Relevant bibliographies by topics / Statistical techniques

Academic literature on the topic 'Statistical techniques'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Statistical techniques"

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J, Prathipa. "Stock Market Analytics: Statistical and Machine Learning Techniques." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1828–33. http://dx.doi.org/10.37200/ijpr/v24i1/pr200284.

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Madnani, Bhavika. "Statistical Methods: Quantitative Techniques in Business and Industry." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 1099–105. http://dx.doi.org/10.31142/ijtsrd17041.

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Caridad, Daniel, Jana Hančlová, Hosn el Woujoud Bousselmi, and Lorena Caridad y López del Río. "Corporate rating forecasting using Artificial Intelligence statistical techniques." Investment Management and Financial Innovations 16, no. 2 (June 24, 2019): 295–312. http://dx.doi.org/10.21511/imfi.16(2).2019.25.

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Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.
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Blais, Francis X., and Z. Ahmed Quraishi. "Appropriate statistical techniques urged." Journal of the American Osteopathic Association 85, no. 10 (October 1, 1985): 14. http://dx.doi.org/10.1515/jom-1985-851003.

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Williams, D. A., and Z. Govindarjulu. "Statistical Techniques in Bioassay." Biometrics 45, no. 3 (September 1989): 1035. http://dx.doi.org/10.2307/2531710.

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Nembhard, Harriet Black. "Applying Contemporary Statistical Techniques." Technometrics 46, no. 4 (November 2004): 490–91. http://dx.doi.org/10.1198/tech.2004.s233.

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Xie, M., and T. N. Goh. "Statistical techniques for quality." TQM Magazine 11, no. 4 (August 1999): 238–42. http://dx.doi.org/10.1108/09544789910272913.

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Zaret, David. "Statistical Techniques and Sociological Theory." Sociological Theory 5, no. 1 (1987): 36. http://dx.doi.org/10.2307/201993.

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Dovich, Robert A., and John Keenan Taylor. "Statistical Techniques for Data Analysis." Technometrics 35, no. 3 (August 1993): 332. http://dx.doi.org/10.2307/1269538.

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Griffith, Daniel A., Gareth Shaw, and Dennis Wheeler. "Statistical Techniques in Geographical Analysis." Technometrics 30, no. 3 (August 1988): 348. http://dx.doi.org/10.2307/1270091.

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Dissertations / Theses on the topic "Statistical techniques"

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Traiger, Elizabeth A. "Statistical Techniques for flood estimation." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504614.

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Whitehead, Christopher David. "Statistical techniques in credit scoring." Thesis, Lancaster University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443518.

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The credit industry requires continued development and application of new statistical methodology that can improve aspects of the business. The first part of the thesis suggests a new diagnostic, derived from Kalman filtering, to assess model performance. It allows systematic updating of tracked statistics over time by incorporating new observations with the previous best estimates. It has benefits that current industry practices do not possess and we illustrate its worth on a mortgage application database. The second part of the thesis is concerned with regression analysis of financial data. To aid in the understanding of financial data quantile regression and a variable transformation is applied to a 'missed payments' database resulting in a greater understanding and more accurate description of the data. A less standard sampling and modelling approach is also employed which may give increased predictive power on independent data not used for model construction. The third part of this thesis is concerned with regression modelling in situations where the dimensionality is large. Latent variable modelling of explanatory and binary response variables is suggested which can be maximised using an EM algorithm. Less progress than anticipated has been accomplished in this area. The first two parts of this thesis have suggested novel statistical methodology that can provide benefits over current industry practices, both of which are adapted to real credit scoring applications.
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Pickard, Lesley Margaret. "Statistical techniques and project monitoring." Thesis, City University London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241438.

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Wu, Qin. "Reliable techniques for survey with sensitive question." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1496.

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Wang, Qun. "Bootstrap techniques for statistical pattern recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0027/MQ52407.pdf.

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Kempson, C. N. "Statistical techniques for digital modulation recognition." Thesis, Cranfield University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277938.

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Automatic modulation recognition is an important part of communications electronic monitoring and surveillance systems where it is used for signal sorting and receiver switching. ' This thesis introduces a novel application of multivariate statistical techniques to the problem of automatic modulation classification. The classification technique uses modulation features derived from time-domain parameters of instantaneous signal envelope, frequency and phase. Principal component analysis (PCA) is employed for data reduction and multivariate analysis of variance (MANOVA) is used to investigate the data and to construct a discriminant function to enable the classification of modulation type. MANOVA is shown to offer advantages over the techniques already used for modulation recognition, even when simple features are used. The technique is used to construct a universal discriminator which is independent of the unknown signal to noise ratio (SNR) of the received signal. The universal discriminator is shown to extend the range of signal-to-noise ratios (SNRs) over which discrimination is possible, being effective over an SNR range of 0-4OdB. Development of discriminant functions using MANOVA is shown to be an extensible technique, capable of application to more complex problems. i
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Jubock, Z. H. "Statistical models and techniques for dendrochronology." Thesis, University of Nottingham, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381088.

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Petrova, E. N. "Statistical techniques in software reliability quantification." Thesis, University of Liverpool, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283677.

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Iloni, Karen. "Biplot graphical display techniques." Master's thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/17119.

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Includes bibliography.
The thesis deals with graphical display techniques based on the singular value decomposition. These techniques, known as biplots, are used to find low dimensional representations of multidimensional data matrices. The aim of the thesis is to provide a review of biplots for a practical statistician who is not familiar with the area. It therefore focuses on the underlying theory, assuming a standard statisticians' knowledge of matrix algebra, and on the interpretation of the various plots. The topic falls in the realm of descriptive statistics. As such, the methods are chiefly exploratory. They are a means of summarising the data. The data matrix is represented in a reduced number of dimensions, usually two, for simplicity of display. The aim is to summarise the information in the matrix and to present a visual representation of this information. The aim in using graphical display techniques is that the "gain in interpretability far exceeds the loss in information" (Greenacre, 1984). A graphical description is often more easy to understand than a numerical one. Histograms and pie charts are familiar forms of data representation to many people with no other, or very rudimentary, statistical understanding. These are applicable to univariate data. For multivariate data sets, univariate methods do not reveal interesting relationships in the data set as a whole. In addition, a biplot can be presented in a manner which can be readily understood by non-statistically minded individuals. Greenacre (1984) comments that only in recent years has the value of statistical graphics been recognised. Young (1989) notes that recently there has been a shift in emphasis, among statisticians towards exploratory data analysis methods. This school of thought was given momentum by the publication of the book "Exploratory Data Analysis" (Tukey, 1977). The trend has been facilitated by advances in computer technology which have increased both the power and the accessibility of computers. Biplot techniques include the popular correspondence analysis. The original proponents of correspondence analysis (among them Benzecri) reject probabilistic modelling. At the other extreme, some view graphical display techniques as a mere preliminary to the more traditional statistical approaches. Under the latter view, graphical display techniques are used to suggest models and hypotheses. The emphasis in exploratory data techniques such as graphical displays is on 'getting a feel' for the data rather than on building models and testing hypotheses. These methods do not replace model building and hypothesis testing, but supplement them. The essence of the philosophy is that models are suggested by the data, rather than the frequently followed route of first fitting a model. Some work has gone into developing inferential methods, with hypothesis tests and associated p-values for biplot-type techniques (Lebart et al, 1984, Greenacre, 1984). However, this aspect is not important if the techniques are viewed merely as exploratory. Chapter Two provides the mathematical concepts necessary for understanding biplots. Chapter Three explains exactly what a biplot is, and lays the theoretical framework for the biplot techniques that follow. The goal of this chapter is to provide a framework in which biplot techniques can be classified and described. Correlation biplots are described in Chapter Four. Chapter Five discusses the principal component biplot, and the link between these and principal component analysis is drawn. In Chapter Six, correspondence analysis is presented. In Chapter Seven practical issues such as choice of centre are discussed. Practical examples are presented in Chapter Eight. The aim is that these examples illustrate techniques commonly applicable in practice. Evaluation and choice of biplot is discussed in Chapter Nine.
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Wiltshire, S. E. "Statistical techniques for regional flood-frequency analysis." Thesis, University of Newcastle Upon Tyne, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378267.

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Books on the topic "Statistical techniques"

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Elementary statistical techniques. Monterey, Calif: Brooks/Cole, 1985.

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Truong, Young K., and Mechelle M. Lewis, eds. Statistical Techniques for Neuroscientists. Boca Raton : Taylor & Francis, 2016. | Series: Frontiers in: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374345.

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Izenman, Alan J. Modern Multivariate Statistical Techniques. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-78189-1.

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Statistical techniques in bioassay. Basel: Karger, 1988.

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Mason, Robert Deward. Statistical techniques in business and economics. 7th ed. Homewood: Irwin, 1990.

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Mason, Robert Deward. Statistical techniques in business and economics. Boston, Mass: Irwin/McGraw Hill, 1999.

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Mason, Robert Deward. Statistical techniques in business and economics. 8th ed. Homewood, Ill: Irwin, 1993.

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Lind, Douglas A. Statistical techniques in business & economics. New York, NY: McGraw-Hill/Irwin, 2010.

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Lind, Douglas A. Statistical techniques in business & economics. Boston: McGraw-Hill/Irwin, 2008.

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Mishra, B. K. Statistical techniques in social sciences. New Delhi: Anmol Publications, 1991.

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Book chapters on the topic "Statistical techniques"

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March, Marisa Cristina. "Statistical Techniques." In Advanced Statistical Methods for Astrophysical Probes of Cosmology, 57–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35060-3_5.

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Shekhar, Shashi, and Hui Xiong. "Statistical Techniques." In Encyclopedia of GIS, 1141. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1353.

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Blischke, Wallace R., M. Rezaul Karim, and D. N. Prabhakar Murthy. "Additional Statistical Techniques." In Springer Series in Reliability Engineering, 229–64. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-647-4_10.

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Bajpai, A. C., Irene M. Calus, and J. A. Fairley. "Descriptive Statistical Techniques." In Methods of Environmental Data Analysis, 1–35. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9512-9_1.

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DeMaris, Alfred, and Steven H. Selman. "Bivariate Statistical Techniques." In Converting Data into Evidence, 57–77. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7792-1_5.

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Bajpai, A. C., Irene M. Calus, and J. A. Fairley. "Descriptive Statistical Techniques." In Methods of Environmental Data Analysis, 1–35. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2920-6_1.

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Klinke, Sigbert. "Exploratory Statistical Techniques." In Contributions to Statistics, 25–59. Heidelberg: Physica-Verlag HD, 1997. http://dx.doi.org/10.1007/978-3-642-59242-3_2.

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Goudail, François, and Philippe Réfrégier. "Statistical Correlation Techniques." In Statistical Image Processing Techniques for Noisy Images, 49–87. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-8855-3_3.

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Marchevsky, David. "Other Statistical Techniques." In Critical Appraisal of Medical Literature, 265–79. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4205-6_30.

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Roiger, Richard J. "Supervised Statistical Techniques." In Just Enough R!, 79–125. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006695-5.

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Conference papers on the topic "Statistical techniques"

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Vadakkoot, Raveendran, and Achuth Raghavendra. "Improved image visualisation through statistical techniques." In 2009 4th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2009. http://dx.doi.org/10.1109/iciea.2009.5138631.

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Bartley, Philip G., and Shelley B. Begley. "Broadband permittivity determination using statistical techniques." In 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2012. http://dx.doi.org/10.1109/i2mtc.2012.6229540.

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Milner, G. Martin, and Patrice S. Miller. "Statistical Evaluation of Vibration Analysis Techniques." In Intersociety Conference on Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1987. http://dx.doi.org/10.4271/871445.

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DEARMON, JAMES. "Statistical techniques for traffic flow management." In Guidance, Navigation and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-3834.

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Lyons, Louis. "Statistical techniques in high energy physics." In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405258.

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Ahir, T., and R. V. Satyanarayana. "Statistical analysis of image compression techniques." In International Conference on Recent Trends in Engineering, Science & Technology - (ICRTEST 2016). Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1488.

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Shatnawi, Maad, and Nader Mohamed. "Statistical techniques for online personalized advertising." In the 27th Annual ACM Symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2245276.2245406.

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Murthy, K. P. N., and V. S. S. Sastry. "Monte Carlo techniques in statistical physics." In International conference on Statistical Mechanics of Plasticity and Related Instabilities. Trieste, Italy: Sissa Medialab, 2006. http://dx.doi.org/10.22323/1.023.0018.

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Laaraiedh, Mohamed, Nicolas Amiot, Stephane Avrillon, and Bernard Uguen. "Theoretical performances assessment of hybrid localization techniques." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967654.

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Pal, Piya, and P. P. Vaidyanathan. "Correlation-aware techniques for sparse support recovery." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319753.

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Reports on the topic "Statistical techniques"

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Kassam, Saleem A. Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1986. http://dx.doi.org/10.21236/ada185774.

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Kassam, Saleem A. Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada262731.

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Kassam, S. A. Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada167164.

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Kassam, Saleem A. Research on Statistical Techniques for Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, February 1990. http://dx.doi.org/10.21236/ada218811.

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Miller, Barton. Lightweight and Statistical Techniques for Petascale PetaScale Debugging. Office of Scientific and Technical Information (OSTI), June 2014. http://dx.doi.org/10.2172/1135799.

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Schreuder, Hans T., Richard Ernst, and Hugo Ramirez-Maldonado. Statistical techniques for sampling and monitoring natural resources. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2004. http://dx.doi.org/10.2737/rmrs-gtr-126.

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Larson, Edward C., B. E. Parker, Poor Jr., and H. V. Multivariate Nonparametric Statistical Techniques for Simulation Model Validation. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada330896.

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Blair, S. C. Analysis of compressive fracture in rock using statistical techniques. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/90212.

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Cabezas, Clara, and Philip Resnik. Using WSD Techniques for Lexical Selection in Statistical Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada453538.

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Ho, Dominic. Advanced Statistical Signal Processing Techniques for Landmine Detection Using GPR. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada610753.

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