Статті в журналах: "Statistical association"

1

Borgan, Ørnulf, and Marie Lilleborge. "The Norwegian Statistical Association." EMS Newsletter 2019-6, no. 112 (June 6, 2019): 39–40. http://dx.doi.org/10.4171/news/112/10.

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2

de Macedo, Carmen. "Guilt by Statistical Association." Journal of Philosophy 105, no. 6 (2008): 320–32. http://dx.doi.org/10.5840/jphil2008105640.

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3

Hennekens, Charles H. "Statistical Association and Causation." JAMA 305, no. 11 (March 16, 2011): 1134. http://dx.doi.org/10.1001/jama.2011.322.

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4

Qu, Hui-Qi, Matthew Tien, and Constantin Polychronakos. "Statistical significance in genetic association studies." Clinical & Investigative Medicine 33, no. 5 (October 1, 2010): 266. http://dx.doi.org/10.25011/cim.v33i5.14351.

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Clinical & Investigative Medicine (CIM) is receiving an increasing number of reports of candidate-based association studies. The track record of such studies in the past has been poor: numerous genetic associations reported from candidate gene studies have not been replicated in later studies. The rise of the genome-wide association study (GWAS) is changing this situation. A well-designed GWAS may identify a number of candidate loci without bias by screening the whole human genome. Validating and fine-mapping the candidate loci from GWAS are required to clarify the genetic mechanisms. Thus, a candidate-based association study has become a well-directed effort, instead of searching for a needle in a haystack. In the post-GWAS era, exponential growth of candidate-based genetic association studies is expected. A pressing issue accompanying this new trend is the assessment of the validity of an association study. In this editorial, we illustrate the major cause of false positive association from random sampling bias by an empirical example, and emphasize the application of the probability theory in assessing the validity of a genetic association study.

5

Schröer, Wolffram, and Volker C. Weiss. "Molecular association in statistical thermodynamics." Journal of Molecular Liquids 205 (May 2015): 22–30. http://dx.doi.org/10.1016/j.molliq.2014.08.013.

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6

Sapra, RameshLal, and Satish Saluja. "Understanding statistical association and correlation." Current Medicine Research and Practice 11, no. 1 (2021): 31. http://dx.doi.org/10.4103/cmrp.cmrp_62_20.

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7

Gilchrist, Martin. "Springer and the American Statistical Association." CHANCE 5, no. 1-2 (January 1992): 4. http://dx.doi.org/10.1080/09332480.1992.11882453.

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8

Banerjee, P. K. "Fifty Years of Calcutta Statistical Association." Calcutta Statistical Association Bulletin 45, no. 1-2 (March 1995): 1–24. http://dx.doi.org/10.1177/0008068319950101.

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9

Bruzzese, Dario, and Cristina Davino. "Statistical Pruning of Discovered Association Rules." Computational Statistics 16, no. 3 (September 2001): 387–98. http://dx.doi.org/10.1007/s001800100074.

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10

Weiß, Christian H. "Statistical mining of interesting association rules." Statistics and Computing 18, no. 2 (December 21, 2007): 185–94. http://dx.doi.org/10.1007/s11222-007-9047-6.

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11

Fildes, Robert. "Journal of the American Statistical Association." International Journal of Forecasting 5, no. 2 (January 1989): 294. http://dx.doi.org/10.1016/0169-2070(89)90106-4.

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12

Fildes, Robert. "Journal of the American Statistical Association." International Journal of Forecasting 5, no. 2 (January 1989): 294–95. http://dx.doi.org/10.1016/0169-2070(89)90107-6.

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13

Koehler, Anne B. "Journal of the American Statistical Association." International Journal of Forecasting 11, no. 2 (June 1995): 353–54. http://dx.doi.org/10.1016/0169-2070(95)90067-5.

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14

Mason, R. L., J. D. McKenzie, and R. K. Spoeri. "The Chapters of the American Statistical Association." American Statistician 44, no. 2 (May 1990): 103. http://dx.doi.org/10.2307/2684141.

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15

Fukuda, Katsuhirci, Kazuaki Sugawa, Akemi Wakisaka, Junko Mokiuchi, Nobuo Matsuura, and Yoshiharu Sato. "Statistical detection of HLA and disease association." Tissue Antigens 26, no. 2 (December 11, 2008): 81–86. http://dx.doi.org/10.1111/j.1399-0039.1985.tb00939.x.

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16

Balding, D. "Statistical Approaches for Genome-Wide Association Studies." European Journal of Cancer Supplements 6, no. 9 (July 2008): 187. http://dx.doi.org/10.1016/s1359-6349(08)71841-x.

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17

Wang, Maggie Haitian, Heather J. Cordell, and Kristel Van Steen. "Statistical methods for genome-wide association studies." Seminars in Cancer Biology 55 (April 2019): 53–60. http://dx.doi.org/10.1016/j.semcancer.2018.04.008.

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18

Walker, Helen M. "The Role of the American Statistical Association." American Statistician 59, no. 1 (February 2005): 68–71. http://dx.doi.org/10.1198/000313005x20709.

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19

Stephens, Matthew, and David J. Balding. "Bayesian statistical methods for genetic association studies." Nature Reviews Genetics 10, no. 10 (October 2009): 681–90. http://dx.doi.org/10.1038/nrg2615.

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20

Sun, Ning, and Hongyu Zhao. "Statistical Methods in Genome-Wide Association Studies." Annual Review of Biomedical Data Science 3, no. 1 (July 20, 2020): 265–88. http://dx.doi.org/10.1146/annurev-biodatasci-030320-041026.

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Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

21

Evans, Walker S., Andreas Plesch, John H. Shaw, Natesh L. Pillai, Ellen Yu, Men-Andrin Meier, and Egill Hauksson. "A Statistical Method for Associating Earthquakes with Their Source Faults in Southern California." Bulletin of the Seismological Society of America 110, no. 1 (January 14, 2020): 213–25. http://dx.doi.org/10.1785/0120190115.

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ABSTRACT We present a new statistical method for associating earthquakes with their source faults in the Southern California Earthquake Center’s 3D Community Fault Models (CFMs; Plesch et al., 2007) in near-real time and for historical earthquakes. The method uses the hypocenter location, focal mechanism orientation, and earthquake sequencing to produce the probabilities of association between a given earthquake and each fault in the CFM as well as the probability that the event occurred on a fault not represented in the CFM. We used a set of known likely associations (the Known Likely Sets) as training or testing data and demonstrated that our models perform effectively on these examples and should be expected to perform well on other earthquakes with similar characteristics including the full catalog of southern California earthquakes (Hauksson et al., 2012). To produce near-real-time associations for future earthquakes, the models have been implemented as an R script and connected to the Southern California Seismic Network data processing system operated by the California Institute of Technology and the U.S. Geological Survey to automatically produce fault associations for earthquakes of M≥3.0 as they occur. To produce historical associations, we apply the method to the most recent CFM version (v.5.2), yielding modeled historical associations for all events of M≥3.0 in the catalog of southern California earthquakes from 1981 to 2016. More than 80% of these events and 99% of moment within the geography covered by the CFM had a primary association with a CFM fault. The models can help identify clusters of small earthquakes that indicate the onset of activity associated with major faults. The method will also assist in communicating objective information about the faults that source earthquakes to the scientific community and general public. In the event of a damaging southern California earthquake, the near-real-time association will provide valuable information regarding the similarity of the current event to forecast scenarios, potentially aiding in earthquake response.

22

Boisvert, Jean. "Conceptualisation and Modelling of the Process behind Brand Association Transfer." International Journal of Market Research 53, no. 4 (July 2011): 541–56. http://dx.doi.org/10.2501/ijmr-53-4-541-556.

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Although the concept of affect transfer has been addressed by many in the literature, the process underlying the transfer of brand associations from parent brands to their extensions is still unclear despite important theoretical and managerial implications. This paper proposes to conceptualise and model the empirical process underlying such transfer. The findings reveal that the capability of a parent brand to transfer specific brand associations to a line extension depends on an optimisation process where strong transfer occurs only when repeated measures of the same associations are not statistically distinct. Conversely, the transfer is limited when the statistical difference is either positive or negative in repeated measures. When the difference is positive, the extension appears to already ‘own’ the association in comparison to the parent brand and when negative the association is not compatible with the extension. The methodological and managerial implications of brand association transfer are discussed.

23

Holmes, Peter. "Review of International Association for Statistical Education Proceedings." American Statistician 49, no. 3 (August 1995): 276. http://dx.doi.org/10.2307/2684200.

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24

Leermakers, Frans A. M., and Jan M. H. M. Scheutjens. "Statistical thermodynamics of association colloids. 2. Lipid vesicles." Journal of Physical Chemistry 93, no. 21 (October 1989): 7417–26. http://dx.doi.org/10.1021/j100358a033.

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25

Amaddeo, Francesco, and Michele Tansella. "Urbanicity and schizophrenia. From statistical association to causality?" Epidemiology and Psychiatric Sciences 15, no. 4 (December 2006): 239–41. http://dx.doi.org/10.1017/s1121189x00002098.

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26

Kumar, Ashish, Mayank Saini, and Aditi Sharan. "Aspect category detection using statistical and semantic association." Computational Intelligence 36, no. 3 (May 3, 2020): 1161–82. http://dx.doi.org/10.1111/coin.12327.

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27

Leung, Yee, Chang-Lin Mei, and Wen-Xiu Zhang. "Statistical Test for Local Patterns of Spatial Association." Environment and Planning A: Economy and Space 35, no. 4 (April 2003): 725–44. http://dx.doi.org/10.1068/a3550.

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In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis G statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing this kind of analysis, and a randomized permutation approach and normal approximation are commonly used to derive the p-values of the statistics. To circumvent some of the shortcomings of these existing methods and to offer a more formal approach in line with classical statistical framework, we develop in this paper an exact method for computing the p-values of the local Moran's Ii, local Geary's ci, and the modified Ord and Getis G statistics based on the distributional theory of quadratic forms in normal variables. Furthermore, an approximate method, called three-moment χ2 approximation, with explicit calculation formulae is also proposed to achieve a computational cost lower than the exact method. Numerical evaluation on the accuracy of the approximate null distributions of the local statistics demonstrates that the proposed three-moment χ2 method is useful in some situations although it is inappropriate for approximating the null distribution of Ii. The study not only provides an exact test for local patterns of spatial association, but also put the tests of several local statistics within a unified statistical framework.

28

Foster, James E., Mark McGillivray, and Suman Seth. "Composite Indices: Rank Robustness, Statistical Association, and Redundancy." Econometric Reviews 32, no. 1 (January 2013): 35–56. http://dx.doi.org/10.1080/07474938.2012.690647.

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29

Mengersen, K., S. A. Moynihan, and R. L. Tweedie. "Causality and Association: The Statistical and Legal Approaches." Statistical Science 22, no. 2 (May 2007): 227–54. http://dx.doi.org/10.1214/07-sts234.

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30

Rodrigues-Williams, Liliya L., and Craig J. Hogan. "Statistical association of QSO's with foreground galaxy clusters." Astronomical Journal 107 (February 1994): 451. http://dx.doi.org/10.1086/116866.

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31

Schoenlein, Melissa A., and Karen B. Schloss. "Building color-concept association distributions from statistical learning." Journal of Vision 19, no. 10 (September 6, 2019): 299a. http://dx.doi.org/10.1167/19.10.299a.

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32

Ghosh, Subir. "The Measurement of Association: A Permutation Statistical Approach." Technometrics 62, no. 3 (July 2, 2020): 416. http://dx.doi.org/10.1080/00401706.2020.1783948.

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33

Xue, Haoran, and Wei Pan. "Some statistical consideration in transcriptome‐wide association studies." Genetic Epidemiology 44, no. 3 (December 10, 2019): 221–32. http://dx.doi.org/10.1002/gepi.22274.

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34

Barra, Mathias, Fredrik A. Dahl, and Kjersti G. Vetvik. "Statistical Testing of Association Between Menstruation and Migraine." Headache: The Journal of Head and Face Pain 55, no. 2 (October 16, 2014): 229–40. http://dx.doi.org/10.1111/head.12457.

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35

Manchin, S., D. Kravitz, and C. Baker. "Visual statistical learning: Spatial configuration or abstract association?" Journal of Vision 8, no. 6 (March 27, 2010): 656. http://dx.doi.org/10.1167/8.6.656.

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36

Breen, Richard. "Statistical Models of Association for Comparing Cross-Classifications." Sociological Methods & Research 36, no. 4 (May 2008): 442–61. http://dx.doi.org/10.1177/0049124107313856.

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37

Najafi, Amir, Sepehr Janghorbani, Seyed Abolfazl Motahari, and Emad Fatemizadeh. "Statistical Association Mapping of Population-Structured Genetic Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 2 (March 1, 2019): 638–49. http://dx.doi.org/10.1109/tcbb.2017.2786239.

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38

Banerjee, P. K. "An Epilogue—H.K. Nandi and Calcutta Statistical Association." Calcutta Statistical Association Bulletin 40, no. 1-4 (January 1990): 343–46. http://dx.doi.org/10.1177/0008068319900526.

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39

SASTRI, T., and C. O. MALAVE. "STATISTICAL ASSOCIATION LEARNING OF THE MARKOV DECISION PROCESS." IIE Transactions 25, no. 3 (May 1993): 86–98. http://dx.doi.org/10.1080/07408179308964294.

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40

Gibson, Greg. "Statistical Genetics: Gene Mapping Through Linkage and Association." Genes, Brain and Behavior 8, no. 1 (February 2009): 127–28. http://dx.doi.org/10.1111/j.1601-183x.2008.00457.x.

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41

Coleman, S. "The association between tuberculosis and diphtheria." Epidemiology and Infection 146, no. 8 (April 18, 2018): 940–45. http://dx.doi.org/10.1017/s0950268818000936.

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AbstractThis research investigates the long-forgotten relationship between diphtheria and tuberculosis. Historical medical reports from the late 19th century are reviewed followed by a statistical regression analysis of the relationship between the two diseases in the early 20th century. Historical medical reports show a consistent association between diphtheria and tuberculosis that can increase the likelihood and severity of either disease in a co-infection. The statistical analysis uses historical weekly public health data on reported cases in five American cities over a period of several years, finding a modest but statistically significant relationship between the two diseases. No current medical theory explains the association between diphtheria and tuberculosis. Alternative explanations are explored with a focus on how the diseases assimilate iron. In a co-infection, the effectiveness of tuberculosis at assimilating extracellular iron may lead to increased production of diphtheria toxin, worsening that disease, which may, in turn, exacerbate tuberculosis. Iron-dependent repressor genes connect both diseases.

42

Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." International Journal of Agricultural and Environmental Information Systems 6, no. 2 (April 2015): 40–76. http://dx.doi.org/10.4018/ijaeis.2015040104.

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The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.

43

Höfler, Michael, Tanja Brückl, Antje Bittner, and Roselind Lieb. "Visualizing Multivariate Dependencies with Association Chain Graphs." Methodology 3, no. 1 (January 2007): 24–34. http://dx.doi.org/10.1027/1614-2241.3.1.24.

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In a recent paper, a new type of graph to visualize the results from graphical models was proposed. Association chain graphs (ACGs) provide a richer visualization than conventional graphs (directed acyclic and recursive regression graphs) if the data can be described with only a small number of parameters. ACGs display not only which associations reach statistical significance, but also the magnitude of associations (confidence intervals for statistical main effects) as the contrast color to the background color of the graph. In this paper, the ACG visualization is extended especially for the case where all variables are binary by illustrating their relative frequencies. This shows the degrees of associations not only on the individual (as expressed by odds ratios or other indexes of association) but also on the community level. We applied the approach to an extensive example of birth and childhood factors for the onset of affective mental disorders using data from the EDSP (Early Developmental Stages of Psychopathology) Study.

44

Loyola-Rodriguez, Juan Pablo, Carlos Villa-Chavez, Nuria Patiño-Marin, Celia Aradillas-Garcia, Cesar Gonzalez, and Esperanza de la Cruz-Mendoza. "Association between Caries, Obesity and Insulin Resistance in Mexican Adolescents." Journal of Clinical Pediatric Dentistry 36, no. 1 (September 1, 2011): 49–54. http://dx.doi.org/10.17796/jcpd.36.1.e25411r576362262.

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Aim: To determine the association among dental caries, obesity and insulin resistance in Mexican adolescents.Methods: Body Mass Index, obesity (OB) blood pressure, insulin level, insulin resistance (IR), triglycerides level, serum HDL-cholesterol (cHDL), DMFT index and salivary flow were measured. Results:Anthropometric measures showed a significant statistical difference (p < 0.05). Insulin level was 8.98 for healthy subjects, whereas for OB-IR group was 25.35, there was a statistical significant difference (p <0.05). Triglycerides level was 88.50 for healthy subjects and 169.40 mg/dL for OB-IR; cHDL was 52.88 for healthy and 41.82 mg/dL for OB-IR group, both showed a statistically significant difference (p < 0.05). Salivary flow was 4.30 for healthy and for OB-IR group was 5.48 ml/min showed a significant statistical difference(p < 0.05). DMFT index was 3.02 for healthy and for OB-IR adolescents was 4.78, showed a significant statistical difference (p < 0.05). The caries component of DMFT index was 1.84 for healthy and was 3.52 for OB-IR adolescents, showed a significant statistical difference (p < 0.05). According to the multivariate analysis, DMFT (OR=3.10; IC95%=0.20-1.02, p=0.042) and decay (OR=3.30; IC95%=0.19-1.0,p=0.011) were associated with subjects with OB-IR. Conlusion: OB-IR Mexican adolescents showed a positive association with DMFT.

45

Grabusts, Pēteris. "APPLICATION POSSIBILITIES OF ASSOCIATION RULES IN STATISTICAL DATA ANALYSIS." Latgale National Economy Research 1, no. 6 (October 21, 2014): 77. http://dx.doi.org/10.17770/lner2014vol1.6.1168.

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This paper studies one of intelligent data processing methods: using association rules for data analysis. The method of association rule obtaining what was initially developed to analyse consumer’s basket has turned to be a good tool for other tasks too. The method helps search and find regularities of the form X  Y in different kinds of data. Nowadays this method is widely applied in the tasks of large scale database processing and analysing. As a result, methods of association rule construction occupy their place among the basic methods of intelligent data processing. The paper consists of two parts: theoretical and experimental. The theoretical part examines the mathematical aspects of association rule construction in detail and describes basic concepts and algorithm application possibilities. The experimental part presents implementation results and analysis of experiments. Conclusions have been drawn concerning the efficiency of association rules’ application in search of regularities. Even though the association rules mining method is among the fundamental data processing methods, in Latvia this method is not widely used, therefore, the article under consideration reveals the potential possibilities of the association rule mining in the analysis of statistical data.

46

Phan, Nghia Quoc, Phuong Hoai Dang, and Hiep Xuan Huynh. "COLLABORATIVE RECOMMENDERATION BASED ON STATISTICAL IMPLICATION RULES." Journal of Computer Science and Cybernetics 33, no. 3 (March 20, 2018): 247–62. http://dx.doi.org/10.15625/1813-9663/33/3/10683.

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In recent research, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. However, these approaches also reveal some disadvantages of the system, such as taking a longer time for generating association rules; applying the Apriori algorithm on rating sparse matrix resulting in irrelevant information and causing poor recommendation results to target users and association rules generated primarily relying on given threshold of Support and Confidence measures leading to the focus on the majority of rules and ignoring the astonishment of rules to affect the recommendation results. In this study, we propose a new model for collaborative filtering recommender systems: The collaborative recommendation is based on statistical implication rules (IIR); Different from collaborative recommendation based on association rules (AR), the IIR predicts the items for users based on statistical implication rules generated from rating matrix and Implication intensity measures measuring the surprisingness of rules. To evaluate the effectiveness of the model, the experimental section is implemented on three real datasets to compare the results with some different effective models. The results show that the IIR has higher precision on the experimental datasets.

47

Deguen, Séverine, Wahida Kihal-Talantikite, Morgane Gilles, Arlette Danzon, Marion Carayol, and Denis Zmirou-Navier. "Are the effects of air pollution on birth weight modified by infant sex and neighborhood socioeconomic deprivation? A multilevel analysis in Paris (France)." PLOS ONE 16, no. 4 (April 15, 2021): e0247699. http://dx.doi.org/10.1371/journal.pone.0247699.

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Adverse birth outcomes related to air pollution are well documented; however, few studies have accounted for infant sex. There is also scientific evidence that the neighborhood socioeconomic profile may modify this association even after adjusting for individual socioeconomic characteristics. The objective is to analyze the association between air pollution and birth weight by infant sex and neighborhood socioeconomic index. All birth weights (2008–2011) were geocoded at census block level. Each census block was assigned a socioeconomic deprivation level, as well as daily NO2 and PM10 concentrations. We performed a multilevel model with a multiple statistical test and sensible analysis using the spline function. Our findings suggest the existence of a differential association between air pollution and BW according to both neighborhood socioeconomic level and infant sex. However, due to multiple statistical tests and controlling the false discovery rate (FDR), all significant associations became either not statistically significant or borderline. Our findings reinforce the need for additional studies to investigate the role of the neighborhood socioeconomic which could differentially modify the air pollution effect.

48

PONOMARENKO, ALEXEY N., and EKATERINA M. SVIRINA. "RUSSIAN ASSOCIATION OF STATISTICIANS: FILLING THE GAPS IN THE EDUCATION CHAIN." STATISTICS EDUCATION RESEARCH JOURNAL 19, no. 1 (February 29, 2020): 137–48. http://dx.doi.org/10.52041/serj.v19i1.126.

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Typically, training in Russia for professionals includes school, university, and postgraduate education. People make their choice regarding university or job after school, and they choose jobs after university. These are very sensitive matters. Help in making the right choice is a real asset. The Russian Association of Statisticians (RASt) is an independent, non-profit organisation that does not provide statistical education as a university and does not collect and process data as a statistical institution. But RASt helps students, universities, and producers of statistical data find each other. The paper describes the activities of RASt which organises the school competition in statistics called “Trend” to support students in choosing a profession and the kick-off competition “Career” for university students to help them get to know their employers. The organisers of the competition for school children usually face a number of problems related to the young age of participants and to limited funding. If we are talking about such a country as huge as Russia, the problems increase. To solve these problems, organisers use a combination of competition of presentations about original statistical researches provided by school teams in regions and an online quiz on statistical topics at the final stage. Technologically, the entire process is supported by ROSSTAT with its IT network. The organisers hope that the competition will make the profession of statistician more popular in Russia and attract more students to statistical programmes in universities. First published February 2020 at Statistics Education Research Journal Archives

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Hermanussen, Michael, Christian Aßmann, and Detlef Groth. "Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis." International Journal of Environmental Research and Public Health 18, no. 4 (February 11, 2021): 1741. http://dx.doi.org/10.3390/ijerph18041741.

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(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.

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Forabosco, Paola, Mario Falchi, and Marcella Devoto. "Statistical tools for linkage analysis and genetic association studies." Expert Review of Molecular Diagnostics 5, no. 5 (September 2005): 781–96. http://dx.doi.org/10.1586/14737159.5.5.781.

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