Academic literature on the topic 'Complementary cumulative distribution function'
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Journal articles on the topic "Complementary cumulative distribution function"
Kaur, Parneet. "Complementary Cumulative Distribution Function for Performance Analysis of OFDM Signals." IOSR Journal of Electronics and Communication Engineering 2, no. 5 (2012): 05–07. http://dx.doi.org/10.9790/2834-0250507.
Full textKim, Bongsong. "How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function." Evolutionary Bioinformatics 14 (January 2018): 117693431879735. http://dx.doi.org/10.1177/1176934318797352.
Full textIyit, Neslihan. "Modelling world energy security data from multinomial distribution by generalized linear model under different cumulative link functions." Open Chemistry 16, no. 1 (April 30, 2018): 377–85. http://dx.doi.org/10.1515/chem-2018-0053.
Full textSato, Aki Hiro. "A Method to Quantify Risks of Financial Assets: An Empirical Analysis of Japanese Security Prices." Advanced Materials Research 452-453 (January 2012): 469–73. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.469.
Full textSchaeben, Helmut. "Non-Parametric Comparison of Crystallographic Orientation Distributions." Materials Science Forum 1016 (January 2021): 1258–63. http://dx.doi.org/10.4028/www.scientific.net/msf.1016.1258.
Full textChesneau, Christophe. "Study of a unit power-logarithmic distribution." Open Journal of Mathematical Sciences 5, no. 1 (May 24, 2021): 218–35. http://dx.doi.org/10.30538/oms2021.0159.
Full textHassan, Amal, Salwa Assar, and Kareem Ali. "The complementary Poisson-Lindley class of distributions." International Journal of Advanced Statistics and Probability 3, no. 2 (June 29, 2015): 146. http://dx.doi.org/10.14419/ijasp.v3i2.4624.
Full textWalsh, Anthony J., John A. O’Dowd, Vivian M. Bessler, Kai Shi, Frank Smyth, James M. Dailey, Bryan Kelleher, Liam P. Barry, and Andrew D. Ellis. "Characterization of time-resolved laser differential phase using 3D complementary cumulative distribution functions." Optics Letters 37, no. 10 (May 15, 2012): 1769. http://dx.doi.org/10.1364/ol.37.001769.
Full textPrasetyo, Rindang Bangun, Heri Kuswanto, Nur Iriawan, and Brodjol Sutijo Suprih Ulama. "Binomial Regression Models with a Flexible Generalized Logit Link Function." Symmetry 12, no. 2 (February 2, 2020): 221. http://dx.doi.org/10.3390/sym12020221.
Full textHelton, J. C. "Probability, conditional probability and complementary cumulative distribution functions in performance assessment for radioactive waste disposal." Reliability Engineering & System Safety 54, no. 2-3 (November 1996): 145–63. http://dx.doi.org/10.1016/s0951-8320(96)00072-5.
Full textDissertations / Theses on the topic "Complementary cumulative distribution function"
Oltean, Elvis. "Modelling income, wealth, and expenditure data by use of Econophysics." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/20203.
Full textBAIG, CLEMENT RANJITH ANTHIKKAD & IRFAN AHMED. "PERFORMANCE ENHANCEMENT OF OFDM IN PAPR REDUCTION USING NEW COMPANDING TRANSFORM AND ADAPTIVE AC EXTENSION ALGORITHM FOR NEXT GENERATION NETWORKSPERFORMANCE ENHANCEMENT OF OFDM IN PAPR REDUCTION USING NEW COMPANDING TRANSFORM AND ADAPTIVE AC EXTENSION ALGORITHM FOR NEXT GENERATION NETWORKS." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6011.
Full textThe proposed technique namely Adaptive Active Constellation Extension (Adaptive ACE) Algorithm reduced the high Peak-to-Average Power Ratio (PAPR) of the Orthogonal Frequency Division Multiplexing (OFDM) systems. The Peak-to-Average Power Ratio (PAPR) is equal to 6.8 dB for the target clipping ratios of 4 dB, 2 dB and 0 dB by using Adaptive Active Constellation Extension (Adaptive ACE) Algorithm. Thus, the minimum PAPR can be achieved for low target clipping ratios. The Signal-to-Noise Ratio (SNR) of the Orthogonal Frequency Division Multiplexing (OFDM) signal obtained by the Adaptive Active Constellation Extension (Adaptive ACE) algorithm is equal to 1.2 dB at a Bit Error Rate (BER) of 10-0..4 for different constellation orders like 4-Quadrature Amplitude Modulation (4-QAM), 16-Quadrature Amplitude Modulation (16-QAM) and 64-Quadrature Amplitude Modulation (16-QAM). Here, the Bit Error Rate of 10-0.4 or 0.398, that means a total of 398-bits are in error when 1000-bits are transmitted via a communication channel or approximately 4-bits are in error when 10-bits are transmitted via a communication channel, which is high when compared to that of the original Orthogonal Frequency Division Multiplexing (OFDM) signal. The other problems faced by the Adaptive Active Constellation Extension (Adaptive ACE) algorithm are Out-of-Band Interference (OBI) and peak regrowth. Here, the Out-of-Band Interference (OBI) is a form of noise or an unwanted signal, which is caused when the original Orthogonal Frequency Division Multiplexing (OFDM) signal is clipped for reducing the peak signals which are outside of the predetermined area and the peak regrowth is obtained after filtering the clipped signal. The peak regrowth results to, increase in the computational time and computational complexity. In this paper, we have proposed a PAPR reduction scheme to improve the bit error rate performance by applying companding transform technique. Hence, 1-1.5 dB reduction in PAPR with this Non-companding technique is achieved. In Future, We can accept to implement the same on Rician and Rayleigh channels.
Clement Ranjith Anthikkad (E-mail: clement.ranjith@gmail.com / clan11@bth.se) & Irfan Ahmed Baig (E-mail: baig.irfanahmed@gmail.com / ir-a11@bth.se )
Liu, Xuecheng 1963. "Nonparametric maximum likelihood estimation of the cumulative distribution function with multivariate interval censored data : computation, identifiability and bounds." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=79036.
Full textForgo, Vincent Z. Mr. "A Distribution of the First Order Statistic When the Sample Size is Random." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3181.
Full textJeisman, Joseph Ian. "Estimation of the parameters of stochastic differential equations." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16205/.
Full textEricok, Ozlen. "Uncertainty Assessment In Reserv Estimation Of A Naturally Fractured Reservoir." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605713/index.pdf.
Full textOK, Ö
zlen M.S., Department of Petroleum and Natural Gas Engineering Supervisor : Prof. Dr. Fevzi GÜ
MRAH December 2004, 169 pages Reservoir performance prediction and reserve estimation depend on various petrophysical parameters which have uncertainties due to available technology. For a proper and economical field development, these parameters must be determined by taking into consideration their uncertainty level and probable data ranges. For implementing uncertainty assessment on estimation of original oil in place (OOIP) of a field, a naturally fractured carbonate field, Field-A, is chosen to work with. Since field information is obtained by drilling and testing wells throughout the field, uncertainty in true ranges of reservoir parameters evolve due to impossibility of drilling every location on an area. This study is based on defining the probability distribution of uncertain variables in reserve estimation and evaluating probable reserve amount by using Monte Carlo simulation method. Probabilistic reserve estimation gives the whole range of probable v original oil in place amount of a field. The results are given by their likelyhood of occurance as P10, P50 and P90 reserves in summary. In the study, Field-A reserves at Southeast of Turkey are estimated by probabilistic methods for three producing zones
Karabogaz Formation, Kbb-C Member of Karababa formation and Derdere Formation. Probability density function of petrophysical parameters are evaluated as inputs in volumetric reserve estimation method and probable reserves are calculated by @Risk software program that is used for implementing Monte Carlo method. Outcomes of the simulation showed that Field-A has P50 reserves as 11.2 MMstb in matrix and 2.0 MMstb in fracture of Karabogaz Formation, 15.7 MMstb in matrix and 3.7 MMstb in fracture of Kbb-C Member and 10.6 MMstb in matrix and 1.6 MMstb in fracture of Derdere Formation. Sensitivity analysis of the inputs showed that matrix porosity, net thickness and fracture porosity are significant in Karabogaz Formation and Kbb-C Member reserve estimation while water saturation and fracture porosity are most significant in estimation of Derdere Formation reserves.
Cunha, Lucas Santana da. "Modelos não lineares resultantes da soma de regressões lineares ponderadas por funções distribuição acumulada." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-04052016-100308/.
Full textThe electronic controllers spray aimed at minimizing the variation of inputs rates applied in the field. They are part of a control system, and allow for compensation for variation spray travel speed during operation. There are several types of electronic spray controllers on the market and one way to select which more efficient under the same conditions, ie in the same system of control, is to quantify the system response time for each specific driver. The objective of this study was to estimate the response times for changes in speed of an electronic spraying system via nonlinear regression models, these resulting from the sum of weighted linear regressions for cumulative distribution functions. Data were obtained on the Application Technology Laboratory, located in the Department of Biosystems Engineering from College of Agriculture \"Luiz de Queiroz\", University of Sao Paulo, in Piracicaba, Sao Paulo, Brazil. The models used were the logistic and Gompertz, resulting from a weighted sum of two constant linear regressions with weight given by the cumulative distribution function logistics and Gumbell respectively. Reparametrization been proposed for inclusion in the control system response time models, in order to improve the statistical interpretation and inference of the same. It has also been proposed a non-linear regression model two-phase which is the weighted sum of constant linear regressions weight given by a cumulative distribution function exponential hyperbolic sine Cauchy in which a simulation study was conducted using the methodology of Monte Carlo to evaluating the maximum likelihood estimates of the model parameters.
Bothenna, Hasitha Imantha. "Approximation of Information Rates in Non-Coherent MISO wireless channels with finite input signals." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1516369758012866.
Full textDhuness, Kahesh. "An offset modulation method used to control the PAPR of an OFDM transmission." Thesis, University of Pretoria, 2012. http://hdl.handle.net/2263/27258.
Full textThesis (PhD)--University of Pretoria, 2012.
Electrical, Electronic and Computer Engineering
unrestricted
Ahmad, Shafiq, and Shafiq ahmad@rmit edu au. "Process capability assessment for univariate and multivariate non-normal correlated quality characteristics." RMIT University. Mathematical and Geospatial Sciences, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091127.121556.
Full textBooks on the topic "Complementary cumulative distribution function"
Wildman, Valerie Jean. Estimating effective area surveyed with the cumulative distribution function. Corvallis, Or: Dept. of Statistics, Oregon State University, 1985.
Find full textMa, Xiaofang. Computation of the probability density function and the cumulative distribution function of the generalized gamma variance model. Ottawa: National Library of Canada, 2002.
Find full textBook chapters on the topic "Complementary cumulative distribution function"
Gooch, Jan W. "Cumulative Distribution Function." In Encyclopedic Dictionary of Polymers, 980. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15210.
Full textNguyen, Hung T., and Gerald S. Rogers. "The Cumulative Distribution Function." In Springer Texts in Statistics, 201–7. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1013-9_24.
Full textKokoska, Stephen, and Christopher Nevison. "The Binomial Cumulative Distribution Function." In Springer Texts in Statistics, 50–51. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9629-1_5.
Full textKokoska, Stephen, and Christopher Nevison. "The Poisson Cumulative Distribution Function." In Springer Texts in Statistics, 52–54. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9629-1_6.
Full textZhu, Jun, S. N. Lahiri, and Noel Cressie. "Asymptotic Distribution of the Empirical Cumulative Distribution Function Predictor under Nonstationarity." In Spatial Statistics: Methodological Aspects and Applications, 1–20. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0147-9_1.
Full textKokoska, Stephen, and Christopher Nevison. "Cumulative Distribution Function for the Standard Normal Random Variable." In Springer Texts in Statistics, 55–56. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9629-1_7.
Full textLima, Roberta, and Rubens Sampaio. "Uncertainty Quantification and Cumulative Distribution Function: How are they Related?" In Springer Proceedings in Mathematics & Statistics, 253–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91143-4_24.
Full textZhang, Juan, and John E. Kolassa. "A comparison of the accuracy of saddlepoint conditional cumulative distribution function approximations." In Complex Datasets and Inverse Problems, 250–59. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000193.
Full textLiu, Xuecheng, and Shoumei Li. "Cumulative Distribution Function Estimation with Fuzzy Data: Some Estimators and Further Problems." In Synergies of Soft Computing and Statistics for Intelligent Data Analysis, 83–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33042-1_10.
Full textGalgana, Rigel, Cengke Shi, Amy Greenwald, and Takehiro Oyakawa. "A Dynamic Program for Computing the Joint Cumulative Distribution Function of Order Statistics." In SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), 160–70. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976830.15.
Full textConference papers on the topic "Complementary cumulative distribution function"
Zhu, Bingcheng, Zhaoquan Zeng, and Julian Cheng. "Arbitrarily tight bounds on cumulative distribution function of Beckmann distribution." In 2017 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2017. http://dx.doi.org/10.1109/iccnc.2017.7876099.
Full textTanyer, Suleyman Gokhun. "The Cumulative Distribution Function for a finite data set." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204462.
Full textDash, Jatindra K., Sudipta Mukhopadhyay, and Niranjan Khandelwal. "Complementary cumulative precision distribution: a new graphical metric for medical image retrieval system." In SPIE Medical Imaging, edited by Claudia R. Mello-Thoms and Matthew A. Kupinski. SPIE, 2014. http://dx.doi.org/10.1117/12.2043156.
Full textMayhew, Gregory L. "Cumulative distribution function for order 7 de Bruijn weight classes." In 2009 IEEE Aerospace conference. IEEE, 2009. http://dx.doi.org/10.1109/aero.2009.4839405.
Full textKrug, Gregory, and Janusz Madejski. "Improving track condition by application of Quasi Cumulative Distribution Function (QCDF)." In Fifth International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2018. http://dx.doi.org/10.5592/co/cetra.2018.749.
Full textJang, Seul Ki, Yoon-Gyoo Lee, Gyoung-Soo Park, and Choon-Woo Kim. "Content-dependent contrast enhancement for displays based on cumulative distribution function." In IS&T/SPIE Electronic Imaging, edited by Reiner Eschbach, Gabriel G. Marcu, and Alessandro Rizzi. SPIE, 2013. http://dx.doi.org/10.1117/12.2007236.
Full textMalinova, Anna, Olga Rahneva, Nikolay Pavlov, Angel Golev, and Vesselin Kyurkchiev. "A look at the Garima cumulative distribution function some related problems." In SEVENTH INTERNATIONAL CONFERENCE ON NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040121.
Full textLee, Jongmin, Pingping Zhu, and Jose C. Principe. "A parameter-free kernel design based on cumulative distribution function for correntropy." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6707021.
Full textYelmanov, Sergei, Olena Hranovska, and Yuriy Romanyshyn. "Image Enhancement Technique based on Power-Law Transformation of Cumulative Distribution Function." In 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT). IEEE, 2019. http://dx.doi.org/10.1109/aiact.2019.8847876.
Full textManjunath, K. N., and U. C. Niranjan. "Linear Models of Cumulative Distribution Function for Content based Medical Image Retrieval." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615981.
Full textReports on the topic "Complementary cumulative distribution function"
Helton, J. C. Probability, conditional probability and complementary cumulative distribution functions in performance assessment for radioactive waste disposal. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/231952.
Full textHelton, J. C., and A. W. Shiver. A Monte Carlo procedure for the construction of complementary cumulative distribution functions for comparison with the EPA release limits for radioactive waste disposal. Office of Scientific and Technical Information (OSTI), October 1994. http://dx.doi.org/10.2172/27828.
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