Готові списки джерел за темами / Multiple statistical analysis

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Добірка наукової літератури з теми "Multiple statistical analysis"

Автор: Grafiati
Опубліковано: 22 лютого 2025

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Статті в журналах з теми "Multiple statistical analysis"

1

Fienberg, Stephen E., Michael M. Meyer, and Stanley S. Wasserman. "Statistical Analysis of Multiple Sociometric Relations." Journal of the American Statistical Association 80, no. 389 (March 1985): 51–67. http://dx.doi.org/10.1080/01621459.1985.10477129.

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2

Teixeira-Pinto, Armando, and Laura Mauri. "Statistical Analysis of Noncommensurate Multiple Outcomes." Circulation: Cardiovascular Quality and Outcomes 4, no. 6 (November 2011): 650–56. http://dx.doi.org/10.1161/circoutcomes.111.961581.

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3

Lenth, Russell, and Søren Højsgaard. "Reproducible statistical analysis with multiple languages." Computational Statistics 26, no. 3 (March 2, 2011): 419–26. http://dx.doi.org/10.1007/s00180-011-0245-5.

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4

Donner, A. "The statistical analysis of multiple binary measurements." Journal of Clinical Epidemiology 41, no. 9 (1988): 899–905. http://dx.doi.org/10.1016/0895-4356(88)90107-2.

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5

Dabrowski, Andre Robert, and David McDonald. "Statistical Analysis of Multiple Ion Channel Data." Annals of Statistics 20, no. 3 (September 1992): 1180–202. http://dx.doi.org/10.1214/aos/1176348765.

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6

Jankovic, Slobodan. "The Multivariate Statistical Analysis – Multiple Linear Regression." International Journal on Biomedicine and Healthcare 10, no. 4 (2022): 173. http://dx.doi.org/10.5455/ijbh.2022.10.173-175.

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Background. When processing the results of observational studies we ave to usemultivariate statistical methods that will examine the simultaneous influence of both independent and confounding variables on the outcome, Objective: The aim of this paper is to further explain how a researcher could decide whether multiple linear regression is suitable statistical option for processing his (her) data, and then how to implement it properly. Methods: This article is a narrative review of literature about logic, assumptions, quality check and interpretation of multiple linear regression. Results: Multiple linear regression is a complex linear equation (model) in which on one side of the equal sign is the absolute value of the dependent variable (i.e., the outcome), and on the other is a sum of additions, of which only one is a constant, and all others are the product of an independent or confounding variable and their coefficients. After checking assumptions and quality of the model, we may decide whether a predictor has significant influence on outcome, or not, and calculate size of this influence. Conclusions: Multiple linear regression is an extremely useful statistical model for explaining the influence of multiple predictors simultaneously on a continuous type dependent variable, but it requires the fulfillment of fairly strict assumptions in order to be used. That's why multiple linear regression should be used only when the conditions are met, otherwise other types of linear and non-linear models whose assumptions are far more lenient should be resorted to. .
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7

Calvo, Borja, and Guzmán Santafé. "scmamp: Statistical Comparison of Multiple Algorithms in Multiple Problems." R Journal 8, no. 1 (2016): 248. http://dx.doi.org/10.32614/rj-2016-017.

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8

ÖZKAYA, Güven, Özlem TAŞKAPILIOĞLU, and İlker ERCAN. "Statistical Shape Analysis of Handwriting of Patients with Multiple Sclerosis." Turkiye Klinikleri Journal of Medical Sciences 32, no. 6 (2012): 1702–9. http://dx.doi.org/10.5336/medsci.2012-30233.

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Greenwood, Jeremy J. D. "Statistical Analysis of Experiments Conducted at Multiple Sites." Oikos 69, no. 2 (March 1994): 334. http://dx.doi.org/10.2307/3546155.

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Salo, J., H. M. El-Sallabi, and P. Vainikainen. "Statistical Analysis of the Multiple Scattering Radio Channel." IEEE Transactions on Antennas and Propagation 54, no. 11 (November 2006): 3114–24. http://dx.doi.org/10.1109/tap.2006.883964.

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Дисертації з теми "Multiple statistical analysis"

1

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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2

DI, BRISCO AGNESE MARIA. "Statistical Network Analysis: a Multiple Testing Approach." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/96090.

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The problem of identifying connections between nodes in a network model is of fundamental importance in the analysis of brain networks because each node represents a specific brain region that can potentially be connected to other brain regions by means of functional relations; the dynamical behavior of each node can be quantified by adopting a correlation measure among time series. In this contest, the whole set of links between nodes in a network can be represented by means of an adjacency matrix with high dimension, that can be obtained by performing a huge number of simultaneous tests on correlations. In this regard, the Thesis has dealt with the problem of multiple testing in a Bayesian perspective, by examining in depth the “Bayesian False Discovery Rate” (FDR), already defined in Efron, and by introducing the “Bayesian Power” (BP). The behavior of the FDR and BP estimators has been analyzed both with asymptotic theory and with Monte Carlo simulations; furthermore, it has been investigated the robustness of the proposed estimators by simulating specific pattern of dependencies among the p-values associated to the multiple comparisons. Such a multiple testing approach, that allows to control both FDR and BP, has been applyied to a dataset provided by the Milan Center for Neuroscience (NeuroMi). Once selected a sample of 70 participants, classified properly into young subjects and elderly subjects, subject by subject network models have been constructed in order to verify two alternative theories about changes in the pattern of functional connectivity as time goes by, namely the de-differentiation hypothesis versus the localization hypothesis. This objective has been achieved by selecting some proper network measures in order to verify the original hypotheses about the pattern of functional connectivity in the elderly’s group and in the group of young subjects, and by constructing some ad-hoc measures.
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3

Liu, Wei. "Analysis of power functions of multiple comparisons tests." Thesis, University of Bath, 1990. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235586.

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Zain, Zakiyah. "Combining multiple survival endpoints within a single statistical analysis." Thesis, Lancaster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618302.

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The aim of this thesis is to develop methodology for combining multiple endpoints within a single statistical analysis that compares the responses of patients treated with a novel treatment with those of control patients treated conventionally. The focus is on interval-censored bivariate survival data, and five real data sets from previous studies concerning multiple responses are used to illustrate the techniques developed. The background to survival analysis is introduced by a general description of survival data, and an overview of existing methods and underlying models is included. A review is given of two of the most popular survival analysis methods, namely the logrank test and Cox's proportional hazards model. The global score test methodology for combining multiple end points is described in detail, and application to real data demonstrates its benefits. The correlation between two score statistics arising from bivariate interval-censored survival data is the core of this research. The global score test methodology is extended to the case of bivariate interval-censored survival data and a complementary log-log link is applied to derive the covariance and the correlation between the two score statistics. A number of common scenarios are considered in this investigation and the accuracy of the estimator is evaluated by means of extensive simulations. An established method, namely the approach of Wei, Lin and Weissfeld, is examined and compared with the proposed method using both real and simulated data. It is concluded that our method is accurate, consistent and comparable to the competitor. This study marked the first successful development of the global score test methodology for bivariate survival data, employing a new approach to the derivation of the covariance between two score statistics on the basis of an interval-censored model. Additionally. the relationship between the jackknife technique and the Wei, Lin and Weissfeld method has been clarified.
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5

李志傑 and Chi-kit Li. "The statistical analysis of multi-way and multiple compositions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1986. http://hub.hku.hk/bib/B31230672.

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Li, Chi-kit. "The statistical analysis of multi-way and multiple compositions /." [Hong Kong] : University of Hong Kong, 1986. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12323652.

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7

Nashimoto, Kane. "Multiple comparison techniques for order restricted models /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144445.

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8

Bianchini, Germán. "Wildland Fire Prediction based on Statistical Analysis of Multiple Solutions." Doctoral thesis, Universitat Autònoma de Barcelona, 2006. http://hdl.handle.net/10803/5762.

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Анотація:
En diferentes áreas científicas, el uso de modelos para representar sistemas físicos se ha tornado una tarea habitual. Estos modelos reciben parámetros de entradas representando condiciones particulares y proveen una salida que representa la evolución del sistema. Usualmente, dichos modelos están integrados en herramientas de simulación que pueden ser ejecutadas en una computadora.
Un caso particular donde los modelos resultan muy útiles es la predicción de la propagación de Incendios Forestales. Los incendios se han vuelto un gran peligro que cada año provoca grandes pérdidas desde el punto de vista ambiental, económico, social y humano. En particular, las estaciones secas y calurosas incrementan seriamente el riesgo de incendios en el área Mediterránea. Por lo tanto, el uso de modelos es relevante para estimar el riesgo de incendios y predecir el comportamiento de los mismos.
Sin embargo, en muchos casos, los modelos presentan una serie de limitaciones. Estas se relacionan con la necesidad de un gran número de parámetros de entrada. En muchos casos, tales parámetros presentan cierto grado de incertidumbre debido a la imposibilidad de medirlos en tiempo real, y deben ser estimados a partir de datos indirectas. Además, en muchos casos estos modelos no se pueden resolver analíticamente y deben ser calculados aplicando métodos numéricos que son una aproximación de la realidad.
Se han desarrollado diversos métodos basados en asimilación de datos para optimizar los parámetros de entrada. Comúnmente, estos métodos operan sobre un gran número de parámetros de entrada y, a través de optimización, se enfocan en hallar un único conjunto de parámetros que describa de la mejor forma posible el comportamiento previo. Por lo tanto, es de esperar que el mismo conjunto de valores pueda ser usado para describir el futuro inmediato.
Sin embargo, esta clase de predicción se basa en un solo conjunto de parámetros y, por lo que se explicó, debido a aquellos parámetros que presentan un comportamiento dinámico, los valores optimizados pueden no resultar adecuados para el siguiente paso.
El presente trabajo propone un método alternativo. Nuestro sistema, llamado Sistema Estadístico para la Gestión de Incendios Forestales, se basa en conceptos estadísticos. Su objetivo es hallar un patrón del comportamiento del incendio, independientemente de los valores de los parámetros. En este método, cada parámetro es representado mediante un rango de valores y una cardinalidad. Se generan todos los posibles escenarios considerando todas las posibles combinaciones de los valores de los parámetros de entrada, y entonces se evalúa la propagación para cada caso. Los resultados son agregados estadísticamente para determinar la probabilidad de que cada área se queme. Esta agregación se utiliza para predecir el área quemada en el siguiente paso.
Para validar nuestro método, usamos un conjunto de quemas reales prescritas. Además, comparamos nuestro método contra otros dos. Uno de estos dos métodos fue implementado para este trabajo: GLUE (Generalized Likelihood Uncertainty Estimation). Dicho método corresponde a una adaptación de un sistema hidrológico. El otro caso (Método Evolutivo) es un algoritmo genético previamente desarrollado e implementado también por nuestro equipo de investigación.
Los sistemas propuestos requieren un gran número de simulaciones, razón por la cual decidimos usar un esquema paralelo para implementarlos. Esta forma de trabajo difiere del esquema tradicional de teoría y experimentación, lo cual es la forma común de la ciencia y la ingeniería. El cómputo científico está en continua expansión, principalmente a través del análisis de modelos matemáticos implementados en computadores. Los científicos e ingenieros desarrollan programas de computador que modelan los sistemas bajo estudio. Esta metodología está creando una nueva rama de la ciencia basada en métodos computacionales, la cual crece de forma acelerada. Esta aproximación es llamada Ciencia Computacional.
In many different scientific areas, the use of models to represent the physical system has become a common strategy. These models receive some input parameters representing the particular conditions and provide an output representing the evolution of the system. Usually, these models are integrated in simulation tools that can be executed on a computer.
A particular case where models are very useful is the prediction of Forest Fire propagation. Forest fire is a very significant hazard that every year provokes huge looses from the environmental, economical, social and human point of view. Particularly dry and hot seasons seriously increase the risk of forest fires in the Mediterranean area. Therefore, the use of models is very relevant to estimate fire risk, and predict fire behavior.
However, in many cases models present a series of limitations. Usually, such limitations are due to the need of a large number of input parameters. In many cases such parameters present some uncertainty due to the impossibility to measure all of them in real time and must be estimated from indirect measurements. Moreover, in most cases these models cannot be solved analytically and must be solved applying numerical methods that are only an approach to reality (still without considering the limitations that present the translations of these solutions when they are carried out by means of computers).
Several methods based on data assimilation have been developed to optimize the input parameters. In general, these methods operate over a large number of input parameters, and, by mean of some kind of optimization, they focus on finding a unique parameter set that would describe the previous behavior in the best form. Therefore, it is hoped that the same set of values could be used to describe the immediate future.
However, this kind of prediction is based on a single value of parameters and, as it has been said above, for those parameters that present a dynamic behavior the new optimized values cannot be adequate for the next step.
The objective of this work is to propose an alternative method. Our method, called Statistical System for Forest Fire Management, is based on statistical concepts. Its goal is to find a pattern of the forest fire behavior, independently of the parameters values. In this method, each parameter is represented by a range of values with a particular cardinality for each one of them. All possible scenarios considering all possible combinations of input parameters values are generated and the propagation for each scenario is evaluated. All results are statically aggregated to determine the burning probability of each area. This aggregation is used to predict the burned area in the next step.
To validate our method, we use a set of real prescribed burnings. Furthermore, we compare our method against two other methods. One of these methods was implemented by us for this work: GLUE (Generalized Likelihood Uncertainty Estimation). It corresponds to an adaptation of a hydrological method. The other method (Evolutionary method) is a genetic algorithm previously developed and implemented by our research team.
The proposed system requires a large number of simulations, a reason why we decide to use a parallel-scheme to implement them. This way of working is different from traditional scheme of theory and experiment, which is the common form of science and engineering. The scientific computing approach is in continuous expansion, mainly through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs that model the systems under study. This methodology is creating a new branch of science based on computational methods that is growing very fast. This approach is called Computational Science.
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Miller, Christopher Ryan 'Red'. "Statistical analysis of wireless networks predicting performance in multiple environments /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Jun%5FMiller.pdf.

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Анотація:
Thesis (M.S. in Applied Science (Operations Research))--Naval Postgraduate School, June 2006.
Thesis Advisor(s): David Annis. "June 2006." Includes bibliographical references (p.57). Also available in print.
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Miller, Christopher Ryan. "Statistical analysis of wireless networks predicting performance in multiple environments." Thesis, Monterey, California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2817.

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With the advent of easily accessible, deployable, and usable 802.11 technology, users can connect and network with practically any infrastructure that exists today. Due to that simplicity and ease of use, it only seems logical that the military and tactical users should also employ these technologies. The questions regarding 802.11 network performances in a hostile and signal-unfriendly environment (i.e., high temperature and high humidity) have yet to be answered. The goal of this thesis is to quantify 802.11 network capabilities, in terms of throughput, while it is employed in those areas. Ultimately, the objective is to produce statistical models able to represent any variations in the 802.11 signals and network due to those environmental factors.
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Книги з теми "Multiple statistical analysis"

1

Booth, Gordon D. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1994.

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2

Stanley, Feldman, ed. Multiple regression in practice. Beverly Hills: Sage Publications, 1985.

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3

Jaccard, James. Interaction effects in multiple regression. 2nd ed. Thousand Oaks, Calif: Sage Publications, 2003.

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4

Robert, Turrisi, and Wan Choi K, eds. Interaction effects in multiple regression. Newbury Park: Sage Publications, 1990.

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5

Orme, John G. Multiple regression with discrete dependent variables. New York: Oxford University Press, 2009.

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6

1923-, Cohen Jacob, and Cohen Jacob 1923-, eds. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. Mahwah, N.J: L. Erlbaum Associates, 2003.

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7

Bechhofer, Robert E. Design and analysis of experiments for statistical selection, screening, and multiple comparisons. New York: Wiley, 1995.

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8

Sheldon, Zedeck, ed. Data analysis for research designs: Analysis-of-variance and multiple regression/correlation approaches. New York: W.H. Freeman, 1989.

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9

K, Wan Choi, ed. LISREL approaches to interaction effects in multiple regression. Thousand Oaks, Calif: Sage Publications, 1996.

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10

Lin, Nancy Pei-ching. A new approach to sample size determination of replicated Latin square designs and analysis of multiple comparison procedures. [Tʻai-pei shih: Ching sheng wen wu kung ying kung ssu, 1985.

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Частини книг з теми "Multiple statistical analysis"

1

Heiberger, Richard M., and Burt Holland. "Multiple Comparisons." In Statistical Analysis and Data Display, 199–233. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_7.

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Heiberger, Richard M., and Burt Holland. "Multiple Comparisons." In Statistical Analysis and Data Display, 155–85. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_7.

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Armstrong, Richard A., and Anthony C. Hilton. "Multiple Linear Regression." In Statistical Analysis in Microbiology: Statnotes, 127–33. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch25.

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Armstrong, Richard A., and Anthony C. Hilton. "Stepwise Multiple Regression." In Statistical Analysis in Microbiology: Statnotes, 135–38. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch26.

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Gatnar, Eugeniusz. "Fusion of Multiple Statistical Classifiers." In Data Analysis, Machine Learning and Applications, 19–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9_3.

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Heiberger, Richard M., and Burt Holland. "Multiple Regression—Regression Diagnostics." In Statistical Analysis and Data Display, 345–75. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_11.

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Heiberger, Richard M., and Burt Holland. "Multiple Regression—Regression Diagnostics." In Statistical Analysis and Data Display, 297–327. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_11.

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Little, Roderick J. A., and Donald B. Rubin. "Bayes and Multiple Imputation." In Statistical Analysis with Missing Data, 200–220. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119013563.ch10.

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Pardo, Scott. "Multiplicity and Multiple Comparisons." In Statistical Analysis of Empirical Data, 33–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43328-4_4.

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Rotello, Caren M., Jerome L. Myers, Arnold D. Well, and Robert F. Lorch. "Introduction to Multiple Regression." In Research Design and Statistical Analysis, 538–62. 4th ed. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003453550-24.

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Тези доповідей конференцій з теми "Multiple statistical analysis"

1

Ortiz-Bustos, Josefa, Helena Pérez del Pulgar, Isabel del Hierro, and Sanjiv Prashar. "STATISTICAL ANALYSIS OF MULTIPLE-CHOICE QUESTIONS IN CHEMISTRY EDUCATION." In 17th annual International Conference of Education, Research and Innovation, 6785–88. IATED, 2024. https://doi.org/10.21125/iceri.2024.1637.

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Kamoljitprapa, Pianpool, Sirikanlaya Sookkhee, and Orathai Polsen. "Statistical Analysis for Genome Data Based on Multiple SNPs Using Kernel Machine Based Test." In 2024 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 262–66. IEEE, 2024. https://doi.org/10.1109/ri2c64012.2024.10784332.

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Sinha, Debjit, Vasant Rao, Chaitanya Peddawad, Michael Wood, Jeffrey Hemmett, Suriya Skariah, and Patrick Williams. "Statistical Timing Analysis considering Multiple-Input Switching." In 2020 57th ACM/IEEE Design Automation Conference (DAC). IEEE, 2020. http://dx.doi.org/10.1109/dac18072.2020.9218601.

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Ray, Priyadip, and Lawrence Carin. "Nonparametric Bayesian factor analysis of multiple time series." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967742.

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Chen, Shengchang, Shujing Lu, Ying Wen, and Yue Lu. "Using multiple sequence alignment and statistical language model to integrate multiple Chinese address recognition outputs." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333742.

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6

Liu, Jian, Tao Peng, Qingyi Quan, and Lili Cao. "Performance analysis of the Statistical Priority-Based Multiple Access." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322509.

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7

Czajka, Adam, and Kevin W. Bowyer. "Statistical analysis of multiple presentation attempts in iris recognition." In 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). IEEE, 2015. http://dx.doi.org/10.1109/cybconf.2015.7175982.

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8

Houston, Eric, Stephen Parker, and Douglas Keene. "Statistical Analysis of Multiple Encoded Ultrasonic Testing Data Sets." In ASME 2024 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/pvp2024-125166.

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Abstract Localized corrosion, commonly referred to as “pitting,” in carbon steel piping systems has a significant financial and regulatory impact on commercial nuclear power plants. Quantifying the level of degradation and identifying locations which may violate the minimum wall thickness requirements or leak is an ongoing task at most, if not all, sites. Utilizing historic inspection data to predict the current and future state of a piping system is challenging without a robust definition of a pit. Work has recently been conducted by Parker, Roukema, and Patten to define “pitting” in geometric terms as it relates to localized wall loss due to corrosion in carbon steel piping [1]. The authors presented a novel approach to identifying the non-uniform pipe wall as a “pit” at discrete locations. As the authors note, the limited data set restricted the applicability of the paper’s conclusions to only the Service Water system for which the data was obtained. This paper expands upon the previous work by increasing the number of data sets evaluated and includes additional sites with varying water chemistry and operating history. The paper utilizes a slightly simplified analysis of localized corrosion to identify the number of pits within an encoded ultrasonic thickness data set. The objective of this paper is to determine if the set of geometric characteristics proposed in the previous work can be reasonably applied to a more diverse set of piping systems.
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Mahmood, Khalid, Syed Muhammad Asad, Muhammad Moinuddin, Azzedine Zerguine, and S. Paul. "Statistical analysis of multiple access interference in Rayleigh fading environment for MIMO CDMA systems." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884663.

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Fu, Jinxin, and H. Daniel Ou-Yang. "Statistical Analysis of Transiently Trapped Multiple Nanoparticles in Optical Confinement." In JSAP-OSA Joint Symposia. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/jsap.2013.19a_d5_1.

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Звіти організацій з теми "Multiple statistical analysis"

1

Colbert, Mark A. Statistical Analysis of Multiple Choice Testing. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada407446.

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2

Zhan, Peng. Statistical Analysis and Data Visualization in R. Instats Inc., 2022. http://dx.doi.org/10.61700/dizyg5iq1mqj5469.

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This seminar provides a gentle introduction to R for those looking to use it for applied statistics and visualisation, covering a range of essential topics on basic data manipulation, regression analysis, and visualizing data and results. Examples from multiple fields are used to demonstrate the applications. An official Instats certificate of completion is provided at the conclusion of the seminar.
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3

Johnson, Jeffrey O., John W. Raby, and David I. Knapp. Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada600391.

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4

Zaninotto, Paola. Multiple Imputation by Chained Equations (MICE). Instats Inc., 2024. https://doi.org/10.61700/1tr36kp5gwa5b1858.

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This seminar provides a comprehensive introduction to Multiple Imputation by Chained Equations (MICE) in R, offering researchers advanced skills to effectively handle missing data using robust statistical techniques. Led by Dr. Paola Zaninotto from University College London, participants will gain proficiency in applying MICE across various research disciplines, enhancing data analysis and interpretation capabilities.
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5

Moeyaert, Mariola. Introduction to Meta-Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/9egp6tqy3koga469.

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This seminar provides hands-on instruction covering the process of conducting a meta-analysis, from the planning stage through the selection of appropriate statistical techniques, the issues involved in analyzing data, and the interpretation of results. During the seminar you will learn how to use meta-analytic techniques to combine evidence across different research studies; integrate multiple studies into a single statistical framework; obtain precise and generalizable estimates of effect sizes; and explain differences arising from conflicting study results. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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6

Moeyaert, Mariola. Introduction to Meta-Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/z1ui6nlaom67q469.

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Анотація:
This seminar provides hands-on instruction covering the process of conducting a meta-analysis, from the planning stage through the selection of appropriate statistical techniques, the issues involved in analyzing data, and the interpretation of results. During the seminar you will learn how to use meta-analytic techniques to combine evidence across different research studies; integrate multiple studies into a single statistical framework; obtain precise and generalizable estimates of effect sizes; and explain differences arising from conflicting study results. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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7

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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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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Tarko, Andrew P., Mario Romero, Cristhian Lizarazo, and Paul Pineda. Statistical Analysis of Safety Improvements and Integration into Project Design Process. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317121.

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RoadHAT is a tool developed by the Center for Road Safety and implemented for the INDOT safety management practice to help identify both safety needs and relevant road improvements. This study has modified the tool to facilitate a quick and convenient comparison of various design alternatives in the preliminary design stage for scoping small and medium safety-improvement projects. The modified RoadHAT 4D incorporates a statistical estimation of the Crash Reduction Factors based on a before-and-after analysis of multiple treated and control sites with EB correction for the regression-to-mean effect. The new version also includes the updated Safety Performance Functions, revised average costs of crashes, and the comprehensive table of Crash Modification Factors—all updated to reflect current Indiana conditions. The documentation includes updated Guidelines for Roadway Safety Improvements. The improved tool will be implemented at a sequence of workshops for the final end users and preceded with a beta-testing phase involving a small group of INDOT engineers.
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Pălici, Bogdan, Alin Savu, Maria Trifon, Cristian Georgescu, Cătălin Toma, Alexandru Mihăilescu, and Gabriel Simion. Mapping - Interactive Tool for Exploring Statistical Data about Cultural Infrastructure in Romania. National Institute for Cultural Research and Training, 2021. http://dx.doi.org/10.61789/mod.cdi.crtg.en.21.

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The Cartographic Module is an instrument for data selection, structuring, visualisation and analysis. It provides for multiple selection functionalities both at geographical area level (county, locality) and at data level (category of indicators and indicators). Three types of dynamic graphs are available on the indicator pages: comparison/evolution, ranking and frequency.
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10

Tosi, R., R. Codina, J. Principe, R. Rossi, and C. Soriano. D3.3 Report of ensemble based parallelism for turbulent flows and release of solvers. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.06.

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In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel with different initial conditions. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. Its error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time in order to minimize the initialization bias, accompanied by strategies to reduce simulation cost. The framework is validated on two very high Reynolds number obstacle problems of wind engineering interest in a high performance computing environment.
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