Relevant bibliographies by topics / Mixture of lognormal distributions

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

Academic literature on the topic 'Mixture of lognormal distributions'

Author: Grafiati
Published: 18 April 2022
Last updated: 30 July 2025

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Journal articles on the topic "Mixture of lognormal distributions"

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Liu, Z., J. Almhana, F. Wang, and R. Mcgorman. "Mixture Lognormal Approximations to Lognormal Sum Distributions." IEEE Communications Letters 11, no. 9 (2007): 711–13. http://dx.doi.org/10.1109/lcomm.2007.070656.

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Cao, Quang V., and Qinglin Wu. "Characterizing wood fiber and particle length with a mixture distribution and a segmented distribution." Holzforschung 61, no. 2 (2007): 124–30. http://dx.doi.org/10.1515/hf.2007.023.

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Abstract The length data from 12 samples of wood fibers and particles were described using lognormal and Weibull distributions. While both distributions fitted the middle range of the data well, the lognormal distribution provided a closer fit for short fibers and particles and the Weibull distribution was more appropriate for long ones. A mixture of the lognormal and Weibull distributions was developed using a variable weight to allow the new distribution to take the lognormal form for short fibers and gradually change to the Weibull form for long fibers. In the segmented distribution approac
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Areeak, Tidarut, and Tanatip Hanpayak. "MIXTURE DISTRIBUTION ANALYSIS OF DAIRY AVERAGE PM2.5 CONCENTRATIONS IN BANGKOK, THAILAND." Suranaree Journal of Science and Technology 31, no. 5 (2025): 030232(1–10). https://doi.org/10.55766/sujst-2024-05-e05095.

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Air pollution, specifically PM2.5, poses significant health risks and global environmental challenges. Bangkok, the capital city of Thailand, also experiences severe levels of PM2.5. The objective of this study is to determine the optimal probability distribution for PM2.5 concentration in Bangkok. Daily average PM2.5 concentrations from January 1, 2018, to December 31, 2022, were analyzed at 10 monitoring sites in the Bangkok area. The concentration patterns of PM2.5 were characterized using several statistical distributions, including lognormal, gamma, and Weibull distributions, as well as 2
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Kechejian, H., V. K. Ohanyan, and V. G. Bardakhchyan. "On Poisson Mixture of Lognormal Distributions." Lobachevskii Journal of Mathematics 41, no. 3 (2020): 340–48. http://dx.doi.org/10.1134/s1995080220030087.

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Cao, Quang V., and Thomas J. Dean. "Modeling Crown Structure from LiDAR Data with Statistical Distributions." Forest Science 57, no. 5 (2011): 359–64. http://dx.doi.org/10.1093/forestscience/57.5.359.

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Abstract The objective of this study was to evaluate the ability of three statistical distributions to characterize the vertical distribution of foliage mass in canopies of even-aged loblolly pine stands, based on airborne-scanning light detection and ranging (LiDAR) data. The functions were the Weibull and SB distributions and a mixture of the lognormal and Weibull distributions. Results indicated that the mixture distribution fit the LiDAR data better than the Weibull and SB distributions, according to three goodness-of-fit statistics. By switching from the lognormal for data near the tree t
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So, Jacky C. "The Distribution of Financial Ratios—A Note." Journal of Accounting, Auditing & Finance 9, no. 2 (1994): 215–23. http://dx.doi.org/10.1177/0148558x9400900205.

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Three competitive distributions are offered by the literature to explain the non-normality and skewness of the cross-sectional distribution of financial ratios: the mixture of normal distributions, the lognormal distribution, and the gamma distribution. Using a new technique, this paper shows that the lognormal distribution and the gamma distribution are not supported by the empirical evidence. Although these two distributions indeed capture skewness, they do not portray the correct shape of the distributions. The non-normal stable Paretian distribution seems to be good candidate to describe t
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Susanto, Irwan, and Sri Sulistijowati Handajani. "PENGELOMPOKAN RUMAH TANGGA DI INDONESIA BERDASARKAN PENDAPATAN PER KAPITA DENGAN MODEL FINITE MIXTURE." MEDIA STATISTIKA 13, no. 1 (2020): 13–24. http://dx.doi.org/10.14710/medstat.13.1.13-24.

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In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of hou
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Al-Moisheer, A. S. "Mixture of Lindley and Lognormal Distributions: Properties, Estimation, and Application." Journal of Function Spaces 2021 (December 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/9358496.

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Finite mixture models provide a flexible tool for handling heterogeneous data. This paper introduces a new mixture model which is the mixture of Lindley and lognormal distributions (MLLND). First, the model is formulated, and some of its statistical properties are studied. Next, maximum likelihood estimation of the parameters of the model is considered, and the performance of the estimators of the parameters of the proposed models is evaluated via simulation. Also, the flexibility of the proposed mixture distribution is demonstrated by showing its superiority to fit a well-known real data set
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Brown, Simon, and David C. Simcock. "The Amoroso distribution as a model of the distribution of blood haemoglobin concentration." Deviot Institute Working Papers 2022 (January 3, 2022): 01. https://doi.org/10.5281/zenodo.7954065.

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The distribution of haemoglobin (Hb) concentration is often treated as though it is normal or lognormal, but the issue is frequently ignored. Part of the problem is that Hb concentration distribution can have negative, zero or positive skewness. To address this issue, we have shown previously that a normal mixture might be appropriate in some circumstances. Here we show that the Amoroso distribution is as good as or better than (a) the normal, lognormal, gamma, Weibull and skew normal distributions and (b) the normal mixture distribution used to describe Hb concentration. The Amoroso distribut
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Brown, Simon, and David C. Simcock. "On the distribution of blood haemoglobin concentration." Deviot Institute Working Papers 2021 (December 1, 2021): 02. https://doi.org/10.5281/zenodo.7951921.

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The distribution of blood haemoglobin (Hb) concentration is generally treated as though it is normal or lognormal, although the data are often negatively skewed, which is inconsistent with both distributions. The issue is complicated by the fact that the data are often edited to exclude biologically ‘implausible’ values and then to exclude biologically ‘abnormal’ values. This editing process tends to render the distribution more ‘statistically normal’, which, while convenient, prompts the concern that that might have been the desired outcome. Using the NHANE
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Dissertations / Theses on the topic "Mixture of lognormal distributions"

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Pavelka, Roman. "Využití kvantilových funkcí při kostrukci pravděpodobnostních modelů mzdových rozdělení." Doctoral thesis, Vysoká škola ekonomická v Praze, 2004. http://www.nusl.cz/ntk/nusl-77099.

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Over the course of years from 1995 to 2008 was acquired by Average Earnings Information System under the professional gestation of the Czech Republic Ministry of Labor and Social Affairs wage and personal data by individual employees. Thanks to the fact that in this statistical survey are collected wage and personal data by concrete employed persons it is possible to obtain a wage distribution, so it how this wages spread out among individual employees. Values that wages can be assumed in whole wage interval are not deterministical but they result from interactions of many random influences. T
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Feng, Jingyu. "Modeling Distributions of Test Scores with Mixtures of Beta Distributions." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1068.pdf.

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Jesus, Sandra Rêgo de. "Análise bayesiana objetiva para as distribuições normal generalizada e lognormal generalizada." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/4495.

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Made available in DSpace on 2016-06-02T20:04:53Z (GMT). No. of bitstreams: 1 6424.pdf: 5426262 bytes, checksum: 82bb9386f85845b0d3db787265ea8236 (MD5) Previous issue date: 2014-11-21<br>The Generalized Normal (GN) and Generalized lognormal (logGN) distributions are flexible for accommodating features present in the data that are not captured by traditional distribution, such as the normal and the lognormal ones, respectively. These distributions are considered to be tools for the reduction of outliers and for the obtention of robust estimates. However, computational problems have always been
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Lynch, O'Neil. "Mixture distributions with application to microarray data analysis." Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/2075.

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The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. In this dissertation we proposed two methods to determine differentially expressed genes. For the penalized normal mixture model (PMMM) to determine genes that are differentially expressed, we penalized both the variance and the mixing proportion parameters simultaneously. The variance parameter was penalized so that the log-likelihood will be bounded, while the mixing proportion parameter was penalized
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Soegiarso, Restuti Widayati. "A covariate model in finite mixture survival distributions." Case Western Reserve University School of Graduate Studies / OhioLINK, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=case1056547652.

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屠烈偉 and Lit-wai Tao. "Statistical inference on a mixture model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977480.

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Tao, Lit-wai. "Statistical inference on a mixture model." [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13781479.

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Cross, Richard J. (Richard John). "Efficient Tools For Reliability Analysis Using Finite Mixture Distributions." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4853.

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The complexity of many failure mechanisms and variations in component manufacture often make standard probability distributions inadequate for reliability modeling. Finite mixture distributions provide the necessary flexibility for modeling such complex phenomena but add considerable difficulty to the inference. This difficulty is overcome by drawing an analogy to neural networks. With appropropriate modifications, a neural network can represent a finite mixture CDF or PDF exactly. Training with Bayesian Regularization gives an efficient empirical Bayesian inference of the failure time dis
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Miller, Blake Allen Ashman Keith M. "Mixture modeling analysis of bimodal globular cluster color distributions." Diss., UMK access, 2006.

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Thesis (M.S.)--Dept. of Physics. University of Missouri--Kansas City, 2006.<br>"A thesis in physics." Typescript. Advisor: Keith Ashman. Vita. Title from "catalog record" of the print edition Description based on contents viewed Nov. 1, 2007. Includes bibliographical references (leaves 51-53). Online version of the print edition.
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Choy, Sai Tsang Boris. "Robust Bayesian analysis using scale mixture of normals distributions." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404256.

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Books on the topic "Mixture of lognormal distributions"

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L, Crow Edwin, and Shimizu Kunio 1948-, eds. Lognormal distributions: Theory and applications. M. Dekker, 1988.

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Lachos Dávila, Víctor Hugo, Celso Rômulo Barbosa Cabral, and Camila Borelli Zeller. Finite Mixture of Skewed Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98029-4.

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Titterington, D. M. Statistical analysis of finite mixture distributions. Wiley, 1985.

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Balakrishnan, N., and William W. S. Chen. Handbook of Tables for Order Statistics from Lognormal Distributions with Applications. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5309-0.

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S, Chen William W., ed. Handbook of tables for order statistics from lognormal distributions with applications. Kluwer Academic Publishers, 1999.

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R, Hancock Gregory, and Samuelsen Karen M, eds. Advances in latent variable mixture models. Information Age Pub., 2008.

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Evans, Michael J. Numerical aspects in estimating the parameters of a mixture of normal distributions. University of Toronto, Dept. of Statistics, 1991.

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Ćwik, Jan. Nonidentifiability of components in a mixture of distributions corresponding to repeated observations. Institute of Computer Sciences, Polish Academy of Sciences, 1991.

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Gellert, Charles Lawrence. Misaligned (shuffled) data analysis with application to gene regulation. University at Albany, School of Public Health, Dept. of Biometry and Statistics, 1990.

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service), SpringerLink (Online, ed. Medical Applications of Finite Mixture Models. Springer-Verlag Berlin Heidelberg, 2009.

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Book chapters on the topic "Mixture of lognormal distributions"

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Thomopoulos, Nick T. "Lognormal." In Probability Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76042-1_9.

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Thomopoulos, Nick T. "Lognormal." In Statistical Distributions. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65112-5_9.

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Thomopoulos, Nick T. "Bivariate Lognormal." In Probability Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76042-1_10.

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Thomopoulos, Nick T. "Bivariate Lognormal." In Statistical Distributions. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65112-5_20.

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Balakrishnan, N., and William W. S. Chen. "Lognormal Distributions and Properties." In Handbook of Tables for Order Statistics from Lognormal Distributions with Applications. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5309-0_2.

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Moy, Ronald L., Li-Shya Chen, and Lie Jane Kao. "Normal and Lognormal Distributions." In Study Guide for Statistics for Business and Financial Economics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11997-7_7.

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Lee, John, and Cheng-Few Lee. "Normal and Lognormal Distributions." In Essentials of Excel VBA, Python, and R. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14236-9_7.

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Lee, Cheng-Few, John Lee, Jow-Ran Chang, and Tzu Tai. "The Normal and Lognormal Distributions." In Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38867-0_7.

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Lee, Cheng-Few, John C. Lee, and Alice C. Lee. "The Normal and Lognormal Distributions." In Statistics for Business and Financial Economics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5897-5_7.

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Baayen, R. Harald. "Mixture distributions." In Word Frequency Distributions. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0844-0_4.

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Conference papers on the topic "Mixture of lognormal distributions"

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Chiodo, Elio. "Bayes Availability Estimation of the “k-out-of-n” Partially Redundant System by Lognormal Prior Distributions." In 2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE, 2024. http://dx.doi.org/10.1109/speedam61530.2024.10609118.

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Maceira, M. Elvira P., Albert C. G. Melo, and J. Francisco M. Pessanha. "Comparing the Performance of Three-Parameter Weibull and Lognormal Distributions in the Generation of Energy Inflows Synthetic Scenarios." In 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2024. http://dx.doi.org/10.1109/pmaps61648.2024.10667335.

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Zeng, Jun, and Yinghua Tong. "Anomaly Detection Methods for Nonparametric Fitting Gaussian Mixture Distributions for Data Features." In 2024 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC). IEEE, 2024. https://doi.org/10.1109/iiotbdsc64371.2024.00044.

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Gumenyuk, A. S., D. A. Dzhedirov, Yu I. Bulygin, and I. S. Kuptsova. "APPROXIMATION FEATURES OF THE PARTICLE SIZE DISTRIBUTION OF METAL MACHINING DUST USING FINITE AND INFINITE FUNCTIONS." In INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION. ООО «ДГТУ-Принт» Адрес полиграфического предприятия: 344003, г. Ростов-на-Дону, пл. Гагарина,1., 2023. http://dx.doi.org/10.23947/itse.2023.109-114.

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Determining the metal machining particle size distribution is crucial for aspiration systems design. The main objective of this research is to describe the metal-abrasive machining dust mixture particle size distribution produced in the grinding machine operator working area using finite and infinite functions. The initial particle size distribution of the dust mixture is approximated by model distributions, and the calculation results are compared with experimental data. The lognormal infinite distribution better of all describes the considered dust. It is determined that the model function a
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da Silva, Vinicius R., and Abbas Yongacoglu. "EM algorithm on the approximation of arbitrary PDFs by Gaussian, gamma and lognormal mixture distributions." In 2015 7th IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2015. http://dx.doi.org/10.1109/latincom.2015.7430127.

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de Souza Braun, Meire Pereira, Alice Jordam Caserta, and Helio Aparecido Navarro. "Continuous Particle Size Distributions in a Bubbling Fluidized Bed Using Discrete Element Method." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87419.

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The focus of this paper is to study the behavior of systems with continuous particle size distributions over a gas-solid flow in a bubbling fluidized bed. A lognormal distribution with particle-size range between 800 micrometers and 900 micrometers was used to perform numerical simulations to investigate gas bubbles formation for a polydispersed system. Different drag models were used to predict the bubbles. Species segregation for a binary mixture and a monodispersed system were also studied. Discrete Element Method (DEM) simulations were performed using the source code MFIX (“Multiphase Flow
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Guse, Fabian, Enrico Pasquini, and Katharina Schmitz. "Implementation of Bubble Dynamic Effects by Coupling the Gilmore Equation With Fluid Dynamic Equations Using the Method of Characteristics." In BATH/ASME 2020 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/fpmc2020-2745.

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Abstract In fluid power systems, performance as well as system dynamics are strongly influenced by the presence of bubbles — especially for low system pressures. While the static effect of dissolved air (especially the volume fraction of dissolved air) on the bulk modulus has been extensively investigated in the past, in hydraulics, the dynamic effects due to bubble dynamics have been neglected entirely. Thereby, the dynamic characteristics of the bubbles influence the compressibility of the disperse fluid and, as a consequence, the speed of sound in the mixture and the hydraulic system as a w
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Hurley, Kevin J., Brian McBreen, Fergus Quilligan, Matt Delaney, and Lorraine Hanlon. "Wavelet analysis and lognormal distributions in GRBs." In GAMMA-RAY BURSTS. ASCE, 1998. http://dx.doi.org/10.1063/1.55319.

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Zhao, Lian, and Jiu Ding. "A Strict Approach to Approximating Lognormal Sum Distributions." In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277813.

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Buyukcorak, Saliha, Gunes Karabulut Kurt, and Abbas Yongacoglu. "Lognormal mixture Cramer-Rao lower bound for localization." In 2015 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2015. http://dx.doi.org/10.1109/iwcmc.2015.7289070.

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Reports on the topic "Mixture of lognormal distributions"

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Wilson, D., Matthew Kamrath, Caitlin Haedrich, Daniel Breton, and Carl Hart. Urban noise distributions and the influence of geometric spreading on skewness. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42483.

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Statistical distributions of urban noise levels are influenced by many complex phenomena, including spatial and temporal variations in the source level, multisource mixtures, propagation losses, and random fading from multipath reflections. This article provides a broad perspective on the varying impacts of these phenomena. Distributions incorporating random fading and averaging (e.g., gamma and noncentral Erlang) tend to be negatively skewed on logarithmic (decibel) axes but can be positively skewed if the fading process is strongly modulated by source power variations (e.g., compound gamma).
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Dudel, H. P., and S. H. Lehnigk. Calculation of Quantiles for Hyper-Gamma, Generalized Gumbel, and Lognormal Distributions. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada211521.

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Lee, P. J. Oil and gas pool size probability distributions - J-shaped, lognormal, or Pareto? Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/184100.

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Wadsworth, Spencer. Mixture distributions in collaborative probabilistic forecasting of disease outbreaks. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1007.

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Cobb, Barry R., and Alan W. Johnson. Mixture Distributions for Modeling Lead Time Demand in Coordinated Supply Chains. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612849.

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Abdel-Hamid, Alaa H., and Atef F. Hashem. A New Compound Distribution Based on a Mixture of Distributions and a Mixed System. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.11.01.

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Missov, Trifon I., and Maxim S. Finkelstein. Admissible mixing distributions for a general class of mixture survival models with known asymptotics. Max Planck Institute for Demographic Research, 2011. http://dx.doi.org/10.4054/mpidr-wp-2011-004.

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Lee, Youngrok. Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1116720.

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