston IL USA Tech Univ Denmark Dept Appl Math & Comp Sci DTU Lyngby Denmark Rhein Westfal TH Aachen Inst Occupat Social & Environm Med Med Fac Hlth Living Spaces lab Pauwelsstr 30 D-52074 Aachen Germany Univ Bordeaux CNRS Bordeaux INP UMR 5295I2M F-33400 Talence France Hesam Univ Arts & Metiers Inst Technol CNRS Bordeaux INPI2MUMR 5295 F-33400 Talence France

Given the large amount of time we spend indoors, designing and operating buildings that are safe, comfortable, and conducive to productivity and well-being is essential. To achieve this goal, in the past decades, research has been conducted to investigate the influence of the indoor environment on occupants. Thermal comfort has been the subject of most investigations in this field. However, despite being a consolidated research topic since the 1920s, statistical practices for analysing thermal comfort data often rely on simplified premises, which may be due to several possible factors (e.g., limited computational capabilities and lack of training). Consequently, important aspects of data analysis are often absent or overlooked. Recent statistics and statistical software advances have provided more options for effectively modelling complex issues. However, properly using these tools requires a solid understanding of statistical analysis, increasing the risk of misuse in practice. This paper presents ten questions highlighting the most critical issues regarding statistical analysis for thermal comfort research and practice. The first four questions provide general perspectives concerning statistical data analysis, while the remaining ones address specific problems related to thermal comfort research, but that can extend to all humancentric research in the built environment. Additionally, the last five questions demonstrate the practical significance of analysis pitfalls (i.e., sampling variability, selection bias, variable selection, clustered/nested observations, and measurement error) through examples with synthetic data. This study provides insights into the current statistical 'habits' in thermal comfort research and, more importantly, help researchers better define and conduct their statistical analyses.

关键词： Thermal comfort Human-centric research statistical data analysis Simulations Causal thinking statistical thinking

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Monitoring of the transmission line galloping with a novel distributed optical fibre sensor and its statistical data analysis

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IET Generation, Transmission & Distribution 2019年第1期14卷 166-171页

作者： Huijuan Wu Bo Tang Yao Xiao Haoyu Qiu Yi Zheng Yunjiang Rao Key Lab of Optical Fiber Sensing and Communication (Ministry of Education) School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu 611731 Sichuan Province People's Republic of China

The wind-induced galloping of the overhead transmission line threatens safety and serviceability of the power systems. In this study, a novel distributed optical fibre sensor system based on the polarisation-sensitive optical time-domain reflectometry technology is proposed, which can monitor the long-distance transmission line of over dozens of kilometres online in full scale by measuring its accompanying overhead ground wire grounding wire. Also it has good ability for surviving in harsh environments and easy maintenance. To meet the needs of long-distance monitoring, an equalisation processing method is used to reduce the influence of signal attenuation with the fibre length extension. In the continuous time–space statistics, adaptive galloping signal detection and the galloping level division of wind-induced disturbances are realised through the boxplot method; and in the continuous frequency–space statistics, the galloping frequency distribution along the cable is analysed simultaneously. Through the time–space and the frequency–space statistics upon the big data, the abnormal galloping moments and locations can be determined automatically in real time.

关键词： distributed sensors optical time-domain reflectometry statistical analysis overhead line mechanical characteristics fibre optic sensors condition monitoring transmission line galloping statistical data analysis wind-induced galloping overhead transmission line power systems polarisation-sensitive optical time-domain reflectometry technology long-distance transmission line harsh environments long-distance monitoring equalisation processing method signal attenuation fibre length extension adaptive galloping signal detection galloping level division wind-induced disturbances boxplot method galloping frequency distribution big data abnormal galloping moments overhead ground wire grounding wire continuous time-space statistics continuous frequency-space statistics distributed optical fibre sensor system cable

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An Illustrative Comparison of statistical data analysis and Inference

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Journal of Quality Technology 1975年第1期7卷 13-19页

This paper illustrates some of the basic ideas underlying statistical data analysis. The purpose is to compare these with the standard theoretical approach to statistical inference.

关键词： statistical data analysis statistical Inference Significance Testing Consonance Intervals Poisson Distribution

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statistical analysis and Interpolation of Compositional data in Materials Science

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ACS COMBINATORIAL SCIENCE 2015年第2期17卷 130-136页

作者： Pesenson, Misha Z. Suram, Santosh K. Gregoire, John M. CALTECH Joint Ctr Artificial Photosynth Pasadena CA 91125 USA

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material;that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

关键词： high-throughput screening electrocatalyst inkjet printing sputtering thin-films interpolation compositional data big data complex data statistical data analysis

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analysis on statistical data of Registered Constructor Test

Analysis on Statistical Data of Registered Constructor Test

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4th International Conference on Technology of Architecture and Structure (ICTAS 2011)

作者： Li, Qiang Li, Huimin Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Peoples R China

ISBN: (纸本)9783037852781

After statistical data of Registered Constructor Examination in 2009 have been gathered, the detailed analysis of examination was carried out. Distinction and scoring rate and these relationships of the examination paper researched, statistics result show that too easy and too difficult questions have very low discrimination. Based on statistical data, two prediction formulas on pass rate are presented. A coefficients was presented that could evaluate the overall quality of examination papers, which was used to evaluate the public examinations of Registered Constructor Examination 2009. Characteristics of high and low distinction questions are analyzed that has some significance to the proposition work of Registered Constructor Test.

关键词： Registered Constructor Test statistical data analysis Scoring Rate

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Using virtual experiments to improve data analysis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 046005-046005页

作者： Hughes, Finn Marschall, Manuel Stavridis, Manuel Elster, Clemens Phys Tech Bundesanstalt Math Modelling & Data Anal Abbestr 2-12 D-10587 Berlin Germany

In the data analysis of measurements, the simplified assumption of homoscedastic Gaussian noise is often made to account for the random fluctuations between observations. This may be an inadequate assumption which can deteriorate the results of a data analysis. Repeated measurements, which can be used to infer the true distribution of the data, may be inaccessible, making the true distribution hard to find. In such circumstances, thoroughly designed virtual experiments (VEs) can mimic real and possibly complex measurement processes to infer the true data distribution, which can subsequently be accounted for in an improved analysis of real observations. We explore the potential benefit of such an approach in terms of a metrological application, the tilted-wave interferometer (TWI). Our VE for the TWI yields not just the mean of the data, but also their physically modelled, random fluctuations arising in repeated observations. We use the virtual data to derive a statistical data model that includes correlations and heteroscedasticity. In applying a Bayesian data analysis procedure utilising said statistical model in conjunction with a vague prior for the quantity of interest, virtual data with a known ground truth are analysed and the quality of the resulting estimates are assessed. In addition, a comparison is carried out to the often-employed, simplified approach assuming homoscedastic, independent noise. We observe a significant improvement in the results when a more adequate statistical model for the data is utilised, along with a reliable uncertainty quantification. The work proposes the idea to extend the utilisation of a VE to inferring the noise characteristics of real observations, in turn leading to significantly improved data analysis procedures. The potential benefit is demonstrated to be substantial in terms of the considered metrological case study. Future research is discussed, including other ways that VEs could be used to further improve data analysis

关键词： virtual experiments Bayesian uncertainty evaluation statistical data analysis Monte Carlo

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