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Journal articles on the topic 'Nonlinear statistical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Ford, I., and A. R. Gallant. "Nonlinear Statistical Models." Biometrics 45, no. 2 (1989): 694. http://dx.doi.org/10.2307/2531513.

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2

Wolak, Frank A., and A. Ronald Gallant. "Nonlinear Statistical Models." Journal of Business & Economic Statistics 6, no. 4 (1988): 518. http://dx.doi.org/10.2307/1391473.

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3

Tsai, Chih-Ling, and A. Ronald Gallant. "Nonlinear Statistical Models." Journal of the American Statistical Association 84, no. 405 (1989): 336. http://dx.doi.org/10.2307/2289891.

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4

Bates, Douglas. "Nonlinear Statistical Models." Technometrics 30, no. 4 (1988): 453–54. http://dx.doi.org/10.1080/00401706.1988.10488443.

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5

Katti, S. K., and Prabha Betne. "Nonlinear Statistical Models." Technometrics 36, no. 4 (1994): 433–34. http://dx.doi.org/10.1080/00401706.1994.10485867.

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6

Parker, Gene. "Statistical change detection using nonlinear models." Journal of the Acoustical Society of America 101, no. 5 (1997): 3044. http://dx.doi.org/10.1121/1.418677.

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7

Duffin, Connor, Edward Cripps, Thomas Stemler, and Mark Girolami. "Statistical finite elements for misspecified models." Proceedings of the National Academy of Sciences 118, no. 2 (2020): e2015006118. http://dx.doi.org/10.1073/pnas.2015006118.

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We present a statistical finite element method for nonlinear, time-dependent phenomena, illustrated in the context of nonlinear internal waves (solitons). We take a Bayesian approach and leverage the finite element method to cast the statistical problem as a nonlinear Gaussian state–space model, updating the solution, in receipt of data, in a filtering framework. The method is applicable to problems across science and engineering for which finite element methods are appropriate. The Korteweg–de Vries equation for solitons is presented because it reflects the necessary complexity while being su

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8

Dare, Tyler, Rebecca McDaniel, and Ayesha Shah. "Nonlinear, statistical models of tire-pavement noise." Noise Control Engineering Journal 64, no. 3 (2016): 324–34. http://dx.doi.org/10.3397/1/376382.

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9

Zhu, Hong-Tu, and Sik-Yum Lee. "Statistical analysis of nonlinear factor analysis models." British Journal of Mathematical and Statistical Psychology 52, no. 2 (1999): 225–42. http://dx.doi.org/10.1348/000711099159080.

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10

Lin, Jin-Guan, Feng-Chang Xie, and Bo-Cheng Wei. "Statistical Diagnostics for Skew-t-Normal Nonlinear Models." Communications in Statistics - Simulation and Computation 38, no. 10 (2009): 2096–110. http://dx.doi.org/10.1080/03610910903249502.

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11

Ross, Gavin J. S., Prajneshu, and C. Sarada. "Reparameterization of nonlinear statistical models: a case study." Journal of Applied Statistics 37, no. 12 (2010): 2015–26. http://dx.doi.org/10.1080/02664760903207332.

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12

Campbell, Edward P. "Statistical Modeling in Nonlinear Systems." Journal of Climate 18, no. 16 (2005): 3388–99. http://dx.doi.org/10.1175/jcli3459.1.

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Abstract The use of linear statistical methods in building climate prediction models is examined, particularly the use of anomalies. The author’s perspective is that the climate system is a nonlinear interacting system, so the impact of modeling using anomalies rather than observed data directly is considered. With reference to the Lorenz system and a simple model for regime dependence, it is shown that anomalies impair our ability to reconstruct nonlinear dynamics. Some alternative approaches in the literature that offer an attractive way forward are explored, focusing on Bayesian hierarchica

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13

Mariani, Maria C., Francis Biney, and Osei K. Tweneboah. "Analyzing Medical Data by Using Statistical Learning Models." Mathematics 9, no. 9 (2021): 968. http://dx.doi.org/10.3390/math9090968.

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In this work, we investigated the prognosis of three medical data specifically, breast cancer, heart disease, and prostate cancer by using 10 machine learning models. We applied all 10 models to each dataset to identify patterns in them. Furthermore, we use the models to diagnose risk factors that increases the chance of these diseases. All the statistical learning techniques discussed were grouped into linear and nonlinear models based on their similarities and learning styles. The models performances were significantly improved by selecting models while taking into account the bias-variance

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14

Barton, Curtis N., Robert C. Braunberg, and Leonard Friedman. "Nonlinear Statistical Models for the Joint Action of Toxins." Biometrics 49, no. 1 (1993): 95. http://dx.doi.org/10.2307/2532605.

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15

Que, Ye, Zhensheng Huang, and Riquan Zhang. "Statistical estimation in partially nonlinear models with random effects." Statistical Theory and Related Fields 1, no. 2 (2017): 227–33. http://dx.doi.org/10.1080/24754269.2017.1396425.

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16

Cysneiros, Francisco José A., and Luis Hernando Vanegas. "Residuals and their statistical properties in symmetrical nonlinear models." Statistics & Probability Letters 78, no. 18 (2008): 3269–73. http://dx.doi.org/10.1016/j.spl.2008.06.011.

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17

周, 梦齐. "Statistical Analysis for Nonlinear Joint Mean and Variance Models." Statistical and Application 03, no. 02 (2014): 68–75. http://dx.doi.org/10.12677/sa.2014.32010.

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18

程, 忠. "Statistical Diagnostics for Nonlinear Models with Right-Censored Data." Statistics and Application 07, no. 06 (2018): 614–21. http://dx.doi.org/10.12677/sa.2018.76070.

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19

Gao, Jiti, and Hua Liang. "Statistical Inference in Single-Index and Partially Nonlinear Models." Annals of the Institute of Statistical Mathematics 49, no. 3 (1997): 493–517. http://dx.doi.org/10.1023/a:1003118812392.

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20

Greenberg, William, Ludmila Uvarova, and Tatyana Naumovich. "Mathematical models in nonlinear statistical mechanics second international conference." Transport Theory and Statistical Physics 26, no. 3 (1997): 379–82. http://dx.doi.org/10.1080/00411459708020294.

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21

Greenberg, William. "Mathematical models in nonlinear statistical mechanics third international conference." Transport Theory and Statistical Physics 28, no. 2 (1999): 191–94. http://dx.doi.org/10.1080/00411459908205657.

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22

Irwin, Mark E., Noel Cressie, and Gardar Johannesson. "Spatial-temporal nonlinear filtering based on hierarchical statistical models." Test 11, no. 2 (2002): 249–302. http://dx.doi.org/10.1007/bf02595708.

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23

Tsykin, E. N. "Multiple nonlinear statistical models for runoff simulation and prediction." Journal of Hydrology 77, no. 1-4 (1985): 209–26. http://dx.doi.org/10.1016/0022-1694(85)90207-0.

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24

Patzelt, Felix, and Jean-Philippe Bouchaud. "Nonlinear price impact from linear models." Journal of Statistical Mechanics: Theory and Experiment 2017, no. 12 (2017): 123404. http://dx.doi.org/10.1088/1742-5468/aa9335.

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25

LuValle, M. J., T. L. Welsher, and K. Svoboda. "Acceleration transforms and statistical kinetic models." Journal of Statistical Physics 52, no. 1-2 (1988): 311–30. http://dx.doi.org/10.1007/bf01016417.

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26

Ruj�n, P�l. "Cellular automata and statistical mechanical models." Journal of Statistical Physics 49, no. 1-2 (1987): 139–222. http://dx.doi.org/10.1007/bf01009958.

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27

Tsykin, E. N. "Multiple Statistical models for simulation and prediction of nonlinear processes." Stochastic Analysis and Applications 3, no. 4 (1985): 485–509. http://dx.doi.org/10.1080/07362998508809075.

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28

Blankenship, Erin E., Walter W. Stroup, Sean P. Evans, and Stevan Z. Knezevic. "Statistical inference for calibration points in nonlinear mixed effects models." Journal of Agricultural, Biological, and Environmental Statistics 8, no. 4 (2003): 455–68. http://dx.doi.org/10.1198/1085711032642.

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29

Dennis, Brian, Robert A. Desharnais, J. M. Cushing, and R. F. Costantino. "Nonlinear Demographic Dynamics: Mathematical Models, Statistical Methods, and Biological Experiments." Ecological Monographs 65, no. 3 (1995): 261–82. http://dx.doi.org/10.2307/2937060.

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30

Feng, San Ying, Gao Rong Li, and Jun Hua Zhang. "Efficient statistical inference for partially nonlinear errors-in-variables models." Acta Mathematica Sinica, English Series 30, no. 9 (2014): 1606–20. http://dx.doi.org/10.1007/s10114-014-1358-x.

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31

So, Mike K. P., and Ray S. W. Chung. "Statistical inference for conditional quantiles in nonlinear time series models." Journal of Econometrics 189, no. 2 (2015): 457–72. http://dx.doi.org/10.1016/j.jeconom.2015.03.037.

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32

Bates, Bryson C. "Nonlinear, discrete flood event models, 2. Assessment of statistical nonlinearity." Journal of Hydrology 99, no. 1-2 (1988): 77–89. http://dx.doi.org/10.1016/0022-1694(88)90079-0.

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33

Hinds, M. A., and G. A. Milliken. "Statistical Methods for Using Nonlinear Models to Compare Silage Treatments." Biometrical Journal 29, no. 7 (1987): 825–34. http://dx.doi.org/10.1002/bimj.4710290711.

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34

Sinha, A., and P. Roy. "PT symmetric models with nonlinear pseudosupersymmetry." Journal of Mathematical Physics 46, no. 3 (2005): 032102. http://dx.doi.org/10.1063/1.1843273.

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35

MacKay, N. J. "Boundary integrability of nonlinear sigma models." Theoretical and Mathematical Physics 142, no. 2 (2005): 270–74. http://dx.doi.org/10.1007/s11232-005-0069-y.

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36

Benzi, Roberto, Boris Levant, Itamar Procaccia, and Edriss S. Titi. "Statistical properties of nonlinear shell models of turbulence from linear advection models: rigorous results." Nonlinearity 20, no. 6 (2007): 1431–41. http://dx.doi.org/10.1088/0951-7715/20/6/006.

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37

Ciuperca, Gabriela. "A Method to Treat Some Dynamical Statistical Models." Journal of Biological Systems 06, no. 04 (1998): 357–75. http://dx.doi.org/10.1142/s0218339098000236.

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In this paper we present a method for the estimation of the parameters of models described by a nonlinear system of differential equations: we study the maximum likelihood estimator and the jackknife estimator for parameters of the system and for the covariance matrix of the state variables and we seek possible linear relations between parameters. We take into account the difficulty due to the small number of observations. The optimal experimental design for this kind of problem is determined. We give an application of this method for the glucose metabolism of goats.

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38

Eglit, Y. Y., К. Y. Eglite, A. R. Balybin, and A. S. Gramatskiy. "ALGORITHM FOR ESTIMATING PARAMETERS OF THE NONLINEAR SYSTEM." System analysis and logistics 2, no. 28 (2021): 52–57. http://dx.doi.org/10.31799/2077-5687-2021-2-52-57.

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The article presents an algorithm for estimating the parameters of nonlinear systems, which is one of the main tasks of classical statistical analysis. The parametric estimation of the coefficients of models that are based on experimental data is the basis. The basis for evaluating parameters. Key words: estimation algorithm, nonlinear systems, statistical analysis, nonlinear function, experiment, models.

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39

Cugliandolo, Leticia F., David S. Dean, and Hajime Yoshino. "Nonlinear susceptibilities of spherical models." Journal of Physics A: Mathematical and Theoretical 40, no. 16 (2007): 4285–303. http://dx.doi.org/10.1088/1751-8113/40/16/003.

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40

Jiang, Yu Ying, and Yong Ming Zhang. "Statistical Inference for a Kind of Nonlinear Regression Model." Advanced Materials Research 616-618 (December 2012): 2149–52. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.2149.

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As we all know, statistical inference of linear models has been a hot topic of statistical and econometric research. However, in many practical problems, the variable of interest and covariates are often nonlinear relationship. The performance of the statistical inference using linear models model can be very poor. In this paper, the statistical inference of a nonlinear regression model under some additional restricted conditions is investigated. The restricted estimator for the unknown parameter is proposed. Under some mild conditions, the asymptotic normality of the proposed estimator is est

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41

Calderhead, Ben, and Mark Girolami. "Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods." Interface Focus 1, no. 6 (2011): 821–35. http://dx.doi.org/10.1098/rsfs.2011.0051.

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Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and

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42

Statistical Consultants, Inc. "PCNONLIN and NONLIN84: Software for the Statistical Analysis of Nonlinear Models." American Statistician 40, no. 1 (1986): 52. http://dx.doi.org/10.2307/2683122.

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43

Centurelli, Francesco, Alberto Di Martino, Giuseppe Scotti, Pasquale Tommasino, and Alessandro Trifiletti. "Extraction of CAD-compatible statistical nonlinear models of GaAs HEMT MMICs." Microwave and Optical Technology Letters 51, no. 9 (2009): 2163–66. http://dx.doi.org/10.1002/mop.24543.

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44

Li, Runze, and Lei Nie. "Efficient Statistical Inference Procedures for Partially Nonlinear Models and their Applications." Biometrics 64, no. 3 (2007): 904–11. http://dx.doi.org/10.1111/j.1541-0420.2007.00937.x.

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45

Ord, J. K., A. B. Koehler, and R. D. Snyder. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models." Journal of the American Statistical Association 92, no. 440 (1997): 1621–29. http://dx.doi.org/10.1080/01621459.1997.10473684.

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46

Mahfouz, Mohamed, Ahmed Badawi, Brandon Merkl, et al. "Patella sex determination by 3D statistical shape models and nonlinear classifiers." Forensic Science International 173, no. 2-3 (2007): 161–70. http://dx.doi.org/10.1016/j.forsciint.2007.02.024.

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47

Yang, Yuqing, and Shongming Huang. "On the statistical and biological behaviors of nonlinear mixed forest models." European Journal of Forest Research 132, no. 5-6 (2013): 727–36. http://dx.doi.org/10.1007/s10342-013-0705-2.

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48

Bates, Bryson C. "Nonlinear, discrete flood event models, 2. Assessment of statistical nonlinearity — Correction." Journal of Hydrology 113, no. 1-4 (1990): 369–70. http://dx.doi.org/10.1016/0022-1694(90)90184-y.

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49

Panwar, Sanjeev, K. N. Singh, Ranjit Kumar Paul, et al. "Forecasting fish yield using statistical nonlinear growth models – A reparameterization concept." Indian Journal of Extension Education 56, no. 3 (2020): 9–14. http://dx.doi.org/10.5958/2454-552x.2020.00002.x.

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50

Meyer, H., J.-C. Anglès d'Auriac, and H. Bruus. "Spectral properties of statistical mechanics models." Journal of Physics A: Mathematical and General 29, no. 18 (1996): L483—L488. http://dx.doi.org/10.1088/0305-4470/29/18/006.

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