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Journal articles on the topic 'Statistické predikce'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Dušek, Ladislav, Klára Benešová, Ondřej Ngo, et al. "Díl III. – Epidemiologie idiopatických střevních zánětů v české populaci – časový vývoj a statistické predikce počtu pacientů." Gastroenterologie a hepatologie 73, no. 3 (2019): 257–64. http://dx.doi.org/10.14735/amgh2019257.

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2

Widji Pamungkas, Wahyu, M. Syamsul Ma'arif, Tun Tedja Irawadi, and Yandra Arkeman. "STATISTICAL CONTROL DETECTION ADAPTIVE (SCDA) MODELING FOR MONITORING AND PREDICTION OF NATIONAL CRUDE PALM OIL (CPO) VOLUME." Jurnal Teknologi Industri Pertanian 27, no. 1 (2017): 1–8. http://dx.doi.org/10.24961/j.tek.ind.pert.2017.27.1.1.

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3

Radcliffe, C. J., and X. L. Huang. "Putting Statistics into the Statistical Energy Analysis of Automotive Vehicles." Journal of Vibration and Acoustics 119, no. 4 (1997): 629–34. http://dx.doi.org/10.1115/1.2889773.

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Sound and vibration transmission modeling methods are important to the design process for high quality automotive vehicles. Statistical Energy Analysis (SEA) is an emerging design tool for the automotive industry that was initially developed in the 1960’s to estimate root-mean-square sound and vibration levels in structures and interior spaces. Although developed to estimate statistical mean values, automotive design application of SEA needs the additional ability to predict statistical variances of the predicted mean values of sound and vibration. This analytical ability would allow analysis

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4

Boyages, John, Nathan Coombs, Sharon Laura, and Owen Ung. "STATISTICS CAN PREDICT PATIENT CONFUSION." ANZ Journal of Surgery 77, no. 7 (2007): 598–99. http://dx.doi.org/10.1111/j.1445-2197.2007.04165.x.

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5

Szeląg, Bartosz, Łukasz Bąk, Roman Suligowski, and Jarosław Górski. "Statistical models to predict discharge overflow." Water Science and Technology 78, no. 5 (2018): 1208–18. http://dx.doi.org/10.2166/wst.2018.392.

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Abstract In the paper, a comparison of prediction results concerning the annual number of discharges of stormwater from the drainage system due to stormwater overflows is depicted. The prediction has been computed by means of storm water management model (SWMM) and probabilistic models. Regarding the probabilistic modelling some simple statistical models such as logit, probit, Gompertz and linear discriminant analysis model have been applied, and as for the hydrodynamic modelling a generator of synthetic rainfall based on the Monte Carlo method has been used. The analyses conducted has shown t

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6

Monti, Nicolas Emanuel, and Roberto Mariano Garcia. "A Statistical Analysis to Predict Financial Distress." Journal of Service Science and Management 03, no. 03 (2010): 309–35. http://dx.doi.org/10.4236/jssm.2010.33038.

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7

Abdel-Rahman, H. I., and B. A. Marzouk. "Statistical method to predict the sunspots number." NRIAG Journal of Astronomy and Geophysics 7, no. 2 (2018): 175–79. http://dx.doi.org/10.1016/j.nrjag.2018.08.001.

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8

Wright, Ian H., Charles Kooperberg, Barbara A. Bonar, and Gerard Bashein. "Statistical Modeling to Predict Elective Surgery Time." Anesthesiology 85, no. 6 (1996): 1235–45. http://dx.doi.org/10.1097/00000542-199612000-00003.

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Background Accurate estimation of operating times is a prerequisite for the efficient scheduling of the operating suite. The authors, in this study, sought to compare surgeons' time estimates for elective cases with those of commercial scheduling software, and to ascertain whether improvements could be made by regression modeling. Methods The study was conducted at the University of Washington Medical Center in three phases. Phase 1 retrospectively reviewed surgeons' time estimates and the scheduling system's estimates throughout 1 yr. In phase 2, data were collected prospectively from partici

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9

WRIGHT, IAN H., CHARLES KOOPERBERG, BARBARA A. BONAR, GERARD BASHEIN, and James H. Diaz. "Statistical Modeling to Predict Elective Surgery Time." Survey of Anesthesiology 41, no. 6 (1997): 335. http://dx.doi.org/10.1097/00132586-199712000-00022.

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10

Esclapez, Lucas, Félix Collin-Bastiani, Eleonore Riber, and Bénédicte Cuenot. "A statistical model to predict ignition probability." Combustion and Flame 225 (March 2021): 180–95. http://dx.doi.org/10.1016/j.combustflame.2020.10.051.

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11

Bannister, Adam, and Olaf Teschke. "Can Statistics Predict the Fields Medal Winners?" EMS Newsletter 2018-6, no. 108 (2018): 40–43. http://dx.doi.org/10.4171/news/108/11.

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12

Rumagit, Silviani E., and Azhari SN. "Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 7, no. 2 (2013): 189. http://dx.doi.org/10.22146/ijccs.3359.

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AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan. ARIMA merupakan salah satu metode statistik yang banyak diguna

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13

Wilson, Adam. "Risk-Based Statistical Approach To Predict Casing Leaks." Journal of Petroleum Technology 70, no. 06 (2018): 71–72. http://dx.doi.org/10.2118/0618-0071-jpt.

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14

Earnest, Arul, Say Beng Tan, Annelies Wilder-Smith, and David Machin. "Comparing Statistical Models to Predict Dengue Fever Notifications." Computational and Mathematical Methods in Medicine 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/758674.

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Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the femaleAedes aegyptimosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We develo

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15

Scarrott, Carl, and Granville Tunnicliffe Wilson. "Building a statistical model to predict reactor temperatures." Journal of Applied Statistics 28, no. 3-4 (2001): 497–511. http://dx.doi.org/10.1080/02664760120034207.

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16

Wigena, Aji Hamim, Anik Djuraidah, and Akbar Rizki. "Semiparametric modeling in statistical downscaling to predict rainfall." Applied Mathematical Sciences 9 (2015): 4371–82. http://dx.doi.org/10.12988/ams.2015.54362.

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17

Hofmann, Markus J., Chris Biemann, Chris Westbury, Mariam Murusidze, Markus Conrad, and Arthur M. Jacobs. "Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings." Cognitive Science 42, no. 7 (2018): 2287–312. http://dx.doi.org/10.1111/cogs.12662.

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18

WRIGHT, MICHAEL G., and TREFOR P. WILLIAMS. "USING BIDDING STATISTICS TO PREDICT COMPLETED CONSTRUCTION COST." Engineering Economist 46, no. 2 (2001): 114–28. http://dx.doi.org/10.1080/00137910108967565.

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19

Zhang, Jia Wei, Erica Kessler, and Jason L. G. Braasch. "Self-compassion mindsets can predict statistics course performance via intelligence mindsets and statistics anxiety." Learning and Individual Differences 90 (August 2021): 102047. http://dx.doi.org/10.1016/j.lindif.2021.102047.

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20

Castro, Frank P. "A Statistical Model to Predict Subsequent Fracture after Kyphoplasty." Spine Journal 14, no. 11 (2014): S118. http://dx.doi.org/10.1016/j.spinee.2014.08.293.

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21

Fitzgerald, Susan. "A Statistical Tool to Predict Outcomes with Epilepsy Surgery." Neurology Today 15, no. 5 (2015): 1. http://dx.doi.org/10.1097/01.nt.0000462535.62023.94.

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22

McGuiness, Erin, and Sue McNeil. "Statistical Models to Predict Commercial‐ and Parking‐Space Occupancy." Journal of Urban Planning and Development 117, no. 4 (1991): 129–39. http://dx.doi.org/10.1061/(asce)0733-9488(1991)117:4(129).

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23

Chakravarty, Suchana, and Debashis Barik. "Steady state statistical correlations predict bistability in reaction motifs." Molecular BioSystems 13, no. 4 (2017): 775–84. http://dx.doi.org/10.1039/c7mb00052a.

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24

Venkatesh, Kartik K., Robert A. Strauss, Chad A. Grotegut, et al. "Machine Learning and Statistical Models to Predict Postpartum Hemorrhage." Obstetrics & Gynecology 135, no. 4 (2020): 935–44. http://dx.doi.org/10.1097/aog.0000000000003759.

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25

Young, Roger C. "Machine Learning and Statistical Models to Predict Postpartum Hemorrhage." Obstetrics & Gynecology 136, no. 1 (2020): 194–95. http://dx.doi.org/10.1097/aog.0000000000003980.

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26

Hosseini, Mehdi, Sareh Rajabi Agereh, Yones Khaledian, Hossein Jafarzadeh Zoghalchali, Eric C. Brevik, and Seyed Ali Reza Movahedi Naeini. "Comparison of multiple statistical techniques to predict soil phosphorus." Applied Soil Ecology 114 (June 2017): 123–31. http://dx.doi.org/10.1016/j.apsoil.2017.02.011.

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27

Salhi, Asma, Valerie Burdin, Arnaud Boutillon, Sylvain Brochard, Tinashe Mutsvangwa, and Bhushan Borotikar. "Statistical Shape Modeling Approach to Predict Missing Scapular Bone." Annals of Biomedical Engineering 48, no. 1 (2019): 367–79. http://dx.doi.org/10.1007/s10439-019-02354-6.

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28

Morgan, Emily, Allison Fogel, Anjali Nair, and Aniruddh D. Patel. "Statistical learning and Gestalt-like principles predict melodic expectations." Cognition 189 (August 2019): 23–34. http://dx.doi.org/10.1016/j.cognition.2018.12.015.

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29

Coe, Richard. "Predict. Version 1.3." Applied Statistics 40, no. 1 (1991): 189. http://dx.doi.org/10.2307/2347930.

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30

Samonds, Jason M., Wilson S. Geisler, and Nicholas J. Priebe. "Natural image and receptive field statistics predict saccade sizes." Nature Neuroscience 21, no. 11 (2018): 1591–99. http://dx.doi.org/10.1038/s41593-018-0255-5.

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31

Barnett, Tristan, and Stephen R. Clarke. "Combining player statistics to predict outcomes of tennis matches." IMA Journal of Management Mathematics 16, no. 2 (2005): 113–20. http://dx.doi.org/10.1093/imaman/dpi001.

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32

Ettredge, Michael, John Gerdes, and Gilbert Karuga. "Using web-based search data to predict macroeconomic statistics." Communications of the ACM 48, no. 11 (2005): 87–92. http://dx.doi.org/10.1145/1096000.1096010.

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33

Slijper, Harm, Janneke Richter, Eelco Over, Jeroen Smeets, and Maarten Frens. "Statistics Predict Kinematics of Hand Movements During Everyday Activity." Journal of Motor Behavior 41, no. 1 (2009): 3–9. http://dx.doi.org/10.1080/00222895.2009.10125922.

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34

O’Bryant, Monique, Prathiba Natesan Batley, and Anthony J. Onwuegbuzie. "Validation of an Adapted Version of the Statistical Anxiety Scale in English and Its Relationship to Attitudes Toward Statistics." SAGE Open 11, no. 1 (2021): 215824402110013. http://dx.doi.org/10.1177/21582440211001378.

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The aims of this study were to validate an instrument that measured statistics anxiety and to examine how attitudes toward statistics predict statistics anxiety using the Attitudes Toward Statistics (ATS) Scale for a sample of 323 undergraduate social science majors enrolled in colleges and universities in the United States. A confirmatory factor analysis suggested retaining a revised two-factor model of the Statistical Anxiety Scale (SAS) to measure statistics anxiety, namely, help and interpretation anxiety ([Formula: see text] = 49.37, df = 38.13, p = .105, comparative fit index [CFI] = .95

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35

MENYE, Cyrille, Francis Rollin NDOM, Claude Marie NGABIRENG, and Siméon KOUAM FOGUE. "Quantum and Structural Molecular Fragment models used to predict anti-inflammatory activity." SDRP Journal of Computational Chemistry & Molecular Modeling 5, no. 1 (2021): 550–60. http://dx.doi.org/10.25177/jccmm.5.1.ra.10752.

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In this paper, we predict the anti-inflammatory activity of a series of 26 structures of N-arylanthranilic acid. So, Quantitatve Structure-Activity Relationship (QSAR) method remains the focus of many studies aimed at modeling and prediction of physicochemical properties or biological activities of molecule. Two models was used: quantum model and Structural Molecular Fragment (SMF) model. In the first model, semi-empirical (AM1) approach was used to calculate the quantum chemical descriptors using GAUSSIAN 09 package and the others chemical descriptors were calculated with chemaxon package. In

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36

Long, Wu Jian, Kamal Henri Khayat, and Feng Xing. "Statistical Models to Predict Fresh Properties of Self-Consolidating Concrete." Advanced Materials Research 129-131 (August 2010): 853–56. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.853.

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In order to understand the influence of mixture parameters on concrete behaviour, a factorial design was employed in this investigation to identify the relative significance of primary mixture parameters and their coupled effects (interactions) on fresh properties of SCC that are of special interest to precast, prestressed applications. In addition to the 16 SCC mixtures employed, three SCC mixtures corresponding to the central point of the factorial design were prepared to estimate the degree of the experimental error for each of the modeled responses. The mixtures were evaluated to determine

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37

Li Jing-Tian, Wang Jian-Lu, Zhang Bang-Qiang, Rong Xi-Ming, and Ning Xi-Jing. "A statistical model to predict the steady-state creep rate." Acta Physica Sinica 63, no. 2 (2014): 028101. http://dx.doi.org/10.7498/aps.63.028101.

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38

Abler, Daniel, Steve Berger, Alexandre Terrier, Fabio Becce, Alain Farron, and Philippe Büchler. "A statistical shape model to predict the premorbid glenoid cavity." Journal of Shoulder and Elbow Surgery 27, no. 10 (2018): 1800–1808. http://dx.doi.org/10.1016/j.jse.2018.04.023.

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39

Jayanthiladevi, A., V. Nagaraju, L. Devi, et al. "Performing Experimentation with Physics Model to Predict Statistical Weather Condition." Journal of Physics: Conference Series 1712 (December 2020): 012026. http://dx.doi.org/10.1088/1742-6596/1712/1/012026.

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40

Cypher Springer, Shelley, Dawn Fleming, and Thomas C. Hulsey. "A Statistical Model to Predict Nonsurvival in Congenital Diaphragmatic Hernia." Journal of Perinatology 22, no. 4 (2002): 263–67. http://dx.doi.org/10.1038/sj.jp.7210681.

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41

Huetz, Lionel, and Pierre Emmanuel Guillerm. "Database building and statistical methods to predict sailing yacht hydrodynamics." Ocean Engineering 90 (November 2014): 21–33. http://dx.doi.org/10.1016/j.oceaneng.2014.08.003.

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42

Knopp, Matthias Manne, Niels Erik Olesen, Yanbin Huang, René Holm, and Thomas Rades. "Statistical Analysis of a Method to Predict Drug–Polymer Miscibility." Journal of Pharmaceutical Sciences 105, no. 1 (2016): 362–67. http://dx.doi.org/10.1002/jps.24704.

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43

Kidd, Evan, and Joanne Arciuli. "Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax." Child Development 87, no. 1 (2015): 184–93. http://dx.doi.org/10.1111/cdev.12461.

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44

Saladi, Shyam, Alexander E. Chu, and William M. Clemons. "Statistical Models Robustly Predict Membrane Protein Expression in E. Coli." Biophysical Journal 112, no. 3 (2017): 356a. http://dx.doi.org/10.1016/j.bpj.2016.11.1929.

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45

Gottfredson, Stephen D., and Don M. Gottfredson. "Violence Prediction Methods: Statistical and Clinical Strategies." Violence and Victims 3, no. 4 (1988): 303–24. http://dx.doi.org/10.1891/0886-6708.3.4.303.

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The paper suggests that the conventional wisdoms that we cannot and should not predict violence are wrong. We can predict violence, and we should predict violence. It is the unfortunate case, however, that we cannot do it very well, and this is true whether the predictions are made subjectively or statistically. Since the prediction of violence (and of other antisocial behaviors) is so pervasive in our justice and mental health systems, it is important that we attempt to do it better—that is, more efficiently and more effectively. In this paper we show that there is value to both clinical and

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46

Leicht, A., W. Spinks, and J. Lukins. "109 Can team statistics predict performance in Olympic men's basketball?" Journal of Science and Medicine in Sport 8 (December 2005): 65. http://dx.doi.org/10.1016/s1440-2440(17)30604-7.

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47

Leicht, A., W. Spinks, and J. Lukins. "110 Can team statistics predict performance in Olympic women's basketball?" Journal of Science and Medicine in Sport 8 (December 2005): 66. http://dx.doi.org/10.1016/s1440-2440(17)30605-9.

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48

Steipe, Boris, Britta Schiller, Andreas Plückthun, and Stefan Steinbacher. "Sequence Statistics Reliably Predict Stabilizing Mutations in a Protein Domain." Journal of Molecular Biology 240, no. 3 (1994): 188–92. http://dx.doi.org/10.1006/jmbi.1994.1434.

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49

Hansson, L. "New concept used to predict slant path rain attenuation statistics." IEE Proceedings H Microwaves, Antennas and Propagation 137, no. 2 (1990): 89. http://dx.doi.org/10.1049/ip-h-2.1990.0017.

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50

Long, F., Z. Yang, and D. Purves. "Spectral statistics in natural scenes predict hue, saturation, and brightness." Proceedings of the National Academy of Sciences 103, no. 15 (2006): 6013–18. http://dx.doi.org/10.1073/pnas.0600890103.

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