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Journal articles on the topic 'Mixed linear models'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Varga, Štefan. "Quadratic estimations in mixed linear models." Applications of Mathematics 36, no. 2 (1991): 134–44. http://dx.doi.org/10.21136/am.1991.104450.

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2

Landwehr, Jan R., Andreas Herrmann, and Mark Heitmann. "Linear Mixed Models." Marketing ZFP 30, no. 3 (2008): 175–90. http://dx.doi.org/10.15358/0344-1369-2008-3-175.

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3

Volaufová, Júlia, and Viktor Witkovský. "Estimation of variance components in mixed linear models." Applications of Mathematics 37, no. 2 (1992): 139–48. http://dx.doi.org/10.21136/am.1992.104497.

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4

Varga, Štefan. "Estimations of covariance components in mixed linear models." Mathematica Bohemica 121, no. 1 (1996): 29–33. http://dx.doi.org/10.21136/mb.1996.125947.

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5

Verbeke, Geert, Bart Spiessens, and Emmanuel Lesaffre. "Conditional Linear Mixed Models." American Statistician 55, no. 1 (2001): 25–34. http://dx.doi.org/10.1198/000313001300339905.

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6

Geraci, Marco, and Matteo Bottai. "Linear quantile mixed models." Statistics and Computing 24, no. 3 (2013): 461–79. http://dx.doi.org/10.1007/s11222-013-9381-9.

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7

Morrell, Christopher H., Jay D. Pearson, and Larry J. Brant. "Linear Transformations of Linear Mixed-Effects Models." American Statistician 51, no. 4 (1997): 338. http://dx.doi.org/10.2307/2685902.

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8

Morrell, Christopher H., Jay D. Pearson, and Larry J. Brant. "Linear Transformations of Linear Mixed-Effects Models." American Statistician 51, no. 4 (1997): 338–43. http://dx.doi.org/10.1080/00031305.1997.10474409.

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9

Stępniak, Czesław. "Admissible linear estimators in mixed linear models." Journal of Multivariate Analysis 31, no. 1 (1989): 90–106. http://dx.doi.org/10.1016/0047-259x(89)90052-3.

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10

Arellano-Valle, R. B., H. Bolfarine, and V. H. Lachos. "Skew-normal Linear Mixed Models." Journal of Data Science 3, no. 4 (2021): 415–38. http://dx.doi.org/10.6339/jds.2005.03(4).238.

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11

Welham, Sue, Brian Cullis, Beverley Gogel, Arthur Gilmour, and Robin Thompson. "Prediction in linear mixed models." Australian New Zealand Journal of Statistics 46, no. 3 (2004): 325–47. http://dx.doi.org/10.1111/j.1467-842x.2004.00334.x.

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12

Von Tress, Mark. "Generalized, Linear, and Mixed Models." Technometrics 45, no. 1 (2003): 99. http://dx.doi.org/10.1198/tech.2003.s13.

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13

Cowles, Mary Kathryn. "Generalized, Linear, and Mixed Models." Journal of the American Statistical Association 101, no. 476 (2006): 1724. http://dx.doi.org/10.1198/jasa.2006.s145.

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14

Mitchell, Lada. "Generalized, Linear, and Mixed Models." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 2 (2003): 242–43. http://dx.doi.org/10.1111/1467-9884.00356.

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15

Riquelme, Marco, Heleno Bolfarine, and Manuel Galea. "Robust linear functional mixed models." Journal of Multivariate Analysis 134 (February 2015): 82–98. http://dx.doi.org/10.1016/j.jmva.2014.10.008.

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16

Burnett, Richard T., W. H. Ross, and Daniel Krewski. "Non-linear mixed regression models." Environmetrics 6, no. 1 (1995): 85–99. http://dx.doi.org/10.1002/env.3170060108.

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17

Thiébaut, Rodolphe, Hélène Jacqmin-Gadda, Geneviève Chêne, Catherine Leport, and Daniel Commenges. "Bivariate linear mixed models using SAS proc MIXED." Computer Methods and Programs in Biomedicine 69, no. 3 (2002): 249–56. http://dx.doi.org/10.1016/s0169-2607(02)00017-2.

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18

Arendacká, Barbora, Angelika Täubner, Sascha Eichstädt, Thomas Bruns, and Clemens Elster. "Linear Mixed Models: Gum and Beyond." Measurement Science Review 14, no. 2 (2014): 52–61. http://dx.doi.org/10.2478/msr-2014-0009.

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Abstract In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.

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19

Sheu, Ching-Fan, and Sawako Suzuki. "Meta-analysis using linear mixed models." Behavior Research Methods, Instruments, & Computers 33, no. 2 (2001): 102–7. http://dx.doi.org/10.3758/bf03195354.

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20

Lesaffre, Emmanuel, and Geert Verbeke. "Local Influence in Linear Mixed Models." Biometrics 54, no. 2 (1998): 570. http://dx.doi.org/10.2307/3109764.

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21

Kubokawa, Tatsuya, and Muni S. Srivastava. "Prediction in Multivariate Mixed Linear Models." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 33, no. 2 (2003): 245–70. http://dx.doi.org/10.14490/jjss.33.245.

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22

Avilés, Ana Ivelisse. "Linear Mixed Models for Longitudinal Data." Technometrics 43, no. 3 (2001): 375. http://dx.doi.org/10.1198/tech.2001.s630.

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23

Wu, Ping, Yun Fang, and Li-Xing Zhu. "Estimating Moments in Linear Mixed Models." Communications in Statistics - Theory and Methods 37, no. 16 (2008): 2582–94. http://dx.doi.org/10.1080/03610920801947644.

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24

Ye, Kenny Q. "Statistical Tests for Mixed Linear Models." Technometrics 42, no. 2 (2000): 214. http://dx.doi.org/10.1080/00401706.2000.10486014.

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25

Bani-Mustafa, Ahmed, K. M. Matawie, C. F. Finch, Amjad Al-Nasser, and Enrico Ciavolino. "Recursive residuals for linear mixed models." Quality & Quantity 53, no. 3 (2018): 1263–74. http://dx.doi.org/10.1007/s11135-018-0814-6.

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26

Seifert, Burkhardt. "Testing hypotheses in mixed linear models." Journal of Statistical Planning and Inference 36, no. 2-3 (1993): 253–68. http://dx.doi.org/10.1016/0378-3758(93)90128-s.

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27

Grilli, Leonardo, and Carla Rampichini. "Selection bias in linear mixed models." METRON 68, no. 3 (2010): 309–29. http://dx.doi.org/10.1007/bf03263542.

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28

Wang, Wei. "Identifiability of linear mixed effects models." Electronic Journal of Statistics 7 (2013): 244–63. http://dx.doi.org/10.1214/13-ejs770.

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29

Clayton, David. "Generalized Linear Mixed Models in Biostatistics." Statistician 41, no. 3 (1992): 327. http://dx.doi.org/10.2307/2348554.

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30

Mukhopadhyay, Saurabh, and Alan E. Gelfand. "Dirichlet Process Mixed Generalized Linear Models." Journal of the American Statistical Association 92, no. 438 (1997): 633–39. http://dx.doi.org/10.1080/01621459.1997.10474014.

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31

Nobre, Juvêncio S., and Julio M. Singer. "Leverage analysis for linear mixed models." Journal of Applied Statistics 38, no. 5 (2011): 1063–72. http://dx.doi.org/10.1080/02664761003759016.

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32

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

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33

Kahrari, F., C. S. Ferreira, and R. B. Arellano-Valle. "Skew-Normal-Cauchy Linear Mixed Models." Sankhya B 81, no. 2 (2018): 185–202. http://dx.doi.org/10.1007/s13571-018-0173-2.

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34

Gokalp Yavuz, Fulya, and Barret Schloerke. "Parallel computing in linear mixed models." Computational Statistics 35, no. 3 (2020): 1273–89. http://dx.doi.org/10.1007/s00180-019-00950-7.

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35

Santos Nobre, Juvêncio, and Julio da Motta Singer. "Residual Analysis for Linear Mixed Models." Biometrical Journal 49, no. 6 (2007): 863–75. http://dx.doi.org/10.1002/bimj.200610341.

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36

Santos Nobre, Juvêncio, and Julio da Motta Singer. "Residual Analysis for Linear Mixed Models." Biometrical Journal 49, no. 6 (2007): 875. http://dx.doi.org/10.1002/bimj.200790008.

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37

Richardson, A. M., and A. H. Welsh. "Covariate Screening in Mixed Linear Models." Journal of Multivariate Analysis 58, no. 1 (1996): 27–54. http://dx.doi.org/10.1006/jmva.1996.0038.

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38

McGilchrist, C. A., and C. W. Aisbett. "Restricted BLUP for Mixed Linear Models." Biometrical Journal 33, no. 2 (1991): 131–41. http://dx.doi.org/10.1002/bimj.4710330202.

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39

Xu, Ronghui. "Proportional hazards mixed models." Advances in Methodology and Statistics 1, no. 1 (2004): 205–12. http://dx.doi.org/10.51936/lmzi2020.

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We describe our recent work on mixed effects models for right-censored data. Vaida and Xu (2000) provided a general framework for handling random effects in proportional hazards (PH) regression, in a way similar to the linear, non-linear and generalized linear mixed effects models that allow random effects of arbitrary covariates. This general framework includes the frailty models as a special case. Maximum likelihood estimates of the regression parameters, the variance components and the baseline hazard, and empirical Bayes estimates of the random effects can be obtained via an MCEM algoritm. Variances of the parameter estimates are approximated using Louis' formula. We show interesting applications of the PH mixed effects model (PHMM) to a US Vietnam Era Twin Registry study on alcohol abuse, with the primary goal of identifying genetic contributions to such events. The twin pairs in the registry consist of monozygotic and dizygotic twins. After model fitting and for interpretation purposes, the proportional hazards formulation is converted to a linear transformation model before the results on genetic contributions are reported. The model also allows examination of gene and covariate interactions, as well as the modelling of multivariate outcomes (comorbidities).

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40

Forster, Jonathan J., Roger C. Gill, and Antony M. Overstall. "Reversible jump methods for generalised linear models and generalised linear mixed models." Statistics and Computing 22, no. 1 (2010): 107–20. http://dx.doi.org/10.1007/s11222-010-9210-3.

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41

Myers, Donald E. "Linear and Generalized Linear Mixed Models and Their Applications." Technometrics 50, no. 1 (2008): 93–94. http://dx.doi.org/10.1198/tech.2008.s536.

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42

Ker, H. W. "Application of Hierarchical Linear Models/Linear Mixed-effects Models in School Effectiveness Research." Universal Journal of Educational Research 2, no. 2 (2014): 173–80. http://dx.doi.org/10.13189/ujer.2014.020209.

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43

Schober, Patrick, and Thomas R. Vetter. "Linear Mixed-Effects Models in Medical Research." Anesthesia & Analgesia 132, no. 6 (2021): 1592–93. http://dx.doi.org/10.1213/ane.0000000000005541.

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44

Zhu, Hongtu, and Sik-Yum Lee. "Local influence for generalized linear mixed models." Canadian Journal of Statistics 31, no. 3 (2003): 293–309. http://dx.doi.org/10.2307/3316088.

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45

Kubokawa, Tatsuya. "Linear Mixed Models and Small Area Estimation." Japanese journal of applied statistics 35, no. 3 (2006): 139–61. http://dx.doi.org/10.5023/jappstat.35.139.

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46

M. Gad, Ahmed, and Rasha B. El Kholy. "Generalized Linear Mixed Models for Longitudinal Data." International Journal of Probability and Statistics 1, no. 3 (2012): 41–47. http://dx.doi.org/10.5923/j.ijps.20120103.03.

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47

Breslow, N. E., and D. G. Clayton. "Approximate Inference in Generalized Linear Mixed Models." Journal of the American Statistical Association 88, no. 421 (1993): 9. http://dx.doi.org/10.2307/2290687.

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48

Bai, Xiuqin, Kun Chen, and Weixin Yao. "Mixture of linear mixed models using multivariatetdistribution." Journal of Statistical Computation and Simulation 86, no. 4 (2015): 771–87. http://dx.doi.org/10.1080/00949655.2015.1036431.

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49

Fox, Jean-Paul, Duco Veen, and Konrad Klotzke. "Generalized Linear Mixed Models for Randomized Responses." Methodology 15, no. 1 (2019): 1–18. http://dx.doi.org/10.1027/1614-2241/a000153.

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Abstract. Response bias (nonresponse and social desirability bias) is one of the main concerns when asking sensitive questions about behavior and attitudes. Self-reports on sensitive issues as in health research (e.g., drug and alcohol abuse), and social and behavioral sciences (e.g., attitudes against refugees, academic cheating) can be expected to be subject to considerable misreporting. To diminish misreporting on self-reports, indirect questioning techniques have been proposed such as the randomized response techniques. The randomized response techniques avoid a direct link between individual’s response and the sensitive question, thereby protecting the individual’s privacy. Next to the development of the innovative data collection methods, methodological advances have been made to enable a multivariate analysis to relate responses to sensitive questions to other variables. It is shown that the developments can be represented by a general response probability model (including all common designs) by extending it to a generalized linear model (GLM) or a generalized linear mixed model (GLMM). The general methodology is based on modifying common link functions to relate a linear predictor to the randomized response. This approach makes it possible to use existing software for GLMs and GLMMs to model randomized response data. The R-package GLMMRR makes the advanced methodology available to applied researchers. The extended models and software will seriously improve the application of the randomized response methodology. Three empirical examples are given to illustrate the methods.

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50

Berger, Martijn P. F., and Frans E. S. Tan. "Robust designs for linear mixed effects models." Journal of the Royal Statistical Society: Series C (Applied Statistics) 53, no. 4 (2004): 569–81. http://dx.doi.org/10.1111/j.1467-9876.2004.05152.x.

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