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Journal articles on the topic 'Linear model'

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1

Kubáček, Lubomír. "Linear model with inaccurate variance components." Applications of Mathematics 41, no. 6 (1996): 433–45. http://dx.doi.org/10.21136/am.1996.134336.

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2

Wimmer, Gejza. "Linear-quadratic estimators in a special structure of the linear model." Applications of Mathematics 40, no. 2 (1995): 81–105. http://dx.doi.org/10.21136/am.1995.134282.

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3

Syafei, Arie Dipareza, Akimasa Fujiwara, and Junyi Zhang. "Prediction Model of Air Pollutant Levels Using Linear Model with Component Analysis." International Journal of Environmental Science and Development 6, no. 7 (2015): 519–25. http://dx.doi.org/10.7763/ijesd.2015.v6.648.

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4

STUTE, WINFRIED, and LI-XING ZHU. "Model Checks for Generalized Linear Models." Scandinavian Journal of Statistics 29, no. 3 (September 2002): 535–45. http://dx.doi.org/10.1111/1467-9469.00304.

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5

Borowiak, Dale. "Linear Models: A Mean Model Approach." Technometrics 39, no. 4 (November 1997): 425–26. http://dx.doi.org/10.1080/00401706.1997.10485163.

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6

Anderson-Cook, C. M. "Linear models: A mean model approach." Journal of Statistical Planning and Inference 64, no. 1 (October 1997): 153–55. http://dx.doi.org/10.1016/s0378-3758(97)86520-2.

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7

Champ, Charles W. "Linear Models: A Mean Model Approach." Journal of Quality Technology 32, no. 2 (April 2000): 191–92. http://dx.doi.org/10.1080/00224065.2000.11979992.

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8

Müller, Samuel, J. L. Scealy, and A. H. Welsh. "Model Selection in Linear Mixed Models." Statistical Science 28, no. 2 (May 2013): 135–67. http://dx.doi.org/10.1214/12-sts410.

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9

Muske, Kenneth R., and James B. Rawlings. "Model predictive control with linear models." AIChE Journal 39, no. 2 (February 1993): 262–87. http://dx.doi.org/10.1002/aic.690390208.

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10

Shalabh. "Linear Model Methodology." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 4 (September 20, 2010): 935–36. http://dx.doi.org/10.1111/j.1467-985x.2010.00663_5.x.

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11

Cardot, Hervé, Frédéric Ferraty, and Pascal Sarda. "Functional linear model." Statistics & Probability Letters 45, no. 1 (October 1999): 11–22. http://dx.doi.org/10.1016/s0167-7152(99)00036-x.

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12

Ghosh, Subir. "Linear Model Methodology." Journal of Quality Technology 44, no. 1 (January 2012): 80. http://dx.doi.org/10.1080/00224065.2012.11917883.

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13

Guo, Zhengfeng. "Linear model methodology." Journal of Applied Statistics 39, no. 8 (August 2012): 1846–47. http://dx.doi.org/10.1080/02664763.2012.679823.

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14

Veselá, Z., L. Vostrý, and P. Šafus. "Linear and linear-threshold model for genetic parameters for SEUROP carcass traits in Czech beef cattle." Czech Journal of Animal Science 56, No. 9 (September 19, 2011): 414–26. http://dx.doi.org/10.17221/1292-cjas.

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The objective of this study was to estimate genetic parameters for the results of classifying of carcass traits by the SEUROP method in beef cattle in the Czech Republic using linear and linear-threshold models. Genetic parameters were calculated and evaluated in a set of 4276 animals of eleven beef breeds and crosses with dairy and dual-purpose breeds (Aberdeen Angus – 1376, Hereford – 994, Simmental – 651, Charolais – 524, Piemontese – 185, Galloway – 162, Blonde d’Aquitaine – 147, Limousine – 106, Highland – 53, Gasconne – 44, Belgian Blue – 34) in 2005–2008. Aberdeen Angus, Hereford, Charolais and beef Simmental were the most numerous breeds. Fixed effect of a classifier, fixed regression on age at slaughter by means of Legendre polynomial of the second degree separately for the each breed and sex and fixed regression on heterosis coefficient were included in a model equation. Genetic parameters were estimated by a multi-trait animal model using a linear model and a linear-threshold model in which carcass weight (CW) was considered as the linear trait and carcass conformation (CC) and carcass fatness (CF) grading as threshold traits. The heritability coefficient for CW differed only moderately according to the method of the genetic parameter estimation (0.295 in linear model and 0.306 in linear-threshold model). The heritability coefficient for CC was 0.187 in linear model and 0.237 in linear-threshold model. The heritability coefficient for CF grading was 0.089 in linear model and 0.146 in linear-threshold model. Genetic correlation between CW and CC was high (0.823 in linear model and 0.959 in linear-threshold model), the correlation between CW and CF was intermediate (0.332 and 0.328, respectively) and it was low between CF and CC (0.071 and 0.053). If CW was included in the model equation as fixed regression using Legendre polynomial, lower heritability coefficients for CC (0.077 and 0.078) and CF (0.086 and 0.123) were calculated and the correlation between CC and CF was negative (–0.430 and –0.429).
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15

Helin, Runar, Ulf Indahl, Oliver Tomic, and Kristian Hovde Liland. "Non-linear shrinking of linear model errors." Analytica Chimica Acta 1258 (June 2023): 341147. http://dx.doi.org/10.1016/j.aca.2023.341147.

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16

Pan, Wei, and Chap T. Le. "Bootstrap model selection in generalized linear models." Journal of Agricultural, Biological, and Environmental Statistics 6, no. 1 (March 2001): 49–61. http://dx.doi.org/10.1198/108571101300325139.

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17

Yao, Weixin, and Longhai Li. "A New Regression Model: Modal Linear Regression." Scandinavian Journal of Statistics 41, no. 3 (October 31, 2013): 656–71. http://dx.doi.org/10.1111/sjos.12054.

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18

Ni, Xiao, Hao Helen Zhang, and Daowen Zhang. "Automatic model selection for partially linear models." Journal of Multivariate Analysis 100, no. 9 (October 2009): 2100–2111. http://dx.doi.org/10.1016/j.jmva.2009.06.009.

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19

Peng, Heng, and Ying Lu. "Model selection in linear mixed effect models." Journal of Multivariate Analysis 109 (August 2012): 109–29. http://dx.doi.org/10.1016/j.jmva.2012.02.005.

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20

Buscemi, Simona, and Antonella Plaia. "Model selection in linear mixed-effect models." AStA Advances in Statistical Analysis 104, no. 4 (October 28, 2019): 529–75. http://dx.doi.org/10.1007/s10182-019-00359-z.

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21

Raftery, Adrian E., David Madigan, and Jennifer A. Hoeting. "Bayesian Model Averaging for Linear Regression Models." Journal of the American Statistical Association 92, no. 437 (March 1997): 179–91. http://dx.doi.org/10.1080/01621459.1997.10473615.

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22

He, Xin, and Junhui Wang. "Discovering model structure for partially linear models." Annals of the Institute of Statistical Mathematics 72, no. 1 (July 30, 2018): 45–63. http://dx.doi.org/10.1007/s10463-018-0682-9.

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23

Dreyhaupt, Jens, and Ulrich Mansmann. "S34.1: Model comparison for linear mixed models." Biometrical Journal 46, S1 (March 2004): 72. http://dx.doi.org/10.1002/bimj.200490125.

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24

Song, Xin-Yuan, and Sik-Yum Lee. "Model comparison of generalized linear mixed models." Statistics in Medicine 25, no. 10 (2006): 1685–98. http://dx.doi.org/10.1002/sim.2318.

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25

Noy, Dominic, and Raquel Menezes. "Parameter estimation of the Linear Phase Correction model by hierarchical linear models." Journal of Mathematical Psychology 84 (June 2018): 1–12. http://dx.doi.org/10.1016/j.jmp.2018.03.008.

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26

Adamec, Z. "Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship." Journal of Forest Science 61, No. 10 (June 3, 2016): 439–47. http://dx.doi.org/10.17221/68/2015-jfs.

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27

Kudeláš, Jaromír. "The linear model with variance-covariance components and jackknife estimation." Applications of Mathematics 39, no. 2 (1994): 111–25. http://dx.doi.org/10.21136/am.1994.134248.

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28

Wilandari, Yuciana, Sri Haryatmi Kartiko, and Adhitya Ronnie Effendie. "ESTIMASI CADANGAN KLAIM MENGGUNAKAN GENERALIZED LINEAR MODEL (GLM) DAN COPULA." Jurnal Gaussian 9, no. 4 (December 7, 2020): 411–20. http://dx.doi.org/10.14710/j.gauss.v9i4.29260.

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In the articles of this will be discussed regarding the estimated reserves of the claim using the Generalized Linear Model (GLM) and Copula. Copula is a pair function distribution marginal becomes a function of distribution of multivariate. The use of copula regression in this article is to produce estimated reserves of claims. Generalized Linear Model (GLM) used as a marginal model for several lines of business. In research it is used three kinds of line of business that is individual, corporate and professional. The copula used is the Archimedean type of copula, namely Clayton and Gumbel copula. The best copula selection method is done using Akaike Information Criteria (AIC). Maximum Likelihood Estimation (MLE) is used to estimate copula parameters. The copula model used is the Clayton copula as the best copula. The parameter estimation results are used to obtain the estimated reserve value of the claim.
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29

Wang, Xun, Shoumei Li, and Thierry Denaoelig;ux. "Interval-Valued Linear Model." International Journal of Computational Intelligence Systems 8, no. 1 (2015): 114. http://dx.doi.org/10.2991/ijcis.2015.8.1.10.

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30

Segal, Arthur C. "A Linear Diet Model." College Mathematics Journal 18, no. 1 (January 1987): 44. http://dx.doi.org/10.2307/2686315.

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31

VERRAN, JOYCE A., and SANDRA L. FERKETICH. "Testing Linear Model Assumptions." Nursing Research 36, no. 2 (March 1987): 127???129. http://dx.doi.org/10.1097/00006199-198703000-00014.

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32

Lian, Heng. "Functional partial linear model." Journal of Nonparametric Statistics 23, no. 1 (March 2011): 115–28. http://dx.doi.org/10.1080/10485252.2010.500385.

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33

Syrkin, Ilya. "Linear Synchronous Motor Model." MATEC Web of Conferences 297 (2019): 02006. http://dx.doi.org/10.1051/matecconf/201929702006.

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Liner synchronous motor (LSM) is perspective motor for milling machines and other manufacturing equipment such as 3D printers, laser cutting and engraving machines, etc. Some different construction of LSM can be found. The LSM with permanent magnets is modeled in this paper. Comsol Multiphysics is used for finite element model of LSM. Motor stator is built using rear earth magnets N52, an anchor consists of 6 teeth with 3-phase winding. Large cogging force is the problem of LSM, so there is task to reduce this force. This task can be solved by motor geometry optimization. Geometric parameter of motor is represented by variables. It allowed using optimization methods for best geometry search. FEM model with two different mesh sizes is analysed in this paper. Each mesh allows find solution but calculation time and tolerance are different. During experiments, optimal size of tooth for maximal driving force is found. Cogging force is also reduced.
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34

WILLEMS, SANDER. "LINEAR STOCHASTIC DIVIDEND MODEL." International Journal of Theoretical and Applied Finance 23, no. 07 (October 22, 2020): 2050044. http://dx.doi.org/10.1142/s0219024920500442.

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In this paper, we propose a new model for pricing stock and dividend derivatives. We jointly specify dynamics for the stock price and the dividend rate such that the stock price is positive and the dividend rate nonnegative. In its simplest form, the model features a dividend rate that is mean-reverting around a constant fraction of the stock price. The advantage of directly specifying dynamics for the dividend rate, as opposed to the more common approach of modeling the dividend yield, is that it is easier to keep the distribution of cumulative dividends tractable. The model is nonaffine but does belong to the more general class of polynomial processes, which allows us to compute all conditional moments of the stock price and the cumulative dividends explicitly. In particular, we have closed-form expressions for the prices of stock and dividend futures. Prices of stock and dividend options are accurately approximated using a moment matching technique based on the principle of maximal entropy.
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35

Segal, Arthur C. "A Linear Diet Model." College Mathematics Journal 18, no. 1 (January 1987): 44–45. http://dx.doi.org/10.1080/07468342.1987.11973005.

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36

Lakshminarayanan, Mani Y. "The General Linear Model." Technometrics 30, no. 1 (February 1988): 130. http://dx.doi.org/10.1080/00401706.1988.10488351.

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37

Zhao, Meng, and K. B. Kulasekera. "Consistent linear model selection." Statistics & Probability Letters 76, no. 5 (March 2006): 520–30. http://dx.doi.org/10.1016/j.spl.2005.08.020.

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38

Noldus, E. "Non-linear model following." Automatica 23, no. 3 (May 1987): 387–91. http://dx.doi.org/10.1016/0005-1098(87)90012-4.

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39

Sahoo, Shaon, and Soumya Kanti Ganguly. "Optimal Linear Glauber Model." Journal of Statistical Physics 159, no. 2 (January 24, 2015): 336–57. http://dx.doi.org/10.1007/s10955-015-1188-y.

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40

Jungnickel, D. U., and C. Wetterich. "Effective linear meson model." European Physical Journal C 1, no. 3 (February 1998): 669–710. http://dx.doi.org/10.1007/s100520050115.

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41

HARRISON, JEFF, and MIKE WEST. "Dynamic linear model diagnostics." Biometrika 78, no. 4 (1991): 797–808. http://dx.doi.org/10.1093/biomet/78.4.797.

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42

Wang, Xun, Shoumei Li, and Thierry Denœux. "Interval-Valued Linear Model." International Journal of Computational Intelligence Systems 8, no. 1 (September 23, 2014): 114–27. http://dx.doi.org/10.1080/18756891.2014.967010.

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43

O'Donoghue, J. A. "The linear-quadratic model." British Journal of Radiology 61, no. 728 (August 1988): 700. http://dx.doi.org/10.1259/0007-1285-61-728-700-a.

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44

Wheldon, T. E., and A. E. Amin. "The linear-quadratic model." British Journal of Radiology 61, no. 728 (August 1988): 700–702. http://dx.doi.org/10.1259/0007-1285-61-728-700-b.

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45

Révész, L. "The linear-quadratic model." British Journal of Radiology 61, no. 728 (August 1988): 702–3. http://dx.doi.org/10.1259/0007-1285-61-728-702.

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46

Yaes, R. J. "The linear-quadratic model." British Journal of Radiology 61, no. 728 (August 1988): 703–4. http://dx.doi.org/10.1259/0007-1285-61-728-703.

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47

Fowler, J. F. "The linear-quadratic model." British Journal of Radiology 61, no. 728 (August 1988): 704–5. http://dx.doi.org/10.1259/0007-1285-61-728-704.

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48

Withers, H. R. "The linear-quadratic model." British Journal of Radiology 61, no. 728 (August 1988): 705–7. http://dx.doi.org/10.1259/0007-1285-61-728-705.

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49

Svoronos, S. A. "Linear model-dependent control." AIChE Journal 33, no. 3 (April 1987): 394–400. http://dx.doi.org/10.1002/aic.690330305.

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50

Yan, Guohua, William J. Welch, and Ruben H. Zamar. "Model-based linear clustering." Canadian Journal of Statistics 38, no. 4 (November 3, 2010): 716–37. http://dx.doi.org/10.1002/cjs.10082.

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