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Letteratura scientifica selezionata sul tema "Statistical methods"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Statistical methods"

1

Williams, William A., George W. Snedecor, William G. Cochran, and D. F. Cox. "Statistical Methods." Journal of the American Statistical Association 86, no. 415 (September 1991): 834. http://dx.doi.org/10.2307/2290435.

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Barrett, Linda, R. J. Freund, and W. J. Wilson. "Statistical Methods." American Statistician 51, no. 3 (August 1997): 296. http://dx.doi.org/10.2307/2684912.

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Kasparek, Linda, Rudolph J. Freund, and William J. Wilson. "Statistical Methods." American Statistician 48, no. 1 (February 1994): 59. http://dx.doi.org/10.2307/2685092.

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Jones, Peter W. "Statistical Methods." Technometrics 33, no. 1 (February 1991): 114. http://dx.doi.org/10.1080/00401706.1991.10484783.

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Wong, Aldous. "Statistical Methods." Technometrics 36, no. 2 (May 1994): 222. http://dx.doi.org/10.1080/00401706.1994.10485777.

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Farnum, Nicholas R. "Statistical Methods." Journal of Quality Technology 22, no. 3 (July 1990): 252–53. http://dx.doi.org/10.1080/00224065.1990.11979254.

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Wisniewski, Mik, R. J. Freund, and W. J. Wilson. "Statistical Methods." Statistician 43, no. 1 (1994): 209. http://dx.doi.org/10.2307/2348952.

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Jones, Douglas H., George W. Snedecor, and William G. Cochran. "Statistical Methods." Journal of Educational and Behavioral Statistics 19, no. 3 (1994): 304. http://dx.doi.org/10.2307/1165300.

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G.W.A.D. "Statistical methods." Microelectronics Reliability 33, no. 1 (January 1993): 106. http://dx.doi.org/10.1016/0026-2714(93)90050-9.

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Obuchowski, Nancy A. "Multivariate Statistical Methods." American Journal of Roentgenology 185, no. 2 (August 2005): 299–309. http://dx.doi.org/10.2214/ajr.185.2.01850299.

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Tesi sul tema "Statistical methods"

1

Bai, Yang, and 柏楊. "Statistical analysis for longitudinal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841756.

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Young, G. A. "Data-based statistical methods." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383307.

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Bridges, M. "Statistical methods in cosmology." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596904.

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We outline the application of a new method of evidence calculation called nested sampling Skilling (2004). We use a clustered ellipsoidal bound to restrict the parameter space sampled, that is generic enough to be used for even complex multimodal posteriors. We demonstrate our algorithms, COSMOCLUST makes important savings in computational time when compared with previous methods. The study of the primordial power spectrum, which seeded the structure formation observed in both the CMB and large scale structure, is crucial in unravelling early universe physics. In this thesis we analyse a number of spectral parameterisations based on both physical and observational grounds. Using the evidence we determine the most appropriate model in both WMAP 1 year and WMAP 3 year data (including additionally a selection of high resolution CMB and large scale structure data). We conclude that currently the evidence does suggest the need for a tilt in the spectrum, however the presence of running of the spectral index is dependent on the inclusion of, specifically Ly-α data. Bayesian analysis in cosmology is computationally demanding. We have succeeding in improving the efficiency of inference problems for a wide variety of cosmological applications by training neural networks to ‘learn’ how observables such as the CMB spectrum change with input cosmological parameters. We demonstrate that improvements in speed of several orders of magnitude are possible using our algorithm COSMONET.

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Okasha, Mahmoud Khaled Mohamed. "Statistical methods in dendrochronology." Thesis, University of Sheffield, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295760.

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Muncey, Harriet Jane. "Statistical methods in metabolomics." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24877.

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Metabolomics lies at the fulcrum of the system biology 'omics'. Metabolic profiling offers researchers new insight into genetic and environmental interactions, responses to pathophysi- ological stimuli and novel biomarker discovery. Metabolomics lacks the simplicity of a single data capturing technique; instead, increasingly sophisticated multivariate statistical techniques are required to tease out useful metabolic features from various complex datasets. In this work, two major metabolomics methods are examined: Nuclear Magnetic Resonance (NMR) Spec- troscopy and Liquid Chromatography-Mass Spectrometry (LC-MS). MetAssimulo, an 1H-NMR metabolic-profile simulator, was developed in part by this author and is described in the Chap- ter 2. Peak positional variation is a phenomenon occurring in NMR spectra that complicates metabolomic analysis so Chapter 3 focuses on modelling the effect of pH on peak position. Analysis of LC-MS data is somewhat more complex given its 2-D structure, so I review existing pre-processing and feature detection techniques in Chapter 4 and then attempt to tackle the issue from a Bayesian viewpoint. A Bayesian Partition Model is developed to distinguish chro- matographic peaks representing useful features from chemical and instrumental interference and noise. Another of the LC-MS pre-processing problems, data binning, is also explored as part of H-MS: a pre-processing algorithm incorporating wavelet smoothing and novel Gaussian and Exponentially Modified Gaussian peak detection. The performance of H-MS is compared alongside two existing pre-processing packages: apLC-MS and XCMS.

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Kouba, Pavel. "Možnost zavedení a využívání metody SPC ve výrobě v organizaci s.n.o.p CZ, a.s." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-16319.

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The diploma paper is devoted to verification the application of SPC methods and performs evaluation of statistical stability and process eligibility of steel stampings in the real production process. In the second part is the author of the paper trying to design the optimal form of SPC methods for its use in a specified manufacturing process.

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Postler, Štěpán. "Statistická analýza ve webovém prostředí." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199226.

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The aim of this thesis is creating a web application that allows dataset import and analyzing data with use of statistical methods. The application uses a user access that allows multiple number of persons manipulate with a single dataset, as well as interact with each other. Data is stored on a remote server and application is accessible from any computer that is connected to the Internet. The application is created in PHP programming language with use of MySQL database system, and user interface is built in HTML language with use of CSS styles. All parts of application are stored on an attached CD in form of text files. In addition to the web application, a part of the thesis is also a text output, which contains a theoretical part in form of description of the chosen statistical analysis methods, and a practical part containing list of application's functions, data model's description and demonstration of data analysis options on specific examples.

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Corrado, Charles J. "Nonparametric statistical methods in financial market research." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/184608.

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This dissertation presents an exploration of the use of nonparametric statistical methods based on ranks for use in financial market research. Applications to event study methodology and the estimation of security systematic risk are analyzed using a simulation methodology with actual daily security return data. The results indicate that procedures based on ranks are more efficient than normal theory procedures currently in common use.

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Sezgin, Ozge. "Statistical Methods In Credit Rating." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607625/index.pdf.

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Credit risk is one of the major risks banks and financial institutions are faced with. With the New Basel Capital Accord, banks and financial institutions have the opportunity to improve their risk management process by using Internal Rating Based (IRB) approach. In this thesis, we focused on the internal credit rating process. First, a short overview of credit scoring techniques and validation techniques was given. By using real data set obtained from a Turkish bank about manufacturing firms, default prediction logistic regression, probit regression, discriminant analysis and classification and regression trees models were built. To improve the performances of the models the optimum sample for logistic regression was selected from the data set and taken as the model construction sample. In addition, also an information on how to convert continuous variables to ordered scaled variables to avoid difference in scale problem was given. After the models were built the performances of models for whole data set including both in sample and out of sample were evaluated with validation techniques suggested by Basel Committee. In most cases classification and regression trees model dominates the other techniques. After credit scoring models were constructed and evaluated, cut-off values used to map probability of default obtained from logistic regression to rating classes were determined with dual objective optimization. The cut-off values that gave the maximum area under ROC curve and minimum mean square error of regression tree was taken as the optimum threshold after 1000 simulation. Keywords: Credit Rating, Classification and Regression Trees, ROC curve, Pietra Index

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Jung, Min Kyung. "Statistical methods for biological applications." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278454.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007.<br>Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6740. Adviser: Elizabeth A. Housworth. Title from dissertation home page (viewed May 20, 2008).

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Libri sul tema "Statistical methods"

1

Saville, David J., and Graham R. Wood. Statistical Methods. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0747-4.

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1940-, Wilson William J., ed. Statistical methods. Boston: Academic Press, 1993.

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Freund, Rudolf Jakob. Statistical methods. San Diego: Academic Press, 1997.

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Open University. Statistical Methods Course Team., ed. Statistical methods. Milton Keynes: Open University Press, 1987.

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5

Snedecor, George Waddel. Statistical methods. 8th ed. Ames: Iowa State University Press, 1989.

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Standardization, International Organization for. Statistical methods. 3rd ed. Genève: International Organization for Standardization, 1989.

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J, Wilson William, and Mohr Donna L, eds. Statistical methods. 3rd ed. Amsterdam: Elsevier, 2010.

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1940-, Wilson William J., ed. Statistical methods. 2nd ed. San Diego, Calif: Academic Press, 2003.

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Terdik, György. Multivariate Statistical Methods. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81392-5.

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Hanagal, David D., Raosaheb V. Latpate, and Girish Chandra, eds. Applied Statistical Methods. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7932-2.

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Capitoli di libri sul tema "Statistical methods"

1

Wehenkel, Louis A. "Statistical Methods." In Automatic Learning Techniques in Power Systems, 47–70. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_3.

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Nendza, Monika. "Statistical methods." In Structure—Activity Relationships in Environmental Sciences, 63–88. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5805-7_4.

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Rizzi, Chiara. "Statistical Methods." In Springer Theses, 109–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52877-5_6.

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Platen, Eckhard, and David Heath. "Statistical Methods." In A Benchmark Approach to Quantitative Finance, 55–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-47856-0_2.

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Salomon, David. "Statistical Methods." In Data Compression, 21–100. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_2.

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Salomon, David. "Statistical Methods." In Data Compression, 39–150. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_3.

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Salomon, David, and Giovanni Motta. "Statistical Methods." In Handbook of Data Compression, 211–327. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-903-9_5.

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Sullivan, Rob. "Statistical Methods." In Introduction to Data Mining for the Life Sciences, 235–302. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-59745-290-8_6.

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Vidal, René, Yi Ma, and S. Shankar Sastry. "Statistical Methods." In Interdisciplinary Applied Mathematics, 217–66. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-0-387-87811-9_6.

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Salomon, David. "Statistical Methods." In A Guide to Data Compression Methods, 9–55. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21708-6_2.

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Atti di convegni sul tema "Statistical methods"

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Jones, Peter, Kay Lipson, and Brian Phillips. "A role for computer intensive methods in introducing statistical inference." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93311.

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Introductory statistics courses have become increasingly prevalent in a wide variety of undergraduate and graduate programs over the past few years. This has resulted in the study of inferential statistics becoming the norm rather than exception. Whilst statistics was once a course chosen by the more mathematically able student, many current students of statistics have little mathematical aptitude or expertise (Tanis, 1992 for example). As a result, many introductory statistic courses have moved away from including much of the statistical theory that underpins inference to become basically technique oriented "recipe book" courses. the internet danger of producing students capable of performing various complex statistical tests without really knowing what they are doing is obvious, particularly if the house includes the use of a sophisticated statistical computer package.

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Kitchenham, Barbara. "Robust statistical methods." In EASE '15: 19th International Conference on Evaluation and Assessment in Software Engineering. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2745802.2747956.

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Rossi, Nicola, and Meho-Saša Kovačević. "Statistical methods in geotechnics." In 4th Symposium on Doctoral Studies in Civil Engineering. University of Zagreb Faculty of Civil Engineering, 2018. http://dx.doi.org/10.5592/co/phdsym.2018.03.

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Kameneva, Svetlana Vladimirovna. "STATISTICAL METHODS IN ARCHAEOLOGY." In IV Международная научно-практическая конференция "Научные исследования и инновации". KDU, Moscow, 2021. http://dx.doi.org/10.31453/kdu.ru.978-5-7913-1168-9-2021-6-17.

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The use of statistical methods in the analysis of archaeological problems is an effective interdisciplinary approach. The article briefly describes the chronology using of statistical methods in archaeology, provides examples using of basic statistical methods in the analysis of the ceramic ornament the Zmeisky settlement in North Ossetia and more modern archaeological finds made by the Azov archaeological expedition in the Krasnodar region.

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Julie, Hongki. "Application of pedagogy reflective in statistical methods course and practicum statistical methods." In INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Empowering Engineering using Mathematics. Author(s), 2017. http://dx.doi.org/10.1063/1.4994429.

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Swendsen, Robert H. "Acceleration and analysis methods in statistical physics." In Computer-aided statistical physics. AIP, 1992. http://dx.doi.org/10.1063/1.41937.

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Budgett, Stephanie, and Maxine Pfannkuch. "Assessing students’ statistical literacy." In Assessing Student Learning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.07201.

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This paper describes the methods and challenges in assessing an undergraduate course entitled Lies, Damned Lies, and Statistics. The course has three main aims: to instill in students the ability to “think statistically”, to enable students to critically evaluate statistically based reports and to teach students to construct statistically sound reports. The assessment is examined in terms of these aims and the criteria developed by Gal (2002) for statistical literacy.

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Napieralski, Jan Andrzej. "Statistical methods for data prediction." In 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE, 2016. http://dx.doi.org/10.1109/memstech.2016.7507532.

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Miller, S. R. "Statistical Tolerance Methods For Manufacturing." In Electro International, 1991. IEEE, 1991. http://dx.doi.org/10.1109/electr.1991.718225.

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"Session 6C: Statistical Timing Methods." In ICCAD 2004. International Conference on Computer Aided Design. IEEE, 2004. http://dx.doi.org/10.1109/iccad.2004.1382620.

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Rapporti di organizzazioni sul tema "Statistical methods"

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Black, Paul E. Formal methods for statistical software. Gaithersburg, MD: National Institute of Standards and Technology, October 2019. http://dx.doi.org/10.6028/nist.ir.8274.

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Nelson, David O. Statistical methods in physical mapping. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/95181.

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Banks, David, William Dashiell, Leonard Gallagher, Charles Hagwood, Raghu Kacker, and Lynne Rosenthal. Software testing by statistical methods:. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6129.

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Dorigo, Tommaso. Statistical Methods for Fundamental Science. Instats Inc., 2023. http://dx.doi.org/10.61700/tr03is3us8wzp469.

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This 5-day workshop, taught by renowned CERN physicist Tommaso Dorigo, is designed to equip PhD students, professors, and professional researchers with the skills to apply optimal statistical methods in any scientific field. The workshop covers the crucial topics necessary for researchers to avoid typical mistakes in statistical analysis and produce effective statistical inference, enabling you to extract more information from the same data than your peers. The course also provides an introduction to machine learning, offering a solid foundation in powerful statistical methods for all disciplines. An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the workshop.

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Silverman, B. W., and C. Jennison. Statistical Smoothing Methods: Some Practical Aspects. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada193724.

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Silverman, B. W., and C. Jennison. Statistical Smoothing Methods: Some Practical Aspects. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada193737.

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Silverman, B. W., and C. Jennison. Statistical Smoothing Methods: Some Practical Aspects. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada193738.

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Silverman, B. W., and C. Jennison. Statistical Smoothing Methods: Some Practical Aspects. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada195671.

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Fink, Eugene. Statistical Selection Among Problem-Solving Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada327284.

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MacDonald, Jacqueline A., and Mitchell J. Small. Statistical Methods for UXO Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada603920.

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