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Journal articles on the topic 'Directional data'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Boulerice, Bernard, and Gilles R. Ducharme. "Decentered directional data." Annals of the Institute of Statistical Mathematics 46, no. 3 (September 1994): 573–86. http://dx.doi.org/10.1007/bf00773518.

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2

Ahmed, Mohammed Imran Basheer, Rim Ali Zaghdoud, Mohammed Salih Ahmed, Mousa Alrabeea, Abdullatif Alsuwaiti, Nawaf Alzaid, Ahmed Alyousef, et al. "Intelligent Directional Survey Data Analysis to Improve Directional Data Acquisition." Mathematical Modelling of Engineering Problems 10, no. 2 (April 28, 2023): 482–90. http://dx.doi.org/10.18280/mmep.100214.

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3

Novrianti, Novrianti, Rycha Melisa, and Rafhie Adrian. "Kick-Off Point (KOP) and End of Buildup (EOB) Data Analysis in Trajectory Design." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 2 (June 1, 2017): 133. http://dx.doi.org/10.24273/jgeet.2017.2.2.302.

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Well X is a development well which is directionally drilled. Directional drilling is choosen because the coordinate target of Well X is above the buffer zone. The directional track plan needs accurate survey calculation in order to make the righ track for directional drilling. There are many survey calculation in directional drilling such as tangential, underbalance, average angle, radius of curvature, and mercury method. Minimum curvature method is used in this directional track plan calculation. This method is used because it gives less error than other method. Kick-Off Point (KOP) and End of Buildup (EOB) analysis is done at 200 ft, 400 ft, and 600 ft depth to determine the trajectory design and optimal inclination. The hole problem is also determined in this trajectory track design. Optimal trajectory design determined at 200 ft depth because the inclination below 35º and also already reach the target quite well at 1632.28 ft TVD and 408.16 AHD. The optimal inclination at 200 ft KOP depth because the maximum inclination is 18.87º which is below 35º. Hole problem will occur if the trajectory designed at 600 ft. The problems are stuck pipe and the casing or tubing will not able to bend.

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4

Mardia, K. V. "Directional data analysis:an overview." Journal of Applied Statistics 15, no. 2 (January 1988): 115–22. http://dx.doi.org/10.1080/02664768800000018.

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5

Jupp, P. E. "Residuals for directional data." Journal of Applied Statistics 15, no. 2 (January 1988): 137–47. http://dx.doi.org/10.1080/02664768800000021.

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6

Hoffmann, Dorothea, Bill Palmer, and Alice Gaby. "Geocentric directional systems in Australia: a typology." Linguistics Vanguard 8, s1 (January 1, 2022): 67–89. http://dx.doi.org/10.1515/lingvan-2021-0063.

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Abstract This paper presents the results of a survey of geocentric directional systems across Australia using published and unpublished material as well as fieldwork data, providing the first systematic overview of such systems in Australia. The 116 sampled varieties, spoken across diverse landscapes, exhibit variation within and across languages. Many make use of more than one directional system. This paper sets out to create a systematic typological overview of geocentric directionals in Australia taking into account cultural and topographic salience, revisiting existing classifications of directional systems.

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7

Andersson, Fredrik, Adriana Citlali Ramírez, Torgeir Wiik, and Viktor V. Nikitin. "Directional interpolation of multicomponent data." Geophysical Prospecting 65, no. 5 (February 1, 2017): 1246–63. http://dx.doi.org/10.1111/1365-2478.12478.

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8

Lund, Ulric. "Cluster analysis for directional data." Communications in Statistics - Simulation and Computation 28, no. 4 (January 1999): 1001–9. http://dx.doi.org/10.1080/03610919908813589.

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9

Grogan, Mairéad, and Rozenn Dahyot. "Shape registration with directional data." Pattern Recognition 79 (July 2018): 452–66. http://dx.doi.org/10.1016/j.patcog.2018.02.021.

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10

Wang, Guochang, Fode Zhang, and Heng Lian. "Directional regression for functional data." Journal of Statistical Planning and Inference 204 (January 2020): 1–17. http://dx.doi.org/10.1016/j.jspi.2019.03.011.

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11

Rao Jammalamadaka, S., and Ashis SenGupta. "Predictive inference for directional data." Statistics & Probability Letters 40, no. 3 (October 1998): 247–57. http://dx.doi.org/10.1016/s0167-7152(98)00101-1.

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12

DUCHARME, GILLES R., and PHILIP MILASEVIC. "Spatial median and directional data." Biometrika 74, no. 1 (1987): 212–15. http://dx.doi.org/10.1093/biomet/74.1.212.

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13

Ley, Christophe, Yvik Swan, and Thomas Verdebout. "Efficient ANOVA for directional data." Annals of the Institute of Statistical Mathematics 69, no. 1 (August 15, 2015): 39–62. http://dx.doi.org/10.1007/s10463-015-0533-x.

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14

VROLIX, N. "Directional coronary atherectony: Histopathological data." Journal of Molecular and Cellular Cardiology 23 (July 1991): S116. http://dx.doi.org/10.1016/0022-2828(91)90862-g.

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15

Mikami, A., W. T. Newsome, and R. H. Wurtz. "Motion selectivity in macaque visual cortex. II. Spatiotemporal range of directional interactions in MT and V1." Journal of Neurophysiology 55, no. 6 (June 1, 1986): 1328–39. http://dx.doi.org/10.1152/jn.1986.55.6.1328.

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We measured the spatial and temporal limits of directional interactions for 105 directionally selective middle temporal (MT) neurons and 26 directionally selective striate (V1) neurons. Directional interactions were measured using sequentially flashed stimuli in which the spatial and temporal intervals between stimuli were systematically varied over a broad range. A direction index was employed to determine the strength of directional interactions for each combination of spatial and temporal intervals tested. The maximum spatial interval for which directional interactions occurred in a particular neuron was positively correlated with receptive-field size and with retinal eccentricity in both MT and V1. The maximum spatial interval was, on average, three times as large in MT as in V1. The maximum temporal interval for which we obtained directional interactions was similar in MT and V1 and did not vary with receptive-field size or eccentricity. The maximum spatial interval for directional interactions as measured with flashed stimuli was positively correlated with the maximum speed of smooth motion that yielded directional responses. MT neurons were directionally selective for higher speeds than were V1 neurons. These observations indicate that the large receptive fields found in MT permit directional interactions over longer distances than do the more limited receptive fields of V1 neurons. A functional advantage is thereby conferred on MT neurons because they detect directional differences for higher speeds than do V1 neurons. Recent psychophysical studies have measured the spatial and temporal limits for the perception of apparent motion in sequentially flashed visual displays. A comparison of the psychophysical results with our physiological data indicates that the spatiotemporal limits for perception are similar to the limits for direction selectivity in MT neurons but differ markedly from those for V1 neurons. These observations suggest a correspondence between neuronal responses in MT and the short-range process of apparent motion.

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16

Deo, M. C., and R. Burrows. "EXTREME WAVE PREDICTION USING DIRECTIONAL DATA." Coastal Engineering Proceedings 1, no. 20 (January 29, 1986): 11. http://dx.doi.org/10.9753/icce.v20.11.

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Potential inconsistencies in the predictions of long term wave heights can be experienced as a result of different methods of analysis possible when using directional wave data. This paper attempts to illustrate some of them. It involves analysis of two sets of directional wave data - one froa a coastal location in the Irish Sea and another from an offshore location in the North Sea. An attempt is made to eliminate the discrepancies between the long term return-value wave height predictions based upon the conditional height distributions associa ted with different direction sectors and those derived from the oonl-directional data set.

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17

Brunner, Lawrence J., and Albert Y. Lo. "Nonparametric Bayes methods for directional data." Canadian Journal of Statistics 22, no. 3 (September 1994): 401–12. http://dx.doi.org/10.2307/3315601.

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18

Álvarez-Esteban, Pedro C., and Luis A. García-Escudero. "Robust clustering of functional directional data." Advances in Data Analysis and Classification 16, no. 1 (December 9, 2021): 181–99. http://dx.doi.org/10.1007/s11634-021-00482-3.

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AbstractA robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introduced which allows including controlled time warping in that robust clustering procedure to detect typical “templates”. The proposed methodology is illustrated in a real data analysis problem where it is applied to cluster aircraft trajectories.

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19

Bagchi, Partha. "Empirical Bayes estimation in directional data." Journal of Applied Statistics 21, no. 4 (January 1994): 317–26. http://dx.doi.org/10.1080/757583874.

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20

Fisher, Nicholas I., and Peter Hall. "Bootstrap Confidence Regions for Directional Data." Journal of the American Statistical Association 84, no. 408 (December 1989): 996–1002. http://dx.doi.org/10.1080/01621459.1989.10478864.

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21

SER, Gazel. "Directional Data Analysis and An Application." Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi 24, no. 2 (June 1, 2014): 121–26. http://dx.doi.org/10.29133/yyutbd.235925.

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22

Bagchi, Parthasarathy, and Irwin Guttman. "Spuriosity and outliers in directional data." Journal of Applied Statistics 17, no. 3 (January 1990): 341–50. http://dx.doi.org/10.1080/02664769000000006.

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23

Yang, Miin-Shen, and Jinn-Anne Pan. "On fuzzy clustering of directional data." Fuzzy Sets and Systems 91, no. 3 (November 1997): 319–26. http://dx.doi.org/10.1016/s0165-0114(96)00157-1.

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24

Dai, Wenlin, and Marc G. Genton. "Directional outlyingness for multivariate functional data." Computational Statistics & Data Analysis 131 (March 2019): 50–65. http://dx.doi.org/10.1016/j.csda.2018.03.017.

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25

DUCHARME, GILLES R., MYOUNGSHIC JHUN, JOSEPH P. ROMANO, and KINH N. TRUONG. "Bootstrap confidence cones for directional data." Biometrika 72, no. 3 (1985): 637–45. http://dx.doi.org/10.1093/biomet/72.3.637.

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26

Ko, Daijin, and Peter Guttorp. "Robustness of Estimators for Directional Data." Annals of Statistics 16, no. 2 (June 1988): 609–18. http://dx.doi.org/10.1214/aos/1176350822.

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27

Pandolfo, Giuseppe, and Antonio D’Ambrosio. "Depth-based classification of directional data." Expert Systems with Applications 169 (May 2021): 114433. http://dx.doi.org/10.1016/j.eswa.2020.114433.

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28

Li, Jingwen, and Ser Aik Quek. "Locating a target from directional data." Naval Research Logistics 45, no. 4 (June 1998): 353–64. http://dx.doi.org/10.1002/(sici)1520-6750(199806)45:4<353::aid-nav2>3.0.co;2-4.

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29

Oden, Neal L. "Assessing directional effects in spatial data." Statistics in Medicine 12, no. 19-20 (October 1993): 1795–805. http://dx.doi.org/10.1002/sim.4780121907.

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30

Pandolfo, Giuseppe, Davy Paindaveine, and Giovanni C. Porzio. "Distance-based depths for directional data." Canadian Journal of Statistics 46, no. 4 (December 2018): 593–609. http://dx.doi.org/10.1002/cjs.11479.

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31

Tarasevicius, D. "UNI-DIRECTIONAL AND BI-DIRECTIONAL LSTM COMPARISON ON SENSOR BASED SWIMMING DATA." International Journal of Advanced Research 8, no. 5 (May 31, 2020): 735–41. http://dx.doi.org/10.21474/ijar01/10982.

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32

Lawson, Andrew, G. J. G. Upton, and B. Fingleton. "Spatial Data Analysis: Volume 2 Categorical and Directional Data." Statistician 40, no. 3 (1991): 354. http://dx.doi.org/10.2307/2348302.

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33

Agostinelli, C., and M. Romanazzi. "Nonparametric analysis of directional data based on data depth." Environmental and Ecological Statistics 20, no. 2 (August 11, 2012): 253–70. http://dx.doi.org/10.1007/s10651-012-0218-z.

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34

Kalina, Jan. "An application of directional quantiles to economic data with a multivariate response." Serbian Journal of Management 15, no. 2 (2020): 193–203. http://dx.doi.org/10.5937/sjm15-22671.

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Quantile regression represents a popular and useful methodology for modeling quantiles of a response variable based on one or more independent variables. Directional quantiles represent an available extension to the linear regression model with a multivariate response. However, we are not aware of any application of directional quantiles to real data in the literature. An illustration of directional quantiles to an economic dataset is presented in this paper, particularly a modeling of a two-dimensional response in the classical Engel's dataset on household consumption from the 19th century. The results reveal the directional quantiles to yield meaningful results. They order individual observations according to their depth, i.e. from the most central to the most outlying. We compare their result with those of a (more standard) outlier detection. On the whole, we perceive directional quantiles as a potentially useful tool for the analysis of data, if accompanied by a thorough analysis by standard tools.

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35

Wilson, W. Brett. "Data Buoy Aerodynamics." Marine Technology and SNAME News 30, no. 01 (January 1, 1993): 51–60. http://dx.doi.org/10.5957/mt1.1993.30.1.51.

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The National Data Buoy Center (NDBC) operates moored data buoys in waters around the United States, providing highly valuable environmental observations in real time from remote marine regions. Key among these observations are wind speed and direction, and, for that purpose, NDBC uses duplicate anemometers on each buoy for reasons of redundancy and data quality. A significant portion of the NDBC buoy array also measures the directional wave spectrum. Buoys so equipped, when also fitted with a fin on the mast, can produce wind speed and direction from the directional wave system output; however, the results, while good, have not been of sufficient accuracy to be of direct use meteorologically. It is postulated that better mast designs could reduce this inaccuracy. Toward that objective, this paper describes an analytical aerodynamic study, employing a custom-written numerical model, of existing and improved buoy mast configurations.

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36

Fisher, Nicholas I., and Peter Hall. "Correction: Bootstrap Confidence Regions for Directional Data." Journal of the American Statistical Association 85, no. 410 (June 1990): 608. http://dx.doi.org/10.2307/2289832.

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37

Selassie, D., B. Heller, and J. Heer. "Divided Edge Bundling for Directional Network Data." IEEE Transactions on Visualization and Computer Graphics 17, no. 12 (December 2011): 2354–63. http://dx.doi.org/10.1109/tvcg.2011.190.

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38

Boente, Graciela, Daniela Rodriguez, and Wenceslao González Manteiga. "Goodness-of-fit Test for Directional Data." Scandinavian Journal of Statistics 41, no. 1 (May 29, 2013): 259–75. http://dx.doi.org/10.1111/sjos.12020.

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39

Moshfeghi, M. "Directional interpolation for magnetic resonance angiography data." IEEE Transactions on Medical Imaging 12, no. 2 (June 1993): 366–79. http://dx.doi.org/10.1109/42.232268.

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40

Lund, Ulric. "Least circular distance regression for directional data." Journal of Applied Statistics 26, no. 6 (August 1999): 723–33. http://dx.doi.org/10.1080/02664769922160.

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41

Zorn, Conrad R., and Asaad Y. Shamseldin. "Quantifying Directional Dependencies from Infrastructure Restoration Data." Earthquake Spectra 32, no. 3 (August 2016): 1363–81. http://dx.doi.org/10.1193/013015eqs015m.

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Lifeline utilities and critical infrastructures are becoming increasingly interactive and dependent on one another for normal operation. With a natural disaster or disruptive event, these dependencies can be studied under stressed conditions. To replicate events and inform future simulations, such dependencies can be quantified in both magnitude and direction. This paper builds on recent efforts by proposing a new dependency index methodology that gives importance to the direction of dependency between coupled infrastructures and equally weighting the multiple dependencies that may be realized across a variety of lag times. The effectiveness of this methodology is presented as a case study for the 22 February 2011 earthquake experienced in Christchurch, New Zealand. Dependencies are quantified for a range of critical infrastructure couplings, which provide insight into the future application of these results and the requirement for integration with qualitative studies to accurately inform interdependency models.

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42

Beran, Rudolf. "Nonparametric estimation of trend in directional data." Stochastic Processes and their Applications 126, no. 12 (December 2016): 3808–27. http://dx.doi.org/10.1016/j.spa.2016.04.018.

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43

Mushkudiani, Nino A. "Small nonparametric tolerance regions for directional data." Journal of Statistical Planning and Inference 100, no. 1 (January 2002): 67–80. http://dx.doi.org/10.1016/s0378-3758(01)00093-3.

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44

Schulz, Jörn, Sungkyu Jung, Stephan Huckemann, Michael Pierrynowski, J. S. Marron, and Stephen M. Pizer. "Analysis of Rotational Deformations From Directional Data." Journal of Computational and Graphical Statistics 24, no. 2 (April 3, 2015): 539–60. http://dx.doi.org/10.1080/10618600.2014.914947.

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45

García-Portugués, Eduardo, Rosa M. Crujeiras, and Wenceslao González-Manteiga. "Kernel density estimation for directional–linear data." Journal of Multivariate Analysis 121 (October 2013): 152–75. http://dx.doi.org/10.1016/j.jmva.2013.06.009.

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46

Li, X., and H. S. Yu. "Tensorial characterisation of directional data in micromechanics." International Journal of Solids and Structures 48, no. 14-15 (July 2011): 2167–76. http://dx.doi.org/10.1016/j.ijsolstr.2011.03.019.

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47

Blanchet, F. Guillaume, Pierre Legendre, and Daniel Borcard. "Modelling directional spatial processes in ecological data." Ecological Modelling 215, no. 4 (July 2008): 325–36. http://dx.doi.org/10.1016/j.ecolmodel.2008.04.001.

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48

ISHIE, Teruaki, and Kiyoji SHIONO. "A Statistical Analysis of Directional Orientation Data." Geoinformatics 3, no. 4 (1992): 177–202. http://dx.doi.org/10.6010/geoinformatics1990.3.4_177.

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49

Presnell, Brett, Scott P. Morrison, and Ramon C. Littell. "Projected Multivariate Linear Models for Directional Data." Journal of the American Statistical Association 93, no. 443 (September 1998): 1068–77. http://dx.doi.org/10.1080/01621459.1998.10473768.

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50

Pilkington, Mark, and Walter R. Roest. "Removing varying directional trends in aeromagnetic data." GEOPHYSICS 63, no. 2 (March 1998): 446–53. http://dx.doi.org/10.1190/1.1444345.

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Both qualitative and quantitative interpretations of aeromagnetic data can be hindered by the presence of magnetic anomalies caused by mafic dykes. Such anomalies obscure the magnetic signatures due to basement lithology and structure, and their effects will often dominate when automated interpretation methods are applied to gridded data sets. Since dyke swarms are often nonparallel, simple frequency‐domain strike‐sensitive filtering based on a single directional trend is not a viable method for removing their signatures. We use a coordinate transformation to project anomalies of various strikes onto one direction, which is then suppressed using a standard decorrugation method. The resulting grid is subsequently transformed back to the original projection. This approach is illustrated by the removal of the magnetic signature of the Proterozoic Mackenzie dyke swarm occurring in the Slave structural province, Northwest Territories, Canada.

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