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Journal articles on the topic 'Statistical model'

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Published: 4 June 2021
Last updated: 6 July 2024

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1

Li, Ken W. "A Model of Teaching Statistical Computing." International Journal of Information and Education Technology 6, no. 2 (2016): 143–47. http://dx.doi.org/10.7763/ijiet.2016.v6.674.

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2

Iwasaki, Atsushi, Yoshinobu Shimamura, and Akira Todoroki. "OS17-3-6 Optimization of the statistical model for the statistical damage diagnostic method." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS17–3–6——_OS17–3–6—. http://dx.doi.org/10.1299/jsmeatem.2007.6._os17-3-6-.

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3

Choden, Sonam, and Suntaree Unhapipat. "Statistical Model for Personal Loan Prediction in Bhutan." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 416–22. http://dx.doi.org/10.5373/jardcs/v11/20192587.

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4

Claeskens, Gerda. "Statistical Model Choice." Annual Review of Statistics and Its Application 3, no. 1 (June 2016): 233–56. http://dx.doi.org/10.1146/annurev-statistics-041715-033413.

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5

Ahmadi-Hadad, Armia. "Statistical model error." Kidney International 103, no. 6 (June 2023): 1199. http://dx.doi.org/10.1016/j.kint.2023.03.001.

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Ahmadi-Hadad, Armia. "Statistical model error." Diabetes & Metabolic Syndrome: Clinical Research & Reviews 17, no. 4 (April 2023): 102755. http://dx.doi.org/10.1016/j.dsx.2023.102755.

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7

Xie, Yi, Xiaojie Lei, and Peiai Zhang. "Statistical Model for the Trend of Prevalent Languages Speakers." International Journal of Languages, Literature and Linguistics 6, no. 1 (March 2020): 24–30. http://dx.doi.org/10.18178/ijlll.2020.6.1.245.

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8

Kozmenko, Olga, and Viktor Oliynyk. "Statistical model of risk assessment of insurance company’s functioning." Investment Management and Financial Innovations 12, no. 2-1 (August 7, 2015): 189–94. http://dx.doi.org/10.21511/imfi.12(2-1).2015.01.

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9

Komatsu, Kanji. "Statistical Models for Model-Based Drug Development." Japanese Journal of Biometrics 32, Special_Issue_2 (2011): 179–93. http://dx.doi.org/10.5691/jjb.32.179.

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10

Jafari, Ali, Ehsan Khamespanah, Haukur Kristinsson, Marjan Sirjani, and Brynjar Magnusson. "Statistical model checking of Timed Rebeca models." Computer Languages, Systems & Structures 45 (April 2016): 53–79. http://dx.doi.org/10.1016/j.cl.2016.01.004.

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11

Stone, M., Helga Bunke, and Olaf Bunke. "Statistical Inference in Linear Models. Statistical Methods of Model Building, Vol 1." Journal of the Royal Statistical Society. Series A (General) 150, no. 2 (1987): 169. http://dx.doi.org/10.2307/2981636.

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12

WANG, Caibing, Hao GUO, Dingfeng YE, and Ping WANG. "Statistical Model on CRAFT." Chinese Journal of Electronics 31, no. 4 (July 2022): 698–712. http://dx.doi.org/10.1049/cje.2021.00.092.

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13

Dygaszewicz, Janusz, and Bolesław Szafrański. "Statistical surveys — model approach." Wiadomości Statystyczne. The Polish Statistician 63, no. 12 (December 28, 2018): 5–22. http://dx.doi.org/10.5604/01.3001.0014.0726.

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Experiences, both in the area of research and development tasks, as well as those from the project-implementation undertakings concerning IT support of statistical production, indicate that the use of developed mathematical modelling methods is too small in relation to potential possibilities. The aim of the research is to demonstrate that the effects of mathematical modelling in the field of statistical research not only can contribute to the improvement of data processing efficiency in official statistics, but also affect the quality of functional requirements for IT support for statistical surveys. This objective was achieved by discussing the general mathematical model of statistical research, with particular emphasis on the basic phases of statistical production (collection, processing, analysis and dissemination of statistical data), as well as by indicating optimization tasks and benefits resulting from problems that may occur in the process of IT support design. In order to confirm the usefulness of the presented approach, the concept of integration of the effects of mathematical modelling and the traditional design of IT support was presented in the form of a UML diagram.
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14

Lee, L. H., and K. Poolla. "On Statistical Model Validation." Journal of Dynamic Systems, Measurement, and Control 118, no. 2 (June 1, 1996): 226–36. http://dx.doi.org/10.1115/1.2802308.

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In this paper we formulate a particular statistical model validation problem in which we wish to determine the probability that a certain hypothesized parametric uncertainty model is consistent with a given input-output data record. Using a Bayesian approach and ideas from the field of hypothesis testing, we show that in many cases of interest this problem reduces to computing relative weighted volumes of convex sets in RN (where N is the number of uncertain parameters). We also present and discuss a randomized algorithm based on gas kinetics, as well as the existing Hit-and-Run family of algorithms, for probable approximate computation of these volumes.
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15

Barbieri, Roberto R. "Statistical Secondary Recovery Model." SPE Advanced Technology Series 4, no. 01 (May 1, 1996): 44–52. http://dx.doi.org/10.2118/26978-pa.

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16

Yi, Gang, and George Judge. "Statistical model selection criteria." Economics Letters 28, no. 1 (January 1988): 47–51. http://dx.doi.org/10.1016/0165-1765(88)90070-5.

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17

Hariri, Behnoosh, and Shervin Shirmohammadi. "A Statistical Network Traffic Model for First-Person Shooter Games." Journal of Advances in Computer Networks 2, no. 2 (2014): 100–105. http://dx.doi.org/10.7763/jacn.2014.v2.90.

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18

Byung C.Jung and Byeng D.Youn. "CO-KR-4 A Statistical Framework for CAE Model Validation." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _CO—KR—4–1—_CO—KR—4–1. http://dx.doi.org/10.1299/jsmemecj.2012._co-kr-4-1.

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19

Parhomov, Vladimir, Viktor Eselevich, Maxim Eselevich, Battuulai Tsegmed, Sergey Khomutov, Raita Tero, Georgiy Popov, Aleksey Mochalov, Sergey Pilgaev, and Ravil Rakhmatulin. "Correspondence of a global isolated substorm to the McPherron statistical model." Solar-Terrestrial Physics 8, no. 2 (June 30, 2022): 37–46. http://dx.doi.org/10.12737/stp-82202206.

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It is shown that a diamagnetic structure (DS) of the slow solar wind (SW), the source of which on the Sun was a chain of streamers, arrived at Earth’s orbit on December 22, 2015. It interacted with Earth’s magnetosphere under conditions when the northward Bz component of the interplanetary magnetic field (IMF) remained for a long time in preceding undisturbed SW. The interaction and a sharp change in the direction of Bz to the south generated an isolated substorm whose duration depends on the duration of interaction with the DS. The substorm began at midday with the passage of the DS into the magnetosphere and spread to the east. All phases of the substorm — growth, expansion, and recovery — were observed for two hours. Variations in the SW and IMF parameters are shown to coincide for the isolated substorm whose energy source was the slow solar wind DS, and a trigger was the abrupt change in the direction of the vertical IMF component from north to south. The coincidence is justified by statistical generalizations of the same parameters in 40 % of cases of long-term observations of individual substorms whose trigger was a change in Bz direction.
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20

Mishra, Anjay Kumar. "Impact of Agriculture on Economic Development of Nepal using Statistical Model." Journal of Advanced Research in Alternative Energy, Environment and Ecology 8, no. 2 (April 23, 2021): 1–3. http://dx.doi.org/10.24321/2455.3093.202101.

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Nepal has many factors that influence economic development one of the factors in agriculture. The main objectives of this paper are to examine the status of the economic growth of Nepal and dependency on economic growth in Agriculture illustrating statistically.The quantitative cum survey method is employed here to analyse the relationship between the economic growth of Nepal and Agriculture. The macro data used here are based on a secondary source. The data are taken from an economic survey published by the Ministry of Finance, Government of Nepal.Statistical tools like regression analysis, correlations, the coefficient of multiple determinations and the adjusted coefficient of determination have been employed.In Nepal, over the last fifteen years, poverty has remarkably come down despite lower economic growth meaning that reducing poverty would be much easier if the economic growth rate could be increased. Regarding economic growth contribution of the agriculture sector to GDP is gradually decreasing every year, while that of the non-agriculture sector increasing. The contribution of the agriculture sector to GDP is estimated to stand 27.7% and 72.3% respectively in the FY 2019/2020 while their contribution during FY 2018/2019 was 27.0% and 73.0% respectively.
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21

Dimanova, Donika, and Tsvetoslav Tsankov. "STATISTICAL MODEL OF THE INTENSITY OF THE RAINFALL IN SHUMEN REGION." Journal scientific and applied research 4, no. 1 (October 10, 2013): 98–105. http://dx.doi.org/10.46687/jsar.v4i1.86.

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The report justifies the view that despite the apparent trend of warming and drought in recent years, there is an increasing frequency of extreme weather and climatic events (rainfall), which cause extensive damage to infrastructure and high costs to the economy and society. Therefore, to improve the performance and effectiveness of State bodies engaged in the prevention or eradication of harmful effects in emergency situations, it is necessary to make scientifically sound estimates of the frequency of intense precipitation over the next few years. Solving this important practical tasks is possible through the development of a statistical model of the intensity of the precipitation.
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22

Kuusk, Andres, Joel Kuusk, and Mait Lang. "A Statistical Forest Reflectance Model." Remote Sensing 11, no. 23 (November 22, 2019): 2749. http://dx.doi.org/10.3390/rs11232749.

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The variability of forest reflectance among hemiboreal forests can be described with a fewbasis functions. Five basis functions describe almost 98% of variability of directional reflectancespectra in the optical spectral domain (400–1700 nm) in forest stands at the top of a canopy in nadir.A statistical forest reflectance model (SFRM) was developed, the input parameters of which are theforest parameters measured in the course of regular forest inventory. Nadir spectral reflectance ofa forest stand is expressed in the SFRM as a linear combination of basis functions, the weights ofwhich are linear combinations of the 15 stand parameters in the forest inventory database. Multiplecorrelations of the weights on the forest inventory parameters are determined separately for pine,spruce, and broadleaf forests. The basis functions are found from low altitude airborne measurementsover managed forests in southeastern Estonia, where a forest management database is available. Themodel was validated against more than 3000 spectral signatures of forest stands from Sentinel-2Multispectral Imager (MSI) measurements over a test site in southeastern Estonia. In most cases, themodel predicts the forest reflectance spectrum at nadir with a relative error about 20–40%. The errorsof reflectance values are less than 0.02 in most cases. The sole exception is the reflectance of broadleafstands, which in near infrared bands of Sentinel-2 MSI is overestimated by 0.02–0.05.
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23

WATANABE, Norio. "A Fuzzified Statistical Model Selection." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 6 (1993): 1372–82. http://dx.doi.org/10.3156/jfuzzy.5.6_1372.

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24

AUGUSTO, Paulo Sergio Pilz, Elso DRIGO FILHO, and Jose Roberto RUGGIERO. "Statistical Model to DNA Melting." Eclética Química 26 (2001): 77–85. http://dx.doi.org/10.1590/s0100-46702001000100006.

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25

Yang, Rumin, Bin Shen, and Kyung Sup Kwak. "Pure statistical indoor pathloss model." IEICE Electronics Express 7, no. 8 (2010): 527–33. http://dx.doi.org/10.1587/elex.7.527.

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26

Murayama, Norifumi, and Manabu Okumura. "Statistical model for Japanese abbreviations." Intelligent Data Analysis 14, no. 4 (July 22, 2010): 465–78. http://dx.doi.org/10.3233/ida-2010-0432.

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27

Becattini, Francesco, and Lorenzo Ferroni. "Statistical model and microcanonical ensemble." Journal of Physics G: Nuclear and Particle Physics 31, no. 6 (May 23, 2005): S1091—S1094. http://dx.doi.org/10.1088/0954-3899/31/6/065.

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28

Kachapova, Farida, and Ilias Kachapov. "Interaction Model in Statistical Mechanics." Journal of Mathematics and Statistics 13, no. 4 (April 1, 2017): 339–46. http://dx.doi.org/10.3844/jmssp.2017.339.346.

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29

Ordonez, Carlos. "Statistical Model Computation with UDFs." IEEE Transactions on Knowledge and Data Engineering 22, no. 12 (December 2010): 1752–65. http://dx.doi.org/10.1109/tkde.2010.44.

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30

Foster, Theda A., Donna L. Mohr, and Robert C. Elston. "A statistical model of tracking." Communications in Statistics - Theory and Methods 18, no. 8 (1989): 2861–81. http://dx.doi.org/10.1080/03610928908830066.

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31

Major, Seth A., and Kevin L. Setter. "Gravitational statistical mechanics: a model." Classical and Quantum Gravity 18, no. 23 (November 22, 2001): 5125–41. http://dx.doi.org/10.1088/0264-9381/18/23/309.

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32

PITHIOUX, M., P. CHABRAND, and F. MAZEROLLE. "STATISTICAL FAILURE MODEL OF BONES." Journal of Mechanics in Medicine and Biology 02, no. 01 (March 2002): 19–27. http://dx.doi.org/10.1142/s0219519402000125.

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In clinical research, knowledge of the mechanical behavior of bones is helpful for diagnostics and therapeutic processes and the failure of compact bones is a necessary study in clinical analysis, accidentology, and traumatology. The purpose of this paper is to analyse the failure properties of compact bones using a statistical model to interpret stress and strain measurements obtained by INSTRON and X-ray scanner devices. Samples were prepared from a lamellar structure of compact bovine bones and the density of each sample is controlled and taken to be constant (1.9 g/cm 3). The experimental results data thus depend only on defects in the samples. This model may help physicians and surgeons predict bone failure when inserting a prosthesis, for example.
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33

McCullagh, Peter. "What is a statistical model?" Annals of Statistics 30, no. 5 (October 2002): 1225–310. http://dx.doi.org/10.1214/aos/1035844977.

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34

Vandenbosch, R. "Statistical model for fragmentation ofC60." Physical Review A 59, no. 5 (May 1, 1999): 3584–87. http://dx.doi.org/10.1103/physreva.59.3584.

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35

Birgé, Lucien. "Statistical estimation with model selection." Indagationes Mathematicae 17, no. 4 (December 2006): 497–537. http://dx.doi.org/10.1016/s0019-3577(07)00004-3.

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36

Uchaikin, V. V., and R. T. Sibatov. "Statistical model of fluorescence blinking." Journal of Experimental and Theoretical Physics 109, no. 4 (October 2009): 537–46. http://dx.doi.org/10.1134/s106377610910001x.

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37

Kelkar, N. G., and M. Nowakowski. "Statistical model for pionic partons." Physics Letters B 513, no. 1-2 (July 2001): 77–82. http://dx.doi.org/10.1016/s0370-2693(01)00377-x.

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38

Morgenstern, O., I. M. Sokolov, and A. Blumen. "Statistical Model for Surface Fracture." Europhysics Letters (EPL) 22, no. 7 (June 1, 1993): 487–92. http://dx.doi.org/10.1209/0295-5075/22/7/002.

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39

Larsen, Gunner Chr, and Kurt S. Hansen. "Statistical Model of Extreme Shear." Journal of Solar Energy Engineering 127, no. 4 (February 18, 2005): 444–55. http://dx.doi.org/10.1115/1.2035702.

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In order to continue cost-optimization of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function (PDF) of turbulence driven short-term extreme wind shear events, conditioned on the mean wind speed, for an arbitrary recurrence period. The model is based on an asymptotic expansion, and only a few and easily accessible parameters are needed as input. The model of the extreme PDF is supplemented by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate. The measurements have been extracted from ”Database on Wind Characteristics” (http:∕∕www.winddata.com∕), and they refer to a site characterized by a flat homogeneous terrain. The comparison has been conducted for three different mean wind speeds in the range of 15-19m∕s, and model predictions and experimental results are consistent, given the inevitable uncertainties associated with the model as well as with the extreme value data analysis.
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40

van den Bos, A. "Parametric statistical model-based measurement." Measurement 14, no. 1 (September 1994): 55–61. http://dx.doi.org/10.1016/0263-2241(94)90043-4.

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41

DeAngelis, A. R., and A. Z. Mekjian. "Statistical model of nuclear multifragmentation." Physical Review C 40, no. 1 (July 1, 1989): 105–14. http://dx.doi.org/10.1103/physrevc.40.105.

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42

Sornette, Didier, Christian Vanneste, and Leon Knopoff. "Statistical model of earthquake foreshocks." Physical Review A 45, no. 12 (June 1, 1992): 8351–57. http://dx.doi.org/10.1103/physreva.45.8351.

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43

Berk, Richard, Lawrence Brown, and Linda Zhao. "Statistical Inference After Model Selection." Journal of Quantitative Criminology 26, no. 2 (October 20, 2009): 217–36. http://dx.doi.org/10.1007/s10940-009-9077-7.

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44

Tsallis, C., A. M. C. de Souza, and E. M. F. Curado. "Stock exchange: A statistical model." Chaos, Solitons & Fractals 6 (January 1995): 561–67. http://dx.doi.org/10.1016/0960-0779(95)80063-m.

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45

Krantz, D. A., T. W. Hallahan, V. James Macri, and James N. Macri. "Statistical flaw in SURUSS model." Prenatal Diagnosis 24, no. 9 (September 2004): 753–54. http://dx.doi.org/10.1002/pd.930.

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46

Augusto, Paulo Sergio Pilz, Elso Drigo Filho, and Jose Roberto Ruggiero. "Statistical model to DNA melting." Ecletica Quimica 26, no. 1 (December 3, 2001): 77–85. http://dx.doi.org/10.26850/1678-4618eqj.v26.1.2001.p77-85.

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47

Grant, Andrew. "Statistical model simulates cicada surges." Physics Today 2024, no. 01 (January 31, 2024): .zntw. http://dx.doi.org/10.1063/pt.uegh.zntw.

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48

Han, Yu. "Semi-Parametric Statistical Model for Extreme Value Statistical Models and Application in Automatic Control." Applied Mechanics and Materials 680 (October 2014): 455–58. http://dx.doi.org/10.4028/www.scientific.net/amm.680.455.

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The frequency that extreme events appear in the life is low,but once it appears,the impact will be significant; many scholars have conducted in depth research and found that statistical theory of extreme value. The theory of extreme statistics plays a more and more important role in many fields such as automatic control, assembly line etc. This paper,makes an in-depth research towards the characteristics and parameter estimation of the extreme value statistical models,as well as the application,mainly analyzes the Bayes parameter estimation method of extreme value distribution,the extreme value distribution theory and Copula function random vector model.
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49

张, 彤. "Bus Mobile Payment Model Based on Statistical Model." Statistics and Application 08, no. 02 (2019): 320–33. http://dx.doi.org/10.12677/sa.2019.82036.

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50

Rudnyi, E. B. "Statistical model of systematic errors: linear error model." Chemometrics and Intelligent Laboratory Systems 34, no. 1 (August 1996): 41–54. http://dx.doi.org/10.1016/0169-7439(96)00004-4.

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