Relevant bibliographies by topics / ANALYZING DATA

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'ANALYZING DATA'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "ANALYZING DATA"

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Suganthi, R., and P. Kamalakannan. "Analyzing Stock Market Data Using Clustering Algorithm." International Journal of Future Computer and Communication 4, no. 2 (April 2015): 108–11. http://dx.doi.org/10.7763/ijfcc.2015.v4.366.

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Harper, Douglas, Michael S. Ball, and Gregory W. H. Smith. "Analyzing Visual Data." Contemporary Sociology 22, no. 4 (July 1993): 619. http://dx.doi.org/10.2307/2074468.

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Maxwell, Scott E., and Norman Cliff. "Analyzing Multivariate Data." Journal of the American Statistical Association 83, no. 404 (December 1988): 1214. http://dx.doi.org/10.2307/2290163.

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Wasserman, Stanley. "Analyzing Categorical Data." American Statistician 60, no. 3 (August 2006): 291–92. http://dx.doi.org/10.1198/tas.2006.s52.

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Katsaounis, Tena I. "Analyzing Multivariate Data." Technometrics 46, no. 2 (May 2004): 254–55. http://dx.doi.org/10.1198/tech.2004.s798.

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Dasgupta, Nairanjana. "Analyzing Categorical Data." Technometrics 46, no. 2 (May 2004): 255–56. http://dx.doi.org/10.1198/tech.2004.s799.

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MUNRO, BARBARA HAZARD. "Analyzing Nominal Data." Clinical Nurse Specialist 3, no. 4 (1989): 176–77. http://dx.doi.org/10.1097/00002800-198900340-00004.

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Batenkov, Dmitry. "Analyzing EEG data." XRDS: Crossroads, The ACM Magazine for Students 18, no. 1 (September 2011): 36–37. http://dx.doi.org/10.1145/2000775.2000788.

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Goth, Gregory. "Analyzing medical data." Communications of the ACM 55, no. 6 (June 2012): 13–15. http://dx.doi.org/10.1145/2184319.2184324.

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Biemann, Torsten, and Deepak K. Datta. "Analyzing Sequence Data." Organizational Research Methods 17, no. 1 (September 5, 2013): 51–76. http://dx.doi.org/10.1177/1094428113499408.

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Dissertations / Theses on the topic "ANALYZING DATA"

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Ju, Hyunsu. "Topics in analyzing longitudinal data." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1565.

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We propose methods for analyzing longitudinal data, obtained in clinical trials and other applications with repeated measures of responses taken over time. Common characteristics of longitudinal studies are correlated responses and observations taken at unequal points in time. The first part of this dissertation examines the justification of a block bootstrap procedure for the repeated measurement designs, which takes into account the dependence structure of the data by resampling blocks of adjacent observations rather than individual data points. In the case of dependent stationary data, under regular conditions, the approximately studentized or standardized block bootstrap possesses a higher order of accuracy. With longitudinal data, the second part of this dissertation shows that the diagonal optimal weights for unbalanced designs can be made to improve the efficiency of the estimators in terms of mean squared error criterion. Simulation study is conducted for each of the longitudinal designs. We will also analyze repeated measurement data set concerning nursing home residents with multiple sclerosis, which is obtained from a large database termed the minimum data set (MDS).
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Roberg, Abigail M. "Data Visualizations: Guidelines for Gathering, Analyzing, and Designing Data." Ohio University Honors Tutorial College / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1524826335755109.

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Lau, Ho-yin Eric. "Statistical methods for analyzing epidemiological data." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34829969.

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Lau, Ho-yin Eric, and 劉浩然. "Statistical methods for analyzing epidemiological data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34829969.

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Stetcenko, D. O., and Y. V. Smityuh. "Intellectual Data Analyzing Using Wavele TTransformation." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47136.

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This article is about bragorectification setting as a complex object of regulation, operating under uncertainty.From the viewpoint of analysis and synthesis automatic control BRS is complex machine of consistent-parallel structure.It is proved that automatic control systems analysis and synthesis BRS of alcohol plants are multifunction objects. The analysis of existing control algorithms, discussed the advantages and disadvantages of these algorithms. Properties of automated rectification device are showed through interconnection of input options causes changes of output parameters.
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Pan, Feng Wang Wei. "Efficient algorithms in analyzing genomic data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2622.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.
Title from electronic title page (viewed Oct. 5, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
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Björck, Olof. "Analyzing gyro data based image registration." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397459.

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An analysis of gyro sensor data with regards to rotational image registration is conducted in this thesis. This is relevant for understanding how well images captured with a moving camera can be registered using only gyro sensor data as motion input. This is commonly the case for Electronic Image Stabilization (EIS) in handheld devices. The theory explaining how to register images based on gyro sensor data is presented, a qualitative analysis of gyro sensor data from three generic Android smartphones is conducted, and rotational image registration simulations using simulated noise as well as real gyro sensor data from the smartphones are presented. An accuracy metric for rotational image registration is presented that measures image registration accuracy in pixels (relevant for frame to frame image registration) or pixels per second (relevant for video EIS). This thesis shows that noise in gyro sensor data affects image registration accuracy to an extent that is noticeable in 1080x1920 resolution video displayed on larger monitors such as a computer monitor or when zooming digitally, but not to any significant extent displayed on a monitor the size of a regular smartphone display without zooming. Different screen resolutions and frame rates will affect the image registration accuracy and would be interesting to investigate in further work. Ways to improve the gyro sensor data would also be interesting to investigate.
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Hossain, Abu. "General methods for analyzing bounded proportion data." Thesis, London Metropolitan University, 2017. http://repository.londonmet.ac.uk/1243/.

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This thesis introduces two general classes of models for analyzing proportion response variable when the response variable Y can take values between zero and one, inclusive of zero and/or one. The models are inflated GAMLSS model and generalized Tobit GAMLSS model. The inflated GAMLSS model extends the flexibility of beta inflated models by allowing the distribution on (0,1) of the continuous component of the dependent variable to come from any explicit or transformed (i.e. logit or truncated) distribution on (0,1) including highly skewed and/or kurtotic or bimodal distributions. The second proposed general class of model is the generalized Tobit GAMLSS model. The generalized Tobit GAMLSS model relaxes the underlying normal distribution assumption of the latent variable in the Tobit model to a very general class of distribution on the real line. The thesis also provides likelihood inference and diagnostic and model selection tools for these classes of models. Applications of both the models are conducted using different sets of data to check the robustness of the proposed models. The originality of the thesis starts from chapter 4 and in particular chapter 5, 6 and 7 with applications of models in chapter 8, 9 and 10.
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Ho, Wai-shing. "Techniques for managing and analyzing unconventional data." Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B39849028.

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Ho, Wai-shing, and 何偉成. "Techniques for managing and analyzing unconventional data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B39849028.

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Books on the topic "ANALYZING DATA"

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Analyzing environmental data. Hoboken, NJ: Wiley, 2004.

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Simonoff, Jeffrey S. Analyzing Categorical Data. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21727-7.

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Piegorsch, Walter W., and A. John Bailer. Analyzing Environmental Data. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470012234.

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H, Smith Gregory W., ed. Analyzing visual data. Newbury Park, CA: Sage Publications, 1992.

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Aboff, Marcie. Analyzing doggie data. Mankato, Minn: Capstone Press, 2011.

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Douglas, Carroll J., Green Paul E, and Green Paul E, eds. Analyzing multivariate data. Pacific Grove, CA: Thomson Brooks/Cole, 2003.

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Alan, Bryman, and Burgess Robert G, eds. Analyzing qualitative data. London: Routledge, 1994.

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Norman, Cliff. Analyzing multivariate data. San Diego: Harcourt Brace Jovanovich, 1987.

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Smith, W. D. Analyzing forest health data. Asheville, NC: United States Dept. of Agriculture, Forest Service, Southern Research Station, 2005.

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Meisen, Philipp. Analyzing Time Interval Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15728-9.

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Book chapters on the topic "ANALYZING DATA"

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Treder, Martin. "Analyzing Data." In The Chief Data Officer Management Handbook, 353–81. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6115-6_20.

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Zhao, Peng. "Analyzing Data." In Working with Data in Public Health: A Practical Pathway with R, 109–30. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0135-7_7.

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Gilbert, Nigel. "Modelling mobility and change." In Analyzing Tabular Data, 82–100. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-7.

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Gilbert, Nigel. "Choosing and fitting models." In Analyzing Tabular Data, 66–81. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-6.

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Gilbert, Nigel. "High dimension tables." In Analyzing Tabular Data, 101–14. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-8.

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Gilbert, Nigel. "Logistic regression." In Analyzing Tabular Data, 131–46. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-10.

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Gilbert, Nigel. "Effects and odds ratios." In Analyzing Tabular Data, 115–30. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-9.

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Gilbert, Nigel. "Cross-tabulations." In Analyzing Tabular Data, 27–38. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-3.

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Gilbert, Nigel. "Causal analysis." In Analyzing Tabular Data, 147–58. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-11.

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Gilbert, Nigel. "Association and interaction." In Analyzing Tabular Data, 39–51. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003259701-4.

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Conference papers on the topic "ANALYZING DATA"

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Reising, Kiernan. "Analyzing Acoustic Data From Quench Training." In Analyzing Acoustic Data From Quench Training. US DOE, 2021. http://dx.doi.org/10.2172/1835869.

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Carpendale, Sheelagh, Søren Knudsen, Alice Thudt, and Uta Hinrichs. "Analyzing Qualitative Data." In ISS '17: Interactive Surfaces and Spaces. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132272.3135087.

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Garofalakis, Minos. "Analyzing massive data streams." In the 8th ACM SIGMOD workshop. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/882082.882084.

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Hasan, Ragib, Rakesh Bobba, and Himanshu Khurana. "Analyzing NASPInet data flows." In 2009 IEEE/PES Power Systems Conference and Exposition (PSCE). IEEE, 2009. http://dx.doi.org/10.1109/psce.2009.4840100.

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Cheng, Yue, Ali Anwar, and Xuejing Duan. "Analyzing Alibaba’s Co-located Datacenter Workloads." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622518.

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Buchanan, N., Steven Calvez, Derek Doyle, V. Hewes, Alexander Himmel, James Kowalkowski, Andrew Norman, et al. "Analyzing NOvA Neutrino Data with the Perlmutter Supercomputer." In Analyzing NOvA Neutrino Data with the Perlmutter Supercomputer. US DOE, 2023. http://dx.doi.org/10.2172/1915439.

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Stoyanovich, Julia, Marie Jacob, and Xuemei Gong. "Analyzing Crowd Rankings." In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2767109.2767110.

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Greguska, Frank R., Thomas Huang, Brian Wilson, Nga Quach, and Joe Jacob. "Analyzing big ocean science data with NEXUS." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258530.

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Mahajan, Rutuja, William Romine, Michele Miller, and Tanvi Banerjee. "Analyzing Public Outlook towards Vaccination using Twitter." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006136.

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Reittu, Hannu, Ville Kotovirta, Lasse Leskela, Hannu Rummukainen, and Tomi Raty. "Towards analyzing large graphs with quantum annealing." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006174.

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Reports on the topic "ANALYZING DATA"

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Smith, William D., and Barbara L. Conkling. Analyzing forest health data. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2004. http://dx.doi.org/10.2737/srs-gtr-77.

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Smith, William D., and Barbara L. Conkling. Analyzing forest health data. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2004. http://dx.doi.org/10.2737/srs-gtr-77.

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Miller, Kyle G. Analyzing data from a PDV microphone. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1092434.

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Merlo, A. P., and P. H. Worley. Analyzing PICL trace data with MEDEA. Office of Scientific and Technical Information (OSTI), November 1993. http://dx.doi.org/10.2172/10112559.

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Walston, S. Thoughts on Analyzing Neutron Multiplicity Data. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1777340.

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Miller, G., W. C. Inkret, and M. E. Schillaci. Analyzing bioassay data using Bayesian methods -- A primer. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/676932.

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Dunlavy, Daniel M., W. Philip, Jr Kegelmeyer, and Tamara Gibson Kolda. Multilinear algebra for analyzing data with multiple linkages. Office of Scientific and Technical Information (OSTI), April 2006. http://dx.doi.org/10.2172/883132.

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Wu, Kesheng, E. Wes Bethel, Ming Gu, David Leinweber, and Oliver Ruebel. A Big Data Approach to Analyzing Market Volatility. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1171128.

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Xie, Hui, Yi Qian, and Leming Qu. A Semiparametric Approach for Analyzing Nonignorable Missing Data. Cambridge, MA: National Bureau of Economic Research, August 2010. http://dx.doi.org/10.3386/w16270.

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MITRE CORP BEDFORD MA. Understanding the Applicability of Sequential Data Analysis Techniques for Analyzing Usability Data. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada263144.

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