Relevant bibliographies by topics / Statistics and methodology

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Statistics and methodology'

Author: Grafiati
Published: 10 September 2021
Last updated: 27 April 2022

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Journal articles on the topic "Statistics and methodology"

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Obuchowski, Nancy A., and Michael L. Lieber. "Statistics and methodology." Skeletal Radiology 37, no. 5 (May 2008): 393–96. http://dx.doi.org/10.1007/s00256-008-0448-1.

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Whitaker, Heather J., and Yonas Ghebremichael-Weldeselassie. "Self-Controlled Case Series Methodology." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 241–61. http://dx.doi.org/10.1146/annurev-statistics-030718-105108.

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The self-controlled case series method is an epidemiological study design in which individuals act as their own control. The method offers advantages but has several limitations. This article outlines the self-controlled case series method and reviews methodological developments that address some of these limitations.
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RC and W. Paul Vogt. "Dictionary of Statistics and Methodology." Journal of the American Statistical Association 94, no. 446 (June 1999): 657. http://dx.doi.org/10.2307/2670204.

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AA and W. Paul Vogt. "Dictionary of Statistics and Methodology." Journal of the American Statistical Association 89, no. 427 (September 1994): 1150. http://dx.doi.org/10.2307/2290972.

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Luo, Shan, and Ethan Vishniac. "Three-dimensional shape statistics: Methodology." Astrophysical Journal Supplement Series 96 (February 1995): 429. http://dx.doi.org/10.1086/192126.

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Briscoe, Simon, and W. P. Vogt. "Dictionary of Statistics and Methodology." Statistician 43, no. 3 (1994): 473. http://dx.doi.org/10.2307/2348607.

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Giesbrecht, N., Y. Sell, C. Scialfa, L. Sandals, and P. Ehlers. "Essential Topics in Introductory Statistics and Methodology Courses." Teaching of Psychology 24, no. 4 (October 1997): 242–46. http://dx.doi.org/10.1207/s15328023top2404_2.

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The purpose of this research is to assess interdisciplinary agreement about the content of an introductory course in statistics and research methods. We compiled a set of methodological and statistical topics from relevant research, introductory textbooks, and course descriptions. Eighteen professors in the natural sciences, social sciences, and education assessed the importance of these topics for a hypothetical introductory statistics and research methods course. Results indicate agreement across disciplines on the relative importance of 97% of the statistical topics, with 75% of these topics deemed to be important. In contrast, there is agreement on the relative importance of only 48% of the research methods topics. The results are useful in the evaluation of existing statistics courses and texts. In addition, the data provide support for interdisciplinary introductory statistics instruction but also raise questions regarding the apparent discipline-specific nature of instruction in methodology.
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Boersma, Eric, Ron van Domburg, Sanne Hoeks, Isabella Kardys, and Mattie Lenzen. "EuroIntervention - Methodology and Statistics Review Board." EuroIntervention 9, no. 1 (May 2013): 21. http://dx.doi.org/10.4244/eijv9i1a3.

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Degraeuwe, P. L. J., F. H. M. Nieman, A. Yaramis, and K. Bilgin. "Caution About Trial Methodology and Statistics." PEDIATRICS 108, no. 6 (December 1, 2001): 1383–84. http://dx.doi.org/10.1542/peds.108.6.1383-a.

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Stoimenova, Eugenia. "Methodology in robust and nonparametric statistics." Journal of Applied Statistics 40, no. 12 (July 4, 2013): 2773. http://dx.doi.org/10.1080/02664763.2013.816029.

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Dissertations / Theses on the topic "Statistics and methodology"

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Zhang, Bo. "Machine Learning on Statistical Manifold." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/110.

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This senior thesis project explores and generalizes some fundamental machine learning algorithms from the Euclidean space to the statistical manifold, an abstract space in which each point is a probability distribution. In this thesis, we adapt the optimal separating hyperplane, the k-means clustering method, and the hierarchical clustering method for classifying and clustering probability distributions. In these modifications, we use the statistical distances as a measure of the dissimilarity between objects. We describe a situation where the clustering of probability distributions is needed and useful. We present many interesting and promising empirical clustering results, which demonstrate the statistical-distance-based clustering algorithms often outperform the same algorithms with the Euclidean distance in many complex scenarios. In particular, we apply our statistical-distance-based hierarchical and k-means clustering algorithms to the univariate normal distributions with k = 2 and k = 3 clusters, the bivariate normal distributions with diagonal covariance matrix and k = 3 clusters, and the discrete Poisson distributions with k = 3 clusters. Finally, we prove the k-means clustering algorithm applied on the discrete distributions with the Hellinger distance converges not only to the partial optimal solution but also to the local minimum.
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Vrahimis, Andreas. "Smoothing methodology with applications to nonparametric statistics." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/smoothing-methodology-with-applications-to-nonparametric-statistics(6d6567f2-1bfa-4e77-8dbb-71fea7564185).html.

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The work in this thesis is based on kernel smoothing techniques with applications to nonparametric statistical methods and especially kernel density estimation and nonparametric regression. We examine a bootstrap iterative method of choosing the smoothing parameter, in univariate kernel density estimation, and propose an empirical smoothness correction that generally improves the method for small-medium sample sizes tested. In a simulation study performed, the corrected bootstrap iterative method shows consistent overall performance and can compete with other popular widely used methods. The theoretical asymptotic properties of the smoothed bootstrap method, in univariate kernel density estimation, are examined and an adaptive data-based choice of fixed pilot smoothing parameter formed, that provides a good performance trade-off among distributions of various shapes, with fast relative rate of convergence to the optimal. The asymptotic and practical differences of the smoothed bootstrap method, when the diagonal terms of the error criterion are included or omitted, are also examined. The exclusion of the diagonal terms yields faster relative rates of convergence of the smoothing parameter to the optimal but a simulation study shows that for smaller sample sizes, including the diagonal terms can be favourable. In a real data set application both methods produced similar smoothing parameters and the resulting kernel density estimates were of reasonable smoothness.Existing methods of kernel density estimation in two dimensions are discussed and the corrected bootstrap iterative method is adapted to work in the bivariate kernel density estimation, with considerable success. Additionally, the theoretical asymptotic properties of the smoothed bootstrap method, in the bivariate kernel density estimation, are examined, and adaptive data-based choices for the fixed pilot smoothing parameters formed, that provide fast relative rates of convergence to the optimal, compared to other popular methods. The smoothed bootstrap method with the diagonal terms of the error criterion omitted, exhibits slightly faster relative rates of convergence, compared to the method which includes the diagonal terms, and in a simulation study they performed considerably well, compared to other methods. Also, we discover that a scaling transformation of the data, before applying the method, leads to poor results for distributions of various shapes, and it should be generally avoided. In an application using the iris flowers data set, both smoothed bootstrap versions suggested, produce reasonable kernel density estimates. We also look at various methods of estimating the variance of the errors in nonparametric regression and suggest a simple robust method of estimating the error variance, for the homoscedastic fixed design. The method is based on a multiplicative correction of the variance of the residuals and a comparison with popular difference-based methods shows favourable results, especially when the local linear estimator is employed.
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Ren, Yu. "The methodology of flowgraph models." Thesis, London School of Economics and Political Science (University of London), 2011. http://etheses.lse.ac.uk/318/.

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Flowgraph models are directed graph models for describing the dynamic changes in a stochastic process. They are one class of multistate models that are applied to analyse time-to-event data. The main motivation of the flowgraph models is to determine the distribution of the total waiting times until an event of interest occurs in a stochastic process that progresses through various states. This thesis applies the methodology of flowgraph models to the study of Markov and SemiMarkov processes. The underlying approach of the thesis is that the access to the moment generating function (MGF) and cumulant generating function (CGF), provided by Mason’s rule enables us to use the Method of Moments (MM) which depends on moments and cumulant. We give a new derivation of the Mason’s rule to compute the total waiting MGF based on the internode transition matrix of a flowgraph. Next, we demonstrate methods to determine and approximate the distribution of total waiting time based on the inversion of the MGF, including an alternative approach using the Pad´e approximation of the MGF, which always yields a closed form density. For parameter estimation, we extend the Expectation-Maximization (EM) algorithm to estimate parameters in the mixture of negative weight exponential density. Our second contribution is to develop a bias correction method in the Method of Moments (BCMM). By investigating methods for tail area approximation, we propose a new way to estimate the total waiting time density function and survival
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Levy, Melanie E. "Survey analysis| Methodology and application using CHIS data." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527014.

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Over the past hundred years, advancements in survey research and understanding of survey methodology and analysis have removed major biases when small numbers of respondents can speak for larger groups in addition to the ability of modem polls to support inferences about populations. This project presents a brief history of survey methodology and utilizes common applied statistical procedures using the 2009 California Health Interview Survey (CHIS). Survey methodology and analysis will be explored through examples including survey linear regression analysis, canonical correlation and multinomial logistic regression.

This project's goal is to create greater understanding of the survey analysis process, as well as, some of the challenges survey researchers face. With this knowledge more procedures can be adapted to incorporate survey design to expand survey methodology and analysis to reach more diverse research needs.

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Zhang, Ye. "Community Detection| Fundamental Limits, Methodology, and Variational Inference." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10957347.

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Network analysis has become one of the most active research areas over the past few years. A core problem in network analysis is community detection. In this thesis, we investigate it under Stochastic Block Model and Degree-corrected Block Model from three different perspectives: 1) the minimax rates of community detection problem, 2) rate-optimal and computationally feasible algorithms, and 3) computational and theoretical guarantees of variational inference for community detection.

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Stewart, Brandon Michael. "Three Papers in Political Methodology." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467206.

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This collection of three papers develops two statistical techniques for addressing canonical problems in applied computational social science: unsupervised text analysis and regression with dependent data. In both cases I provide a flexible framework that allows the analyst to leverage known structure within the data to improve inference. The first paper introduces the Structural Topic Model (STM) which generalizes and extends a broad class of probabilistic topic models developed in computer science. Crucially for applied social science, STM provides a framework for estimating the factors which drive topical frequency and content within documents. The second paper explores the challenge that non-convex likelihoods pose for applied research with topic models. The paper presents a series of diagnostics and discusses the under-appreciated role of initialization methods. The third paper introduces Latent Factor Regressions (LFR), a new set of tools for regression modeling in the presence of unobserved heterogeneity or dependence between observations. The approach uses interactive latent effects to provide a unified framework for modeling different data structures, including network, time-series cross-sectional and spatial data. Each of these methods is designed with a focus on applied work. Estimation algorithms are presented which are fast enough for applied work and software is either currently available (STM) or in development (LFR). The use of these techniques is illustrated with a range of applications from across political science.
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Smith, Anna Lantz. "Statistical Methodology for Multiple Networks." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492720126432803.

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An, Baoshe. "Poisson approximation in the context of file-merging methodology /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487943610785527.

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Lesser, Elizabeth Rochelle. "A New Right Tailed Test of the Ratio of Variances." UNF Digital Commons, 2016. http://digitalcommons.unf.edu/etd/719.

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It is important to be able to compare variances efficiently and accurately regardless of the parent populations. This study proposes a new right tailed test for the ratio of two variances using the Edgeworth’s expansion. To study the Type I error rate and Power performance, simulation was performed on the new test with various combinations of symmetric and skewed distributions. It is found to have more controlled Type I error rates than the existing tests. Additionally, it also has sufficient power. Therefore, the newly derived test provides a good robust alternative to the already existing methods.
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Kashin, Konstantin Daniel. "Essays on Political Methodology and Data Science." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17464583.

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This collection of six essays makes novel methodological contributions to causal inference, time-series cross-sectional forecasting, and supervised text analysis. The first three essays start from the premise that while randomized experiments are the gold standard for causal claims, randomization is not feasible or ethical for many questions in the social sciences. Researchers have thus devised methods that approximate experiments using nonexperimental control units to estimate counterfactuals. However, control units may be costly to obtain, incomparable to the treated units, or completely unavailable when all units are treated. We challenge the commonplace intuition that control units are necessary for causal inference. We propose conditions under which one can use post-treatment variables to estimate causal effects. At its core, we show when one can obtain identification of causal effects by comparing treated units to other treated units, without recourse to control units. The next two essays demonstrate that the U.S. Social Security Administration's (SSA) forecasting errors were approximately unbiased until about 2000, but then began to grow quickly, with increasingly overconfident uncertainty intervals. Moreover, the errors all turn out to be in the same potentially dangerous direction, each making the Social Security Trust Funds look healthier than they actually are. We also discover the cause of these findings with evidence from a large number of interviews we conducted with participants at every level of the forecasting and policy processes. Finally, the last essay develops a new dataset for studying the influence of business on public policy decisions across the American states. Compiling and digitizing nearly 1,000 leaked legislative proposals made by a leading business lobbying group in the states, along with digitized versions of all state legislation introduced or enacted between 1995 and 2013, we use a two-stage supervised classifier to categorize state bills as either sharing the same underlying concepts or specific language as business-drafted model bills. We find these business-backed bills were more likely to be introduced and enacted by legislatures lacking policy resources, such as those without full-time members and with few staffers.
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Books on the topic "Statistics and methodology"

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Daniel, Wayne W. Business statistics: Basic concepts and methodology. 4th ed. Boston: Houghton Mifflin, 1986.

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Arnold, Barry C., Narayanaswamy Balakrishnan, and Carlos A. Coelho, eds. Methodology and Applications of Statistics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83670-2.

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Biswas, Suddhendu. Topics instatistical methodology. New York: Wiley, 1991.

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Biswas, Suddhendu. Topics in statistical methodology. Edited by Sehgal Vijay Kumar. New York: Wiley, 1991.

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Khuri, André I. Linear model methodology. Boca Raton: Chapman & Hall/CRC, 2010.

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Denman, John. Energy statistics, IEA: Methodology and models. Abu Dhabi: Emirates Center for Strategic Studies and Research, 2001.

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Singh, Yogesh Kumar. Fundamental of research methodology and statistics. New Delhi: New Age International (P) Ltd., Publishers, 2006.

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Khuri, André I. Linear model methodology. Boca Raton: CRC Press, 2010.

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Khuri, André I. Linear model methodology. Boca Raton: Chapman & Hall/CRC, 2010.

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Khuri, André I. Linear model methodology. Boca Raton: Chapman & Hall/CRC, 2010.

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Book chapters on the topic "Statistics and methodology"

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Andonov, Sasho. "Statistics." In Bowtie Methodology, 49–57. Boca Raton : CRC Press, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315158853-3.

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Harpale, Varsha K., and Vinayak K. Bairagi. "Applied Statistics." In Research Methodology, 161–206. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351013277-5.

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Dwyer, Diana, and Jane Scampion. "Methodology and Statistics." In Psychology A Level, 345–74. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13450-2_11.

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Pruzan, Peter. "Uncertainty, Probability and Statistics in Research." In Research Methodology, 207–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27167-5_8.

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van der Laan, Mark J., and James M. Robins. "General Methodology." In Springer Series in Statistics, 102–71. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21700-0_2.

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Weiss, Daniel S., and Kevin L. Delucchi. "Research Methodology and Statistics." In Psychiatry, 2575–92. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118753378.ch131.

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Weiss, Daniel S. "Research Methodology and Statistics." In Psychiatry, 2619–34. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470515167.ch128.

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Dean, Angela, Daniel Voss, and Danel Draguljić. "Response Surface Methodology." In Springer Texts in Statistics, 565–614. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52250-0_16.

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Durham, Todd A., Gary G. Koch, and Lisa M. LaVange. "Introductory Statistics in Medical Research." In Principles of Research Methodology, 207–31. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3360-6_11.

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Braun, Henry. "Research on Statistics." In Methodology of Educational Measurement and Assessment, 179–200. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58689-2_6.

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Conference papers on the topic "Statistics and methodology"

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JONES, SHELTON M., BRIAN C. SUTTON, and KERRIE E. BOYLE. "SURVEY METHODOLOGY FOR STUDYING SUBSTANCE USE PREVENTION PROGRAMS IN SCHOOLS." In Proceedings of Statistics 2001 Canada: The 4th Conference in Applied Statistics. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949531_0013.

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Chopperfield, James, Siu-Ming Tam, and Paul Schubert. "Building expertise in mathematical statistics within the Australian bureau of statistics through partnerships with academics." In Statistics education for Progress: Youth and Official Statistics. International Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.131001.

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Australian Bureau of Statistics recognises the critical importance that its statistical products and services are underpinned by defensive statistical methodology. To this end, the Australian Bureau of Statistics has developed strong partnerships with academics and universities in order to assist with the recruitment of graduates and their development into research leaders in mathematical statisticians. This paper describes the details of the partnerships since 2004 and some of the important benefits.
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Fanta, Petr, and Daniela Horáková. "Design and methodology of collecting data on youth leaving institutional care." In International Days of Statistics and Economics 2019. Libuše Macáková, MELANDRIUM, 2019. http://dx.doi.org/10.18267/pr.2019.los.186.37.

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Motoryn, Ruslan, Tetiana Motoryna, and Kateryna Prykhodko. "Use of official statistical data and methodology in teaching the subject «essential national accounting»." In Advances in Statistics Education: Developments, Experiences, and Assessments. International Association for Statistical Education, 2015. http://dx.doi.org/10.52041/srap.15713.

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Сontent of teaching Essential National Accounting has transition to the International Standards. Statistics have wide spreading all over the globe. Today practically all countries and international organisations rely on the System of National Accounts (SNA) for evaluating economic performance, setting goals, etc. It is necessary to use the specialities of Internet for it learning by universities experience of the world best practice. We elaborated course “Essential National Accounting” for the training of official statisticians and teaching of students of Universities of Economics.We have used the data of Ukrainian Statistical Office, Central Bank of Ukraine and Global Statistics by Internet for the formation of Curricula for specialists on Statistics of the Taras Shevchenko National University of Kyiv and others Universities of Ukraine.
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Nisel, Seyhan, and Rauf Nisel. "Using VIKOR Methodology for Ranking Universities by Academic Performance." In 3rd Annual International Conference on Operations Research and Statistics. Global Science Technology Forum, 2013. http://dx.doi.org/10.5176/2251-1938_ors13.05.

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de Oliveira, Karlson B., Josyleuda Melo Moreira de Oliveira, Raimir Holanda Filho, and Arnaldo D. Belchior. "A New Methodology to Rank Projects Using Multivariate Statistics." In 2007 International Conference on Service Systems and Service Management. IEEE, 2007. http://dx.doi.org/10.1109/icsssm.2007.4280143.

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He, Sen, Glenna Manns, John Saunders, Wei Wang, Lori Pollock, and Mary Lou Soffa. "A statistics-based performance testing methodology for cloud applications." In ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338906.3338912.

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Ferrao, Maria Eugénia. "Integrating statistics: doing research in undergraduate studies." In Statistics education for Progress: Youth and Official Statistics. IASE international Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.13203.

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The paper describes the project Mathematics and Statistics for the Development of Professional Skills. Motivation and integration are keywords of teaching statistics to students who are not primarily interested in statistics. The motivation is based on illustrative problems, questions or examples of the field and real life that can be solved, answered or better understood with the study of the respective statistical content(s). Recently published scientific papers are used as didactic material. In general, the integration occurs throughout the following steps: explaining abstract statistical concepts and methods; applying concepts and methods to the example given as motivation; doing tutorial data analyses with statistical software; replicating data analyses and solving research problems with real data. The pedagogic methodology is based on the mixed uses of lectures and student centered activities. The didactic method exposed allows students an early practice of scientific activities.
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Ponomarenko, 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.

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In the centrally planned economy the main function of official statistics was monitoring of plans’ execution. Hence, official statisticians had to be experts in economics and bookkeeping, like tax inspectors. Russian statistical education was oriented mostly to official needs and statistics was included in the same educational group as economics. Currently, professional requirements for statisticians have changed. Official statistics lost its control function, and the old reporting system is being replaced by sample surveys that are less onerous for respondents and also less expensive. The statistical agency needs more professionals in survey methodology, as well as statistical managers and mathematicians. In 2015, the new professional standard "statistician" was accepted in Russia in line with ISCO 2008. It consists of set of competences for professionals in data collection, processing, analysis and methodology in any field of activity, including business, finance, science, medicine. In 2016, the educational classification of statistics was changed to the same group as mathematics. A new educational standard for statistics was also accepted. This presentation describes part of the reconstructive process.
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Severino, Liliana, and Marta Ruggieri. "Application of ICTS to teaching statistics to non statisticians." In Statistics education for Progress: Youth and Official Statistics. International Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.131107.

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The expansion of the university system coupled with the changes introduced by the new information and communication technologies (NICT), have a direct effect on the traditional methods of education. In fact, ICTS have significantly increased the available information, altering the methodology of statistical treatment. These transformations are progressively being incorporated into the university education, which should take up the challenge of training individuals prepared to efficiently developed its activity in the field of the new information society. This work assesses the experiences in the application of new technologies to the teaching of statistics, describing the role of the software used for practical work and analyzing the possibilities of the Internet in a dual perspective: as a supplement to classroom teaching and as a support for a virtual classroom based on the foregoing of a methodological proposal that tries to adapt the use of technology to different curricular proposals belonging to private and public universities, in different educational establishments, and in situations of marked difference in the amount of students in charge of each professor.
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Reports on the topic "Statistics and methodology"

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Thomas, Douglas S., and Douglas S. Thomas. Methodology for calculating construction industry supply chain statistics. Gaithersburg, MD: National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.sp.1116.

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Abel, Charles E., Barbara A. Tracy, C. L. Vincent, and Robert A. Jensen. Hurricane Hindcast Methodology and Wave Statistics for Atlantic and Gulf Hurricanes from 1956 - 1975. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada207849.

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Goel, Prem K., and T. Ramalingam. The Matching Methodology: Some Statistical Properties. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada177081.

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DEFENSE MANPOWER DATA CENTER ARLINGTON VA. Survey of Active Duty Members: Statistical Methodology Report. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada468103.

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Al Nassir, F., J. Schneider, D. McGrath, and E. Falk. 2014 Service Academy Gender Relations Survey: Statistical Methodology Report. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada612381.

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Fisk, Mark D., Henry L. Gray, and Gary D. McCartor. Statistical Methodology and Assessment of Seismic Event Characterization Capability. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada305487.

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Hurley, Maia, Jeff Schneider, Eric Falk, Carole Massey, David McGrath, Elizabeth P. Van Winkle, and Paul Rosenfeld. 2015 QuickCompass of Sexual Assult-Related Responders: Statistical Methodology Report. Fort Belvoir, VA: Defense Technical Information Center, February 2016. http://dx.doi.org/10.21236/ada630235.

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Friday, Dennis S. Methodology for statistical control of the anechoic chamber field generation system. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.85-3033.

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Gilbert, E. S., W. C. Griffith, and B. A. Carnes. Statistical methodology used in analyses of data from DOE experimental animal studies. Office of Scientific and Technical Information (OSTI), July 1995. http://dx.doi.org/10.2172/95541.

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Fuentes, Anthony, Michelle Michaels, and Sally Shoop. Methodology for the analysis of geospatial and vehicle datasets in the R language. Cold Regions Research and Engineering Laboratory (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42422.

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Abstract:
The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.
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