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Статті в журналах з теми "Statistical data science"
Shanmugam, Ramalingam. "Statistical data science." Journal of Statistical Computation and Simulation 90, no. 9 (June 12, 2019): 1733. http://dx.doi.org/10.1080/00949655.2019.1628902.
Повний текст джерелаCai, Junhui, Avishai Mandelbaum, Chaitra H. Nagaraja, Haipeng Shen, and Linda Zhao. "Statistical Theory Powering Data Science." Statistical Science 34, no. 4 (November 2019): 669–91. http://dx.doi.org/10.1214/19-sts754.
Повний текст джерелаGRANT, ROBERT. "STATISTICAL LITERACY IN THE DATA SCIENCE WORKPLACE." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 17–21. http://dx.doi.org/10.52041/serj.v16i1.207.
Повний текст джерелаBlei, David M., and Padhraic Smyth. "Science and data science." Proceedings of the National Academy of Sciences 114, no. 33 (August 7, 2017): 8689–92. http://dx.doi.org/10.1073/pnas.1702076114.
Повний текст джерелаMorse-Gagne, E. E. "Culturomics: Statistical Traps Muddy the Data." Science 332, no. 6025 (March 31, 2011): 35. http://dx.doi.org/10.1126/science.332.6025.35-b.
Повний текст джерелаEFRON, B., and R. TIBSHIRANI. "Statistical Data Analysis in the Computer Age." Science 253, no. 5018 (July 12, 1991): 390–95. http://dx.doi.org/10.1126/science.253.5018.390.
Повний текст джерелаVansteelandt, Stijn. "Statistical Modelling in the Age of Data Science." Observational Studies 7, no. 1 (2021): 217–28. http://dx.doi.org/10.1353/obs.2021.0013.
Повний текст джерелаChen, Yuxin, Yuejie Chi, Jianqing Fan, and Cong Ma. "Spectral Methods for Data Science: A Statistical Perspective." Foundations and Trends® in Machine Learning 14, no. 5 (2021): 566–806. http://dx.doi.org/10.1561/2200000079.
Повний текст джерелаMacGillivray, Helen. "Data science, statistical investigations, team sport, and assessment." Teaching Statistics 41, no. 1 (January 24, 2019): 1–2. http://dx.doi.org/10.1111/test.12189.
Повний текст джерелаReid, Nancy. "Statistical science in the world of big data." Statistics & Probability Letters 136 (May 2018): 42–45. http://dx.doi.org/10.1016/j.spl.2018.02.049.
Повний текст джерелаДисертації з теми "Statistical data science"
Alarcón, Soto Yovaninna. "Data science in HIV : statistical approaches for therapeutic HIV vaccine data." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672179.
Повний текст джерелаLa presente tesis contribuye a la ciencia de datos abordando problemas biológicos relevantes en el desarrollo de vacunas terapéuticas para el Virus de Inmunodeficiencia Humana (VIH) mediante la modelización de datos procedentes de tres ensayos clínicos diferentes. Algunas de las cuestiones suscitadas en estos estudios y que esta tesis aborda son: identificar biomarcadores para estudiar los factores de riesgo del rebote viral del VIH, explicar el tiempo transcurrido hasta el rebote viral como consecuencia del cese de la terapia antirretroviral (cART) considerando la variabilidad de las fuentes de datos y estudiar la relación entre las variables spot size y spot count en ensayos inmunoabsorbentes (ELISpot). Para abordar cada uno de estos interrogantes desde una perspectiva estadística, en esta tesis hemos adaptado una penalización de red elástica para el modelo de vida acelerada (AFT) con datos censurados en un intervalo, ajustado un modelo de Cox de efectos mixtos con datos censurados en un intervalo y mejorado las metodologías estadísticas existentes para tratar los datos de los ensayos ELISpot y de respuesta binaria, respectivamente. En primer lugar, hemos abordado el problema de tener más de cinco mil ARN mensajeros (ARNm) para explicar el tiempo hasta el rebote viral. Para ello, hemos considerado un enfoque de penalización de red elástica para el modelo de vida acelerada. Esta regularización considera una posible estructura de correlación entre las covariables, como sucede con los ARNm. Para este objetivo, primero derivamos la expresión de la función de verosimilitud penalizada considerando una respuesta censurada en un intervalo (tiempo hasta el rebote viral). A continuación, maximizamos esta función utilizando distintos enfoques y métodos de optimización. Finalmente, aplicamos estos métodos al ensayo clínico DCV2 y discutimos sobre diferentes enfoques numéricos para la maximización de la verosimilitud. En segundo lugar, para explicar el tiempo hasta el rebote viral proponemos ajustar un modelo de Cox de efectos mixtos. Dado que el tiempo hasta el rebote viral está censurado en un intervalo utilizamos imputación múltiple basada en una distribución de Weibull truncada. Este modelo nos permite controlar la heterogeneidad entre los estudios de interrupción analítica del tratamiento (ATI) y el hecho de que los pacientes tengan diferente número de episodios ATI. Según el estudio de simulación que realizamos, nuestro método tiene propiedades deseables en términos de exactitud y precisión de los estimadores de los parámetros de efectos fijos. Finalmente abordamos dos problemas diferentes dentro del ensayo clínico BCN02. Por un lado, ajustamos modelos log-binomiales univariados como alternativa a la clásica regresión logística. Por otro lado, utilizamos un modelo ANOVA no balanceado para analizar la variabilidad de los resultados principales de los ensayos ELISpot a lo largo del tiempo. Aunque los ensayos ELISpot se usan a menudo en el estudio del VIH, la relación entre variables como el spot size, spot count y otras no se había estudiado hasta ahora. En esta tesis hemos propuesto y desarrollado diferentes enfoques estadísticos que han dado respuesta a preguntas biológicas planteadas en tres ensayos clínicos. En este trabajo se destaca la importancia de que los distintos miembros de un equipo científico-multidisciplinar colaboren estrechamente, para así poder determinar la metodología apropiada, hacer correctas interpretaciones clínicas de los resultados de éste y, de esta forma, contribuir a un progreso científico significativo. Esperamos que los resultados originales de esta tesis contribuyan al desarrollo y la evaluación de una vacuna terapéutica del VIH, lo cual ayudaría notablemente a mejorar la calidad de vida de las personas infectadas por VIH.
Bruno, Rexanne Marie. "Statistical Analysis of Survival Data." UNF Digital Commons, 1994. http://digitalcommons.unf.edu/etd/150.
Повний текст джерелаRamaboa, Kutlwano K. K. M. "A comparative evaluation of data mining classification techniques on medical trauma data." Master's thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/5973.
Повний текст джерелаThe purpose of this research was to determine the extent to which a selection of data mining classification techniques (specifically, Discriminant Analysis, Decision Trees, and three artifical neural network models - Backpropogation, Probablilistic Neural Networks, and the Radial Basis Function) are able to correctly classify cases into the different categories of an outcome measure from a given set of input variables (i.e. estimate their classification accuracy) on a common database.
Yuan, Yinyin. "Statistical inference from large-scale genomic data." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/1066/.
Повний текст джерелаGuo, Danni. "Contributions to spatial uncertainty modelling in GIS : small sample data." Doctoral thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/19031.
Повний текст джерелаEnvironmental data is very costly and difficult to collect and are often vague (subjective) or imprecise in nature (e.g. hazard level of pollutants are classified as "harmful for human beings"). These realities in practise (fuzziness and small datasets) leads to uncertainty, which is addressed by my research objective: "To model spatial environmental data with .fuzzy uncertainty, and to explore the use of small sample data in spatial modelling predictions, within Geographic Information System (GIS)." The methodologies underlying the theoretical foundations for spatial modelling are examined, such as geostatistics, fuzzy mathematics Grey System Theory, and (V,·) Credibility Measure Theory. Fifteen papers including three journal papers were written in contribution to the developments of spatial fuzzy and grey uncertainty modelling, in which I have a contributed portion of 50 to 65%. The methods and theories have been merged together in these papers, and they are applied to two datasets, PM10 air pollution data and soil dioxin data. The papers can be classified into two broad categories: fuzzy spatial GIS modelling and grey spatial GIS modelling. In fuzzy spatial GIS modelling, the fuzzy uncertainty (Zadeh, 1965) in environmental data is addressed. The thesis developed a fuzzy membership grades kriging approach by converting fuzzy subsets spatial modelling into membership grade spatial modelling. As this method develops, the fuzzy membership grades kriging is put into the foundation of the credibility measure theory, and approached a full data-assimilated membership function in terms of maximum fuzzy entropy principle. The variable modelling method in dealing with fuzzy data is a unique contribution to the fuzzy spatial GIS modelling literature. In grey spatial GIS modelling, spatial predictions using small sample data is addressed. The thesis developed a Grey GIS modelling approach, and two-dimensional order-less spatially observations are converted into two one-dimensional ordered data sequences. The thesis papers also explored foundational problems within the grey differential equation models (Deng, 1985). It is discovered the coupling feature of grey differential equations together with the help of e-similarity measure, generalise the classical GM( 1,1) model into more classes of extended GM( 1,1) models, in order to fully assimilate with sample data information. The development of grey spatial GIS modelling is a creative contribution to handling small sample data.
Smith, Jeremy Stewart. "A statistical approach to automated detection of multi-component radio sources." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/32986.
Повний текст джерелаRao, Ashwani Pratap. "Statistical information retrieval models| Experiments, evaluation on real time data." Thesis, University of Delaware, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1567821.
Повний текст джерелаWe are all aware of the rise of information age: heterogeneous sources of information and the ability to publish rapidly and indiscriminately are responsible for information chaos. In this work, we are interested in a system which can separate the "wheat" of vital information from the chaff within this information chaos. An efficient filtering system can accelerate meaningful utilization of knowledge. Consider Wikipedia, an example of community-driven knowledge synthesis. Facts about topics on Wikipedia are continuously being updated by users interested in a particular topic. Consider an automatic system (or an invisible robot) to which a topic such as "President of the United States" can be fed. This system will work ceaselessly, filtering new information created on the web in order to provide the small set of documents about the "President of the United States" that are vital to keeping the Wikipedia page relevant and up-to-date. In this work, we present an automatic information filtering system for this task. While building such a system, we have encountered issues related to scalability, retrieval algorithms, and system evaluation; we describe our efforts to understand and overcome these issues.
Dikkala, Sai Nishanth. "Statistical inference from dependent data : networks and Markov chains." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127016.
Повний текст джерелаCataloged from the official PDF of thesis.
Includes bibliographical references (pages 259-270).
In recent decades, the study of high-dimensional probability has taken centerstage within many research communities including Computer Science, Statistics and Machine Learning. Very often, due to the process according to which data is collected, the samples in a dataset have implicit correlations amongst them. Such correlations are commonly ignored as a first approximation when trying to analyze statistical and computational aspects of an inference task. In this thesis, we explore how to model such dependences between samples using structured high-dimensional distributions which result from imposing a Markovian property on the joint distribution of the data, namely Markov Random Fields (MRFs) and Markov chains. On MRFs, we explore a quantification for the amount of dependence and we strengthen previously known measure concentration results under a certain weak dependence condition on an MRF called the high-temperature regime. We then go on to apply our novel measure concentration bounds to improve the accuracy of samples computed according to a certain Markov Chain Monte Carlo procedure. We then show how to extend some classical results from statistical learning theory on PAC-learnability and uniform convergence to training data which is dependent under the high temperature condition. Then, we explore the task of regression on data which is dependent according to an MRF under a stronger amount of dependence than is allowed by the high-temperature condition. We then shift our focus to Markov chains where we explore the question of testing whether a certain trajectory we observe corresponds to a chain P or not. We discuss what is a reasonable formulation of this problem and provide a tester which works without observing a trajectory whose length contains multiplicative factors of the mixing or covering time of the chain P. We finally conclude with some broad directions for further research on statistical inference under data dependence.
by Sai Nishanth Dikkala.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Hennessey, Anthony. "Statistical shape analysis of large molecular data sets." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52088/.
Повний текст джерелаChaudhuri, Abon. "Geometric and Statistical Summaries for Big Data Visualization." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1382235351.
Повний текст джерелаКниги з теми "Statistical data science"
Statistical learning and data science. Boca Raton: CRC Press, 2012.
Знайти повний текст джерелаIsmay, Chester, and Albert Y. Kim. Statistical Inference via Data Science. Boca Raton : Taylor and Francis, 2019. | Series: Chapman & hall/crc the r series: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780367409913.
Повний текст джерелаStatistical data analysis. Oxford: Clarendon Press, 1998.
Знайти повний текст джерелаChen, Ding-Geng, Jiahua Chen, Xuewen Lu, Grace Y. Yi, and Hao Yu, eds. Advanced Statistical Methods in Data Science. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2594-5.
Повний текст джерелаPetrucci, Alessandra, Filomena Racioppi, and Rosanna Verde, eds. New Statistical Developments in Data Science. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21158-5.
Повний текст джерелаD, Maddy, Brew J. S, and Quaternary Research Association, eds. Statistical modelling of quaternary science data. Cambridge: Quaternary Research Association, 1995.
Знайти повний текст джерелаCommission, United Nations Statistical. Statistical data editing: Impact on data quality. New York: United Nations, 2006.
Знайти повний текст джерелаAnalyzing social science data. London: SAGE, 2002.
Знайти повний текст джерелаJust plain data analysis: Finding, presenting, and interpreting social science data. 2nd ed. Lanham: Rowman & Littlefield Publishers, 2012.
Знайти повний текст джерелаStatistics and data analysis for social science. Boston: Allyn & Bacon, 2012.
Знайти повний текст джерелаЧастини книг з теми "Statistical data science"
Timbers, Tiffany, Trevor Campbell, and Melissa Lee. "Statistical inference." In Data Science, 315–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003080978-10.
Повний текст джерелаRose, Doug. "Applying Statistical Analysis." In Data Science, 27–38. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2253-9_4.
Повний текст джерелаDettling, Marcel, and Andreas Ruckstuhl. "Statistical Modelling." In Applied Data Science, 181–203. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11821-1_11.
Повний текст джерелаSimmons, Jeffrey P., Lawrence F. Drummy, Charles A. Bouman, and Marc De Graef. "Materials Science vs. Data Science." In Statistical Methods for Materials Science, 3–12. Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781315121062-1.
Повний текст джерелаMane, Deepa, and Sachin Shelke. "Role of Statistical Methods in Data Science." In Data Science, 21–31. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003283249-2.
Повний текст джерелаAgresti, Alan, and Maria Kateri. "Introduction to Statistical Science." In Foundations of Statistics for Data Scientists, 1–28. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003159834-1.
Повний текст джерелаVillalobos Alva, Jalil. "Statistical Data Analysis." In Beginning Mathematica and Wolfram for Data Science, 209–42. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6594-9_6.
Повний текст джерелаYang, Ching-Chi, and Lih-Yuan Deng. "Statistical Learning Approaches." In Dimensionality Reduction in Data Science, 169–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_8.
Повний текст джерелаYang, Ching-Chi, Max Garzon, and Lih-Yuan Deng. "Conventional Statistical Approaches." In Dimensionality Reduction in Data Science, 79–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_4.
Повний текст джерелаAgresti, Alan, and Maria Kateri. "Statistical Science: A Historical Overview." In Foundations of Statistics for Data Scientists, 333–40. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003159834-9.
Повний текст джерелаТези доповідей конференцій з теми "Statistical data science"
Gibbs, Alison L., and Alex Stringer. "The Fundamental Role of Computation in Teaching Statistical Theory." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.rmcxl.
Повний текст джерелаPonomarenko, Alexey. "Reformatting statistical education in Russia: changes in classifications, standards, and programs." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17314.
Повний текст джерелаvon Bing, Yap. "What can statistics education offer to data science?" In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.ljrkt.
Повний текст джерелаGould, Robert, Suyen Machado, Christine Ong, Terri Johnson, James Molyneux, Steve Nolen, Hongsuda Tangmunarunkit, LeeAnn Trusela, and Linda Zanontian. "Teaching data science to secondary students: the mobilize introduction to data science curriculum." In Promoting Understanding of Statistics about Society. International Association for Statistical Education, 2016. http://dx.doi.org/10.52041/srap.16402.
Повний текст джерелаPuloka, Malia S., Stephanie Budgett, and Maxine Pfannkuch. "Statistical education and official statistics - training future data scientists." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.lciru.
Повний текст джерелаSilva, Maria Eduarda, and Pedro Campos. "Statistical education and official statistics - training future data scientists." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.tqrje.
Повний текст джерелаLampropoulos, Panos. "Statistical inversion of the LOFAR EoR data." In ISKAF2010 Science Meeting. Trieste, Italy: Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.112.0071.
Повний текст джерелаSchwab-McCoy, Aimee. "Developing a first-year seminar course in statistics and data science." In Promoting Understanding of Statistics about Society. International Association for Statistical Education, 2016. http://dx.doi.org/10.52041/srap.16307.
Повний текст джерелаMcKinney, Wes. "Data Structures for Statistical Computing in Python." In Python in Science Conference. SciPy, 2010. http://dx.doi.org/10.25080/majora-92bf1922-00a.
Повний текст джерелаBantilan, Niels. "pandera: Statistical Data Validation of Pandas Dataframes." In Python in Science Conference. SciPy, 2020. http://dx.doi.org/10.25080/majora-342d178e-010.
Повний текст джерелаЗвіти організацій з теми "Statistical data science"
Bates, C. Richards, Melanie Chocholek, Clive Fox, John Howe, and Neil Jones. Scottish Inshore Fisheries Integrated Data System (SIFIDS): Work package (3) final report development of a novel, automated mechanism for the collection of scallop stock data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23449.
Повний текст джерелаBozek, Michael, and Tani Hubbard. Greater Yellowstone Network amphibian monitoring protocol science review: A summary of reviewers’ responses. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293614.
Повний текст джерелаVolkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.
Повний текст джерелаMcGee, Steven, Randi Mcgee-Tekula, and Noelia Baez Rodriguez. Using the Science of Hurricane Resilience to Foster the Development of Student Understanding and Appreciation for Science in Puerto Rico. The Learning Partnership, March 2022. http://dx.doi.org/10.51420/conf.2022.1.
Повний текст джерелаDownes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.
Повний текст джерелаHudgens, Bian, Jene Michaud, Megan Ross, Pamela Scheffler, Anne Brasher, Megan Donahue, Alan Friedlander та ін. Natural resource condition assessment: Puʻuhonua o Hōnaunau National Historical Park. National Park Service, вересень 2022. http://dx.doi.org/10.36967/2293943.
Повний текст джерелаMayfield, Colin. Higher Education in the Water Sector: A Global Overview. United Nations University Institute for Water, Environment and Health, May 2019. http://dx.doi.org/10.53328/guxy9244.
Повний текст джерелаGarsa, Adam, Julie K. Jang, Sangita Baxi, Christine Chen, Olamigoke Akinniranye, Owen Hall, Jody Larkin, Aneesa Motala, Sydne Newberry, and Susanne Hempel. Radiation Therapy for Brain Metasases. Agency for Healthcare Research and Quality (AHRQ), June 2021. http://dx.doi.org/10.23970/ahrqepccer242.
Повний текст джерелаLazonick, William, Philip Moss, and Joshua Weitz. Equality Denied: Tech and African Americans. Institute for New Economic Thinking, February 2022. http://dx.doi.org/10.36687/inetwp177.
Повний текст джерелаEvidence Synthesis and Meta-Analysis for Drug Safety. Council for International Organizations of Medical Sciences (CIOMS), 2016. http://dx.doi.org/10.56759/lela7055.
Повний текст джерела