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Relevant bibliographies by topics / Outliers (Statistics)

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Academic literature on the topic 'Outliers (Statistics)'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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Journal articles on the topic "Outliers (Statistics)"

1

Liao, Xiou, Tongtong Wang, and Guohua Zou. "A Method for Detecting Outliers from the Gamma Distribution." Axioms 12, no. 2 (2023): 107. http://dx.doi.org/10.3390/axioms12020107.

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Outliers often occur during data collection, which could impact the result seriously and lead to a large inference error; therefore, it is important to detect outliers before data analysis. Gamma distribution is a popular distribution in statistics; this paper proposes a method for detecting multiple upper outliers from gamma (m,θ). For computing the critical value of the test statistic in our method, we derive the density function for the case of a single outlier and design two algorithms based on the Monte Carlo and the kernel density estimation for the case of multiple upper outliers. A sim

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2

Wada, Kazumi. "Outliers in official statistics." Japanese Journal of Statistics and Data Science 3, no. 2 (2020): 669–91. http://dx.doi.org/10.1007/s42081-020-00091-y.

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AbstractThe purpose of this manuscript is to provide a survey on the important methods addressing outliers while producing official statistics. Outliers are often unavoidable in survey statistics. They may reduce the information of survey datasets and distort estimation on each step of the survey statistics production process. This paper defines outliers to be focused on each production step and introduces practical methods to cope with them. The statistical production process is roughly divided into the following three steps. The first step is data cleaning, and outliers to be focused are tha

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3

Barnett, Vic. "Outliers and order statistics." Communications in Statistics - Theory and Methods 17, no. 7 (1988): 2109–18. http://dx.doi.org/10.1080/03610928808829736.

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GUO, JIANFENG. "A NOTE ON THE CONVENTIONAL OUTLIER DETECTION TEST PROCEDURES." Boletim de Ciências Geodésicas 21, no. 2 (2015): 433–40. http://dx.doi.org/10.1590/s1982-21702015000200024.

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Under the assumption of that the variance-covariance matrix is fully populated, Baarda's w-test is turn out to be completely different from the standardized least-squares residual. Unfortunately, this is not generally recognized. In the limiting case of only one degree of freedom, all the three types of test statistics, including Gaussian normal test, Student's t-test and Pope's Tau-test, will be invalid for identification of outliers: (1) all the squares of the Gaussian normal test statistic coincide with the goodness-of-fit (global) test statistic, even for correlated observations. Hence, th

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Ramlee, Intan Mastura, Safwati Ibrahim, Wai Zhe Leow, and Mohd Irwan Yusoff. "Detection of Outliers in Circular Regression Model Via DFBETAcIS Statistic." Sains Malaysiana 53, no. 4 (2024): 935–51. http://dx.doi.org/10.17576/jsm-2024-5304-16.

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The outlier issues in circular regression models have recently received much attention. The presence of outliers may cause the sign and magnitude of regression coefficients to vary, resulting in inaccurate model development and incorrect prediction. Many methods for detecting outliers in a circular regression model have been proposed in previous studies such as COVRATIO, D, M, A, and Chord statistics, but it is suspected that they are not very successful in the presence of multiple outliers in a data set since the masking and swamping is not considered in their studies. This study aimed to dev

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6

Chen, Ping, Ling Dong, Wanyi Chen, and Jin-Guan Lin. "Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/910828.

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This paper proposes several test statistics to detect additive or innovative outliers in adaptive functional-coefficient autoregressive (AFAR) models based on extreme value theory and likelihood ratio tests. All the test statistics follow a tractable asymptotic Gumbel distribution. Also, we propose an asymptotic critical value on a fixed significance level and obtain an asymptoticp-value for testing, which is used to detect outliers in time series. Simulation studies indicate that the extreme value method for detecting outliers in AFAR models is effective both for AO and IO, for a lone outlier

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Hossen, Md Bipul. "A new statistical approach for the identification of outlier genes in cancer microarray data." Asian Journal of Medical and Biological Research 6, no. 4 (2021): 795–801. http://dx.doi.org/10.3329/ajmbr.v6i4.51248.

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The aim of microarrays technology is to discover genes, which are differentially expressed as outliers between two or more groups of patients are an important task in the genomics community. The regular pattern of genes may often breakdown due to the presence of outliers and it is essential to detect those genes whose behavior looks abnormal in experimental and biological conditions. Several statistical techniques - t-statistic, cancer outlier profile analysis (COPA), outlier sums (OS), outlier robust t-statistic (ORT), maximum ordered subset t-statistics (MOST) and least sum of ordered subset

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Singh, Alok Kumar, Abhinav Singh, and Rohit Patawa. "Multiple Upper Outlier Detection Procedure in Generalized Exponential Sample." European Journal of Statistics 1, no. 1 (2021): 58–73. http://dx.doi.org/10.28924/ada/stat.1.58.

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Hawkins [6] defined an outlier as an observation that is significantly different from the remaining observations in a dataset so as to arouse suspicion that it was generated by different mechanism. Barnett and Lewis [2] defined an outlier as an observation that deviates significantly in the sample in which it occurs. Spatial outliers are different from outliers and many authors like Singh and Lalitha [9]. Outlier detection procedures for two parameter gamma distribution have been discussed by many authors. But one major disadvantage of the gamma distribution is that the distribution (or surviv

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Promtep, Kullaphat, Phontita Thiuthad, and Natchita Intaramo. "A Comparison of Efficiency of Test Statistics for Detecting Outliers in Normal Population." Sains Malaysiana 51, no. 11 (2021): 3829–41. http://dx.doi.org/10.17576/jsm-2022-5111-26.

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The objective of this research was to compare the efficiency among the test statistics which are used to detect outliers by testing hypothesis methods. The test statistics considered were Dixon’s test, Ferguson’s test, Grubbs’ test, Tw-test, and Tietjen-Moore’s test. The outliers were divided, by how far they are, into two groups: mild and extreme outliers. The efficiency of the test statistics was measured by the probability of type I error and the power of the test. The results showed that Tietjen-Moore’s test can control the probability of type I error according to Cochran and Bradley crite

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Fitrianto, Anwar, Wan Zuki Azman Wan Muhamad, Suliana Kriswan, and Budi Susetyo. "Comparing Outlier Detection Methods using Boxplot Generalized Extreme Studentized Deviate and Sequential Fences." Aceh International Journal of Science and Technology 11, no. 1 (2022): 38–45. http://dx.doi.org/10.13170/aijst.11.1.23809.

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Outliers identification is essential in data analysis since it can make wrong inferential statistics. This study aimed to compare the performance of Boxplot, Generalized Extreme Studentized Deviate (Generalized ESD), and Sequential Fences method in identifying outliers. A published dataset was used in the study. Based on preliminary outlier identification, the data did not contain outliers. Each outlier detection method's performance was evaluated by contaminating the original data with few outliers. The contaminations were conducted by replacing the two smallest and largest observations with

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Dissertations / Theses on the topic "Outliers (Statistics)"

1

Schall, Robert. "Outliers and influence under arbitrary variance." Doctoral thesis, University of Cape Town, 1986. http://hdl.handle.net/11427/21913.

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Using a geometric approach to best linear unbiased estimation in the general linear model, the additional sum of squares principle, used to generate decompositions, can be generalized allowing for an efficient treatment of augmented linear models. The notion of the admissibility of a new variable is useful in augmenting models. Best linear unbiased estimation and tests of hypotheses can be performed through transformations and reparametrizations of the general linear model. The theory of outliers and influential observations can be generalized so as to be applicable for the general univariate

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2

Fung, Wing-kam Tony. "Analysis of outliers using graphical and quasi-Bayesian methods /." [Hong Kong] : University of Hong Kong, 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1236146X.

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Mitchell, Napoleon. "Outliers and Regression Models." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc279029/.

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The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.

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Soon, Shih Chung. "On detection of extreme data points in cluster analysis." Connect to resource, 1987. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262886219.

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馮榮錦 and Wing-kam Tony Fung. "Analysis of outliers using graphical and quasi-Bayesian methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31230842.

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Zaharim, Azami. "Outliers and change points in time series data." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295109.

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Katshunga, Dominique. "Identifying outliers and influential observations in general linear regression models." Thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/6772.

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Includes bibliographical references (leaves 140-149).<br>Identifying outliers and/or influential observations is a fundamental step in any statistical analysis, since their presence is likely to lead to erroneous results. Numerous measures have been proposed for detecting outliers and assessing the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In what follo

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Brännström, Anton. "En jämförelse mellan likelihoodkvottestet och test baserade på spacings vid förekomst av outliers." Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-137280.

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Denna studie jämför styrkefunktionerna mellan det vedertagna likelihoodkvottestet (𝐿𝑇) och test baserade på spacings kallade fidelitytest (𝐹𝑇), under nollhypotesen 𝐻0:𝜃=𝜃0 och den alternativa hypotesen 𝐻𝐴:𝜃≠𝜃0. Tidigare studier har visat att 𝐹𝑇 presterar jämförbara resultat med 𝐿𝑇 under förutsättningar där modellantagandena håller optimalt och där båda testen är applicerbara. FT kan dessutom användas för vissa fall där LT inte fungerar. Syftet med denna uppsats är att med hjälp av simulerade data, undersöka hypotestestens prestation under förhållanden då modellantagandena inte är helt uppfylld

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Liu, Jie. "Exploring Ways of Identifying Outliers in Spatial Point Patterns." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2528.

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This work discusses alternative methods to detect outliers in spatial point patterns. Outliers are defined based on location only and also with respect to associated variables. Throughout the thesis we discuss five case studies, three of them come from experiments with spiders and bees, and the other two are data from earthquakes in a certain region. One of the main conclusions is that when detecting outliers from the point of view of location we need to take into consideration both the degree of clustering of the events and the context of the study. When detecting outliers from the point of v

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Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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Books on the topic "Outliers (Statistics)"

1

Toby, Lewis, ed. Outliers in statistical data. 3rd ed. Wiley, 1994.

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2

Guttman, Irwin. Spuriosity and outliers in circular data. University of Toronto, Dept. of Statistics, 1988.

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3

Daniel Peña Sánchez de Rivera. Comparing probabilistic methods for outlier detection. University of Toronto, Dept. of Statistics, 1992.

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4

Rumburg, Scot. Characteristics of directly expanded hog data outliers. Research and Applications Division, National Agricultural Statistics Service, U.S. Department of Agriculture, 1992.

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Guttman, Irwin. A model for estimating the number of aberrant laboratories /by Irwin Guttman and Ingram Olkin. University of Toronto, Dept. of Statistics, 1991.

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Boeckh, Friedrich. Systematischer Vergleich verschiedener Influenzmasse unter besonderer Berücksichtigung von Verdeckungseffekten. Verlag für Wissenschaft und Forschung, 1994.

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Rousseeuw, Peter J. Robust regression and outlier detection. Wiley, 1987.

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8

Meier, Alan. An analysis of outliers in the RSDP. Applied Science Division, Lawrence Berkeley Laboratory, University of California, 1988.

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9

Kwaśniak, Mieczysław. Badanie wpływu niezawodności wewnętrznej sieci geodezyjnej na efektywność wybranych podejść do wykrywania błędow grubych: Research on the influence of internal reliability of geodetic network on the effectiveness of selected approaches to gross errors detection. Oficyna Wydawnicza Politechniki Warszawskiej, 2012.

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Lopuhaä, Hendrik Paul. Estimation of location and covariance with high breakdown point. Technische Universiteit Delft, 1990.

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Book chapters on the topic "Outliers (Statistics)"

1

Krasker, William S. "Outliers." In Time Series and Statistics. Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_25.

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2

Madansky, Albert. "Identification of Outliers." In Springer Texts in Statistics. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3794-5_5.

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Sen, Ashish, and Muni Srivastava. "Outliers and Influential Observations." In Springer Texts in Statistics. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-4470-7_8.

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Sen, Ashish, and Muni Srivastava. "Outliers and Influential Observations." In Springer Texts in Statistics. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-25092-1_8.

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5

Atkinson, A. C., Siem Jan Koopman, and Neil Shephard. "Outliers and Switches in Time Series." In Asymptotic Statistics. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57984-4_3.

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De Muth, James E. "Tests to Evaluate Potential Outliers." In Practical Statistics for Pharmaceutical Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33989-0_8.

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Cléroux, Robert, Jean-Marie Helbling, and Normand Ranger. "DOMOUSE: Detection of Multivariate Outliers Using S Environment." In Computational Statistics. Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_11.

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Portnoy, Stephen. "Regression Quantile Diagnostics for Multiple Outliers." In Directions in Robust Statistics and Diagnostics. Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-4444-8_8.

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Peña, Daniel. "Outliers, Influential Observations, and Missing Data." In Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118032978.ch6.

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Vieira, Edward T. "Numeric Variables: Data Screening and Removing Outliers." In Introduction to Real World Statistics. Routledge, 2017. http://dx.doi.org/10.4324/9781315233024-12.

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Conference papers on the topic "Outliers (Statistics)"

1

Hadi, A. F., D. Anggraeni, H. Sadiyah, and D. B. C. Wicaksono. "Multivariate outliers detection on GGE Biplot." In 2021 ASIA-PACIFIC CONFERENCE ON APPLIED MATHEMATICS AND STATISTICS. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0083440.

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Martin, R. Douglas. "Data mining for outliers with robust statistics." In Tutorial notes of the seventh ACM SIGKDD international conference. ACM Press, 2001. http://dx.doi.org/10.1145/502786.502789.

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Mokhtar, Nurkhairany Amyra, Yong Zulina Zubairi, and Abdul Ghapor Hussin. "Detecting multiple outliers in linear functional relationship model for circular variables using clustering technique." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982835.

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Di, Nur Faraidah Muhammad, and Siti Zanariah Satari. "The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982854.

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Zahari, Siti Meriam, Norazan Mohamed Ramli, Balkiah Moktar, and Mohammad Said Zainol. "The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers." In STATISTICS AND OPERATIONAL RESEARCH INTERNATIONAL CONFERENCE (SORIC 2013). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4894363.

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Mahmood, Ehab A., Sohel Rana, Abdul Ghapor Hussin, and Habshah Midi. "Detection of outliers in the response and explanatory variables of the simple circular regression model." In INNOVATIONS THROUGH MATHEMATICAL AND STATISTICAL RESEARCH: Proceedings of the 2nd International Conference on Mathematical Sciences and Statistics (ICMSS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4952561.

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Amaratunga, Dhammika. "Reference Ranges and Control Limits that are Resistant to Baseline Outliers." In SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES [SICASH]. Faculty of Humanities and Sciences, SLIIT, 2022. http://dx.doi.org/10.54389/kfhf7274.

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Reference ranges and control limits are used in many settings – for example, to assess a person’s health or to monitor the stability of a manufacturing process. Such ranges are established based on a baseline sample of what is considered normal data, but it is not possible to always avoid a few outliers being present even in this sample. If, as is common, the range is calculated using statistics, such as the mean and standard deviation, which could be influenced by outliers, then the use of such a range could adversely affect the decisions made. This can be avoided by constructing the referenc

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Yang, Tao, and Xiaoping Shi. "Detection of outliers in air pollution from Shijiazhuang using functional data analysis." In 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), edited by Paulo Batista and Yudong Zhang. SPIE, 2023. http://dx.doi.org/10.1117/12.2692369.

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Hamid, Hashibah binti. "A framework of mixed variables classification in the presence of outliers: A robust location model." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012238.

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Kabakci, Fikriye, and Umashanger Thayasivam. "Assessing Robust Clustering for Gaussian Mixtures in the Presence of Outliers and Noise Variables with Component Overlap-A Simulation Study." In 5th Annual International Conference on Operations Research and Statistics (ORS 2017). Global Science & Technology Forum (GSTF), 2017. http://dx.doi.org/10.5176/2251-1938_ors17.23.

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Reports on the topic "Outliers (Statistics)"

1

Lucon, Enrico. Statistical Detection of Outliers in the Certification of NIST Reference Charpy Lots. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ir.8526.

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Alwin, Jennifer Louise, Forest B. Brown, and Michael Evan Rising. Excluding Benchmark Statistical Outliers in Nuclear Criticality Safety Validation: A Comparison Study of Upper Subcritical Limits for Plutonium Systems using Whisper-1.1. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1467235.

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Hofer, Martin, Tomas Sako, Arturo Martinez Jr., et al. Applying Artificial Intelligence on Satellite Imagery to Compile Granular Poverty Statistics. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200432-2.

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This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates, citing data from the Philippines and Thailand. Computer vision techniques were applied on publicly available medium resolution satellite imagery, household surveys, and census data from the two countries. The results suggest that even using publicly accessible satellite imagery, predictions generally aligned with the distributional structure of government-published poverty estimates after calibration. The study further examines the robustness of the resulting estimates to

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Desk, Front. Gender Profiles by Country. Commonwealth of Learning (COL), 2015. http://dx.doi.org/10.56059/11599/1717.

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The Gender Country Profile outlines a country’s available data and rankings regarding gender equality. The profile is first a summary of the country’s demographic overview and rankings within established global indices for sustainable development and gender equity. The profile then follows the World Economic Forum Global Gender Gap Index framework to provide statistics on each country in detail.

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Abizanda, Beatriz. Citizen Security. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0006928.

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This is a presentation for the Caribbean Regional ConSoc Retreat held on June 16, 2011 in Kingston, Jamaica. This document outlines crime and violence as major roadblocks to development in the Caribbean; citing statistics and providing examples of the economic costs to the region. This presentation then goes on to describe the IDB's strategy with regard to citizen security and highlights IDB Funded security programs in the region. The presentation also identifies Civil Society as a potentially relevant stakeholder.

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Breewood, Helen, and Tara Garnett. What is feed-food competition? Edited by Walter Fraanje. Food Climate Research Network, 2020. http://dx.doi.org/10.56661/dde79ca0.

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This building block explores key statistics about competing uses for food system resources, focusing on the use of land, crops and wild fish for feeding humans or feeding livestock – a trade-off known as feed-food competition. It also outlines different ways in which people interpret these figures and sets out how these differing perspectives link to broader debates about what we should eat and how we should produce food, particularly concerning what role (if any) livestock should play in the global food system.

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Holmes, Wendy. Projecting the Need for and Cost of Long-Term Care for Older Persons. Asian Development Bank, 2021. http://dx.doi.org/10.22617/wps210072-2.

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With the numbers of older persons in the Asia and Pacific region increasing rapidly, there is a growing urgency to develop and strengthen long-term care (LTC) support systems and services. This working paper aims to enhance the understanding, particularly of policy makers and those planning modeling studies, of using statistical models to project the need, demand, and cost of LTC services. The paper first outlines modeling and its importance. Second, it describes key concepts related to LTC need, demand, and measurement. Then, it identifies key issues to consider when designing and using model

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Sultan, Maheen, and Pragyna Mahpara. Countering Backlash in the Implementation of Bangladesh's Domestic Violence Act. Countering Backlash and Institute of Development Studies, 2023. http://dx.doi.org/10.19088/backlash.2023.004.

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Domestic violence rates are high in Bangladesh in spite of laws such as the Nari o Shishu Nirjatan Domon Ain and the DVPPA 2010. According to data from the Bangladesh Bureau of Statistics, almost three in every five women (57.7%) have experienced some form of physical, sexual, or emotional violence in their lifetime. While the formulation and enactment of the DVPPA in 2010 was an achievement for the government, especially the Ministry of Women and Children Affairs that proposed it and the coalition for domestic violence known as the Citizens Initiative against Domestic Violence (CIDV) that cha

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Oyekan, Khalimath, Ayodotun Ayorinde, and Oreoluwa Adenuga. The Problem of Out-of-School Children in Nigeria. Research on Improving Systems of Education (RISE), 2023. http://dx.doi.org/10.35489/bsg-rise-ri_2023/058.

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In 2015, all United Nations Member States adopted the 2030 Agenda for Sustainable Development which outlines a blueprint to address global challenges across a broad range of themes including poverty, health, education, inequality, climate change, environmental degradation, peace and justice. The Goal 4 of the 2030 Agenda for Sustainable Development, otherwise referred to as the United Nations Sustainable Development Goal 4, seeks to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Unfortunately, an approximated 263 million children remain ou

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Manitoba Indigenous Tuberculosis History Project (MITHP). Missing Patients Research Guide. Manitoba Indigenous Tuberculosis History Project (MITHP), Department of History, University of Winnipeg, 2024. http://dx.doi.org/10.36939/ir.202402141551.

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This Missing Patients Research Guide contains directions for finding out more about Indigenous patients who entered tuberculosis (TB) sanatoriums and hospitals in Manitoba and never returned home. Part One of the guide presents helpful start-up information. First it explains how to gather useful details including names, dates, and locations that will help in the search as well as how to move forward with your research. Then it outlines three useful “Research Tips”: all of the various names of TB treatment hospitals in Manitoba commonly attended by Indigenous patients; instructions for undertak

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