Journal articles: 'Random effect model'

1

Huang, Hung-Yu, and Wen-Chung Wang. "The Random-Effect DINA Model." Journal of Educational Measurement 51, no. 1 (March 2014): 75–97. http://dx.doi.org/10.1111/jedm.12035.

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Šiaulys, Jonas, and Rokas Puišys. "Survival with Random Effect." Mathematics 10, no. 7 (March 29, 2022): 1097. http://dx.doi.org/10.3390/math10071097.

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The article focuses on mortality models with a random effect applied in order to evaluate human mortality more precisely. Such models are called frailty or Cox models. The main assertion of the paper shows that each positive random effect transforms the initial hazard rate (or density function) to a new absolutely continuous survival function. In particular, well-known Weibull and Gompertz hazard rates and corresponding survival functions are analyzed with different random effects. These specific models are presented with detailed calculations of hazard rates and corresponding survival functions. Six specific models with a random effect are applied to the same data set. The results indicate that the accuracy of the model depends on the data under consideration.

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Kalhori, Lida, and Mohsen Mohhamadzadeh. "Spatial Beta Regression Model with Random Effect." Journal of Statistical Research of Iran 13, no. 2 (March 1, 2017): 215–30. http://dx.doi.org/10.18869/acadpub.jsri.13.2.215.

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Wang, Wen-Chung, and Shiu-Lien Wu. "The Random-Effect Generalized Rating Scale Model." Journal of Educational Measurement 48, no. 4 (December 2011): 441–56. http://dx.doi.org/10.1111/j.1745-3984.2011.00154.x.

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Kayid, M., S. Izadkhah, and D. ALmufarrej. "Random Effect Additive Mean Residual Life Model." IEEE Transactions on Reliability 65, no. 2 (June 2016): 860–66. http://dx.doi.org/10.1109/tr.2015.2491600.

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Motarjem, K., M. Mohammadzadeh, and A. Abyar. "Geostatistical survival model with Gaussian random effect." Statistical Papers 61, no. 1 (June 20, 2017): 85–107. http://dx.doi.org/10.1007/s00362-017-0922-8.

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Spineli, Loukia M., and Nikolaos Pandis. "Fixed-effect versus random-effects model in meta-regression analysis." American Journal of Orthodontics and Dentofacial Orthopedics 158, no. 5 (November 2020): 770–72. http://dx.doi.org/10.1016/j.ajodo.2020.07.016.

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Wen, Limin, Jing Fang, Guoping Mei, and Xianyi Wu. "Optimal credibility estimation of random parameters in hierarchical random effect linear model." Journal of Systems Science and Complexity 28, no. 5 (July 30, 2015): 1058–69. http://dx.doi.org/10.1007/s11424-015-3202-5.

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Hernández, Freddy, and Viviana Giampaoli. "The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model." Stats 1, no. 1 (May 31, 2018): 48–76. http://dx.doi.org/10.3390/stats1010005.

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Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

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ADACHI, Kohei. "A Random Effect Model in Metric Multidimensional Unfolding." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 27, no. 1 (2000): 12–23. http://dx.doi.org/10.2333/jbhmk.27.12.

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Anderson, Jon E., and David M. Hoppe. "Malformed frogs: Bayesian and random-effect model analyses." International Journal of Data Analysis Techniques and Strategies 2, no. 2 (2010): 103. http://dx.doi.org/10.1504/ijdats.2010.032452.

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Zadkarami, Mohammad Reza. "Risk Factors for Perinatal Mortality: Random Effect Model." Asian Journal of Epidemiology 1, no. 2 (June 15, 2008): 53–63. http://dx.doi.org/10.3923/aje.2008.53.63.

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Zadkarami, Mohammad Reza. "Risk Factors for Perinatal Mortality: Random Effect Model*." Asian Journal of Epidemiology 3, no. 3 (August 15, 2010): 154–64. http://dx.doi.org/10.3923/aje.2010.154.164.

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Gupta, Ramesh C., and Rameshwar D. Gupta. "Random effect bivariate survival models and stochastic comparisons." Journal of Applied Probability 47, no. 02 (June 2010): 426–40. http://dx.doi.org/10.1017/s0021900200006732.

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In this paper we propose a general bivariate random effect model with special emphasis on frailty models and environmental effect models, and present some stochastic comparisons. The relationship between the conditional and the unconditional hazard gradients are derived and some examples are provided. We investigate how the well-known stochastic orderings between the distributions of two frailties translate into the orderings between the corresponding survival functions. These results are used to obtain the properties of the bivariate multiplicative model and the shared frailty model.

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Gupta, Ramesh C., and Rameshwar D. Gupta. "Random effect bivariate survival models and stochastic comparisons." Journal of Applied Probability 47, no. 2 (June 2010): 426–40. http://dx.doi.org/10.1239/jap/1276784901.

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In this paper we propose a general bivariate random effect model with special emphasis on frailty models and environmental effect models, and present some stochastic comparisons. The relationship between the conditional and the unconditional hazard gradients are derived and some examples are provided. We investigate how the well-known stochastic orderings between the distributions of two frailties translate into the orderings between the corresponding survival functions. These results are used to obtain the properties of the bivariate multiplicative model and the shared frailty model.

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Li, Yao Xiang, and Li Chun Jiang. "Fitting Growth Model Using Nonlinear Regression with Random Parameters." Key Engineering Materials 480-481 (June 2011): 1308–12. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.1308.

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Mixed Effect models are flexible models to analyze grouped data including longitudinal data, repeated measures data, and multivariate multilevel data. One of the most common applications is nonlinear growth data. The Chapman-Richards model was fitted using nonlinear mixed-effects modeling approach. Nonlinear mixed-effects models involve both fixed effects and random effects. The process of model building for nonlinear mixed-effects models is to determine which parameters should be random effects and which should be purely fixed effects, as well as procedures for determining random effects variance-covariance matrices (e.g. diagonal matrices) to reduce the number of the parameters in the model. Information criterion statistics (AIC, BIC and Likelihood ratio test) are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software.

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Gronau, Quentin F., Daniel W. Heck, Sophie W. Berkhout, Julia M. Haaf, and Eric-Jan Wagenmakers. "A Primer on Bayesian Model-Averaged Meta-Analysis." Advances in Methods and Practices in Psychological Science 4, no. 3 (July 2021): 251524592110312. http://dx.doi.org/10.1177/25152459211031256.

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Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta-analysis models: (a) fixed-effect null hypothesis, (b) fixed-effect alternative hypothesis, (c) random-effects null hypothesis, and (d) random-effects alternative hypothesis. These models are combined according to their plausibilities given the observed data to address the two key questions “Is the overall effect nonzero?” and “Is there between-study variability in effect size?” Bayesian model-averaged meta-analysis therefore avoids the need to select either a fixed-effect or random-effects model and instead takes into account model uncertainty in a principled manner.

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Chetvertakova, Evgeniia S., and Ekaterina V. Chimitova. "Testing significance of random effects for the Wiener degradation model." Analysis and data processing systems, no. 3 (September 30, 2021): 129–42. http://dx.doi.org/10.17212/2782-2001-2021-3-129-142.

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This paper considers the Wiener degradation model with random effects. Random-effect models take into account the unit-to-unit variability of the degradation index. It is assumed that a random parameter has a truncated normal distribution. During the research, the expression for the maximum likelihood estimates and the reliability function has been obtained. Two statistical tests have been proposed to reveal the existence of random effects in degradation data corresponding to the Wiener degradation model. The first test is a well-known likelihood ratio test, and the second one is based on the variance estimate of the random parameter. These tests have been compared in terms of power with the Monte-Carlo simulation method. The result of the research has shown that the criterion based on the variance estimate of the random parameter is more powerful than the likelihood ratio test in the case of the considered pairs of competing hypotheses. An example of the analysis using the proposed tests for the turbofan engine degradation data has been considered. The data set includes the measurements recorded from 18 sensors for 100 engines. Before constructing the degradation model, the single degradation index has been obtained using the principal component method. The hypothesis of the random effect insignificance in the model has been rejected for both tests. It has been shown that the random-effect Wiener degradation model describes the failure time distribution more accurately than the fixed-effect Wiener degradation model.

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Lee, Youngjo, and Hee-Seok Oh. "A new sparse variable selection via random-effect model." Journal of Multivariate Analysis 125 (March 2014): 89–99. http://dx.doi.org/10.1016/j.jmva.2013.11.016.

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Lee, Jang Woo, and Woo Young Choi. "Random Telegraph Noise Model of Tunnel Field-Effect Transistors." Journal of Nanoscience and Nanotechnology 16, no. 10 (October 1, 2016): 10264–67. http://dx.doi.org/10.1166/jnn.2016.13140.

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Ono, Yoshiyuki, and Seiichi Fukuda. "Study of Quantum Hall Effect by Random Matrix Model." Journal of the Physical Society of Japan 61, no. 5 (May 15, 1992): 1676–84. http://dx.doi.org/10.1143/jpsj.61.1676.

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Bai, Yongxin, Manling Qian, and Maozai Tian. "Joint mean–covariance random effect model for longitudinal data." Biometrical Journal 62, no. 1 (September 23, 2019): 7–23. http://dx.doi.org/10.1002/bimj.201800311.

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Cao, Ricardo, Mario Francisco-Fernandez, and Emiliano J. Quinto. "A random effect multiplicative heteroscedastic model for bacterial growth." BMC Bioinformatics 11, no. 1 (2010): 77. http://dx.doi.org/10.1186/1471-2105-11-77.

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Güven, Bilgehan. "Testing for random effect in the Fuller–Battese model." Statistics 48, no. 4 (April 14, 2014): 802–14. http://dx.doi.org/10.1080/02331888.2014.903949.

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Houston, Alexander J. H., Martin Gradhand, and Mark R. Dennis. "A random wave model for the Aharonov–Bohm effect." Journal of Physics A: Mathematical and Theoretical 50, no. 20 (April 26, 2017): 205101. http://dx.doi.org/10.1088/1751-8121/aa660f.

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Herrador, M., M. D. Esteban, T. Hobza, and D. Morales. "A Fay–Herriot Model with Different Random Effect Variances." Communications in Statistics - Theory and Methods 40, no. 5 (February 8, 2011): 785–97. http://dx.doi.org/10.1080/03610920903480858.

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Dion, C., and V. Genon-Catalot. "Bidimensional random effect estimation in mixed stochastic differential model." Statistical Inference for Stochastic Processes 19, no. 2 (June 26, 2015): 131–58. http://dx.doi.org/10.1007/s11203-015-9122-0.

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Fabio, Lizandra C., Gilberto A. Paula, and Mário de Castro. "A Poisson mixed model with nonnormal random effect distribution." Computational Statistics & Data Analysis 56, no. 6 (June 2012): 1499–510. http://dx.doi.org/10.1016/j.csda.2011.12.002.

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Lee, Sangin, Yudi Pawitan, and Youngjo Lee. "A random-effect model approach for group variable selection." Computational Statistics & Data Analysis 89 (September 2015): 147–57. http://dx.doi.org/10.1016/j.csda.2015.02.020.

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Friedman, Lynn. "Estimators of Random Effects Variance Components in Meta-Analysis." Journal of Educational and Behavioral Statistics 25, no. 1 (March 2000): 1–12. http://dx.doi.org/10.3102/10769986025001001.

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In meta-analyses, groups of study effect sizes often do not fit the model of a single population with only sampling, or estimation, variance differentiating the estimates. If the effect sizes in a group of studies are not homogeneous, a random effects model should be calculated, and a variance component for the random effect estimated. This estimate can be made in several ways, but two closed form estimators are in common use. The comparative efficiency of the two is the focus of this report. We show here that these estimators vary in relative efficiency with the actual size of the random effects model variance component. The latter depends on the study effect sizes. The closed form estimators are linear functions of quadratic forms whose moments can be calculated according to a well-known theorem in linear models. We use this theorem to derive the variances of the estimators, and show that one of them is smaller when the random effects model variance is near zero; however, the variance of the other is smaller when the model variance is larger. This leads to conclusions about their relative efficiency.

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Drager, Katie, and Jennifer Hay. "Exploiting random intercepts: Two case studies in sociophonetics." Language Variation and Change 24, no. 1 (March 2012): 59–78. http://dx.doi.org/10.1017/s0954394512000014.

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AbstractAn increasing number of sociolinguists are using mixed effects models, models which allow for the inclusion of both fixed and random predicting variables. In most analyses, random effect intercepts are treated as a by-product of the model; they are viewed simply as a way to fit a more accurate model. This paper presents additional uses for random effect intercepts within the context of two case studies. Specifically, this paper demonstrates how random intercepts can be exploited to assist studies of speaker style and identity and to normalize for vocal tract size within certain linguistic environments. We argue that, in addition to adopting mixed effect modeling more generally, sociolinguists should view random intercepts as a potential tool during analysis.

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Qu Shao-Hua and Cao Wan-Qiang. "Research on polarization effect for relaxor ferroelectrics by spherical random bond-random field model." Acta Physica Sinica 63, no. 4 (2014): 047701. http://dx.doi.org/10.7498/aps.63.047701.

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Shchur, L. N., J. R. Heringa, and H. W. J. Blöte. "Simulation of a directed random-walk model the effect of pseudo-random-number correlations." Physica A: Statistical Mechanics and its Applications 241, no. 3-4 (July 1997): 579–92. http://dx.doi.org/10.1016/s0378-4371(97)00126-x.

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D, Catherine Rexy, Mokesh Rayalu G, and Ponnuraja C. "Unshared gamma frailty model in Tuberculosis patients." International Journal of Research in Pharmaceutical Sciences 9, no. 1 (March 12, 2018): 103. http://dx.doi.org/10.26452/ijrps.v9i1.1198.

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A natural of survival analysis associated the modelling of time-to-failure, consider the time until death or failure. The frailty model could be a random effect unobserved information, wherever the random effect an increasing, impact of baseline hazard function. The frailty model provides a convenient way to introduce random effects, accounts for further variability from unobserved factors, and heterogeneity into models for survival information. This text planned to analyze the frailty gamma distribution has been active to the parameter distribution acting as Weibull, log logistic and log normal distributions with a naturally incidental variable so as to diagnose the prognostic cause that effect the infectious disease patients for survival time. Information analysis is performed victimization STATA software package. Keyword: Frailty model; Parametric Distribution; Gamma frailty model -2LL; Tuberculosis Patients

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Komprej, A., Š. Malovrh, G. Gorjanc, D. Kon, and M. Kovač. "Genetic and environmental parameters estimation for milk traits in Slovenian dairy sheep using random regression model." Czech Journal of Animal Science 58, No. 3 (March 4, 2013): 125–35. http://dx.doi.org/10.17221/6669-cjas.

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(Co)variance components for daily milk yield, fat, and protein content in Slovenian dairy sheep were estimated with random regression model. Test-day records were collected by the ICAR A4 method. Analysis was done for 38 983 test-day records of 3068 ewes in 36 flocks. Common flock environment, additive genetic effect, permanent environment effect over lactations, and permanent environment effect within lactation were included into the random part of the model and modelled with Legendre polynomials on the standardized time scale of days in lactation. Estimation of (co)variance components was done with REML. The eigenvalues of covariance functions for random regression coefficients were calculated to quantify the sufficient order of Legendre polynomial for the (co)variance component estimation of milk traits. The existing 13 to 24% of additive genetic variability for the individual lactation curve indicated that the use of random regression model is justified for selection on the level and shape of lactation curve in dairy sheep. Four eigenvalues sufficiently explained variability during lactation in all three milk traits. Heritability estimate for daily milk yield was the highest in mid lactation (0.17) and lower in the early (0.11) and late (0.08) lactation. In fat content, the heritability was increasing throughout lactation (0.08–0.13). Values in protein content varied from the beginning toward mid lactation (0.15–0.19), while they rapidly increased at the end of lactation (0.28). Common flock environment explained the highest percentage of phenotypic variability: 27–41% in daily milk yield, 31–41% in fat content, and 41–49% in protein content. Variance ratios for the two permanent environment effects were the highest in daily milk yield (0.10–0.27), and lower in fat (0.04–0.08) and protein (0.01–0.10) contents. Additive genetic correlations during the selected test-days were high between the adjacent ones and they tended to decrease at the extremes of the lactation trajectory.

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Goldstein, Harvey, George Leckie, Christopher Charlton, Kate Tilling, and William J. Browne. "Multilevel growth curve models that incorporate a random coefficient model for the level 1 variance function." Statistical Methods in Medical Research 27, no. 11 (May 1, 2017): 3478–91. http://dx.doi.org/10.1177/0962280217706728.

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Aim To present a flexible model for repeated measures longitudinal growth data within individuals that allows trends over time to incorporate individual-specific random effects. These may reflect the timing of growth events and characterise within-individual variability which can be modelled as a function of age. Subjects and methods A Bayesian model is developed that includes random effects for the mean growth function, an individual age-alignment random effect and random effects for the within-individual variance function. This model is applied to data on boys’ heights from the Edinburgh longitudinal growth study and to repeated weight measurements of a sample of pregnant women in the Avon Longitudinal Study of Parents and Children cohort. Results The mean age at which the growth curves for individual boys are aligned is 11.4 years, corresponding to the mean ‘take off’ age for pubertal growth. The within-individual variance (standard deviation) is found to decrease from 0.24 cm2 (0.50 cm) at 9 years for the ‘average’ boy to 0.07 cm2 (0.25 cm) at 16 years. Change in weight during pregnancy can be characterised by regression splines with random effects that include a large woman-specific random effect for the within-individual variation, which is also correlated with overall weight and weight gain. Conclusions The proposed model provides a useful extension to existing approaches, allowing considerable flexibility in describing within- and between-individual differences in growth patterns.

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Park, Minho, Dongmin Lee, and Jinwoo Jeon. "Random Parameter Negative Binomial Model of Signalized Intersections." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1436364.

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Factors affecting accident frequencies at 72 signalized intersections in the Gyeonggi-Do (province) over a four-year period (2007~2010) were explored using the random parameters negative binomial model. The empirical results from the comparison with fixed parameters binomial model show that the random parameters model outperforms its fixed parameters counterpart and provides a fuller understanding of the factors which determine accident frequencies at signalized intersections. In addition, elasticity and marginal effect were estimated to gain more insight into the effects of one-percent and one-unit changes in the dependent variable from changes in the independent variables.

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LeBeau, Brandon, Yoon Ah Song, and Wei Cheng Liu. "Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis." SAGE Open 8, no. 4 (October 2018): 215824401882038. http://dx.doi.org/10.1177/2158244018820380.

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This meta-analysis attempts to synthesize the Monte Carlo (MC) literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new MC studies will be explored.

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Nazarzadeh, Milad, and Zeinab Bidel. "Meta-Analysis of Sleep Duration and Obesity in Children: Fixed Effect Model or Random Effect Model?" Journal of Paediatrics and Child Health 53, no. 9 (September 2017): 923–24. http://dx.doi.org/10.1111/jpc.13667.

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Morris, Darcy Steeg, and Kimberly F. Sellers. "A Flexible Mixed Model for Clustered Count Data." Stats 5, no. 1 (January 7, 2022): 52–69. http://dx.doi.org/10.3390/stats5010004.

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Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however, departures from equi-dispersion may also exist due to the underlying count process mechanism. We study the cross-sectional COM-Poisson regression model—a generalized regression model for count data in light of data dispersion—together with random effects for analysis of clustered count data. We demonstrate model flexibility of the COM-Poisson random intercept model, including choice of the random effect distribution, via simulated and real data examples. We find that COM-Poisson mixed models provide comparable model fit to well-known mixed models for associated special cases of clustered discrete data, and result in improved model fit for data with intermediate levels of over- or underdispersion in the count mechanism. Accordingly, the proposed models are useful for capturing dispersion not consistent with commonly used statistical models, and also serve as a practical diagnostic tool.

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Park, Jungkyu, Ramsey Cardwell, and Hsiu-Ting Yu. "Specifying the random effect structure in linear mixed effect models for analyzing psycholinguistic data." Methodology 16, no. 2 (June 18, 2020): 92–111. http://dx.doi.org/10.5964/meth.2809.

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Linear Mixed Effect Models (LMEM) have become a popular method for analyzing nested experimental data, which are often encountered in psycholinguistics and other fields. This approach allows experimental results to be generalized to the greater population of both subjects and experimental stimuli. In an influential paper Bar and his colleagues (2013; https://doi.org/10.1016/j.jml.2012.11.001) recommend specifying the maximal random effect structure allowed by the experimental design, which includes random intercepts and random slopes for all within-subjects and within-items experimental factors, as well as correlations between the random effects components. The goal of this paper is to formally investigate whether their recommendations can be generalized to wider variety of experimental conditions. The simulation results revealed that complex models (i.e., with more parameters) lead to a dramatic increase in the non-convergence rate. Furthermore, AIC and BIC were found to select the true model in the majority of cases, although selection accuracy varied by LMEM random effect structure.

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Chun Ma, Alfred Ka, and Justina Yuen Ki Cheung. "A Random Utility Model for Shareholders Capturing the Disposition Effect." International Journal of Applied Behavioral Economics 4, no. 2 (April 2015): 1–15. http://dx.doi.org/10.4018/ijabe.2015040101.

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This work proposes a random utility model for individual trading decision in the spirit of prospect theory. This model differs from those in the literature in that empirical data of stock price and volume can be incorporated. The paper tests the model with historical data from the NYSE TAQ database. This model provides one more alternative to link prospect theory and the disposition effect. Simulation results show that this model consistently predicts the disposition effect under all circumstances.

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Sun, Jianguo, and David E. Matthews. "A random-effect regression model for medical follow-up studies." Canadian Journal of Statistics 25, no. 1 (March 1997): 101–11. http://dx.doi.org/10.2307/3315360.n.

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Yen, A. M. F., H. H. Liou, H. L. Lin, and T. H. H. Chen. "Bayesian Random-effect Model for Predicting Outcome Fraught with Heterogeneity." Methods of Information in Medicine 45, no. 06 (2006): 631–37. http://dx.doi.org/10.1055/s-0038-1634127.

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Summary Objective: The study aimed to develop a predictive model to deal with data fraught with heterogeneity that cannot be explained by sampling variation or measured covariates. Methods: The random-effect Poisson regression model was first proposed to deal with over-dispersion for data fraught with heterogeneity after making allowance for measured covariates. Bayesian acyclic graphic model in conjunction with Markov Chain Monte Carlo (MCMC) technique was then applied to estimate the parameters of both relevant covariates and random effect. Predictive distribution was then generated to compare the predicted with the observed for the Bayesian model with and without random effect. Data from repeated measurement of episodes among 44 patients with intractable epilepsy were used as an illustration. Results: The application of Poisson regression without taking heterogeneity into account to epilepsy data yielded a large value of heterogeneity (heterogeneity factor = 17.90, deviance = 1485, degree of freedom (df) = 83). After taking the random effect into account, the value of heterogeneity factor was greatly reduced (heterogeneity factor = 0.52, deviance = 42.5, df = 81). The Pearson χ2 for the comparison between the expected seizure frequencies and the observed ones at two and three months of the model with and without random effect were 34.27 (p = 1.00) and 1799.90 (p <0.0001), respectively. Conclusion: The Bayesian acyclic model using the MCMC method was demonstrated to have great potential for disease prediction while data show over-dispersion attributed either to correlated property or to subject-to-subject variability.

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Park, Jincheol, and Shili Lin. "A random effect model for reconstruction of spatial chromatin structure." Biometrics 73, no. 1 (May 23, 2016): 52–62. http://dx.doi.org/10.1111/biom.12544.

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Bahmad, L., A. Benyoussef, and A. El Kenz. "Random crystal-field effect on the spin- Blume–Capel model." Journal of Magnetism and Magnetic Materials 320, no. 3-4 (February 2008): 397–402. http://dx.doi.org/10.1016/j.jmmm.2007.06.017.

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Murphy, S. A. "Consistency in a Proportional Hazards Model Incorporating a Random Effect." Annals of Statistics 22, no. 2 (June 1994): 712–31. http://dx.doi.org/10.1214/aos/1176325492.

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Kouassi, Eugene, and Kern O. Kymn. "Prediction in the two-way random-effect model with heteroskedasticity." Journal of Forecasting 27, no. 5 (August 2008): 451–63. http://dx.doi.org/10.1002/for.1016.

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Kawasaki, Mitsuhiro. "The Rejuvenation Effect in the Two-State Random Energy Model." Journal of the Physical Society of Japan 70, no. 6 (June 15, 2001): 1762–67. http://dx.doi.org/10.1143/jpsj.70.1762.

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Fei, Huiyang, Kyle Yazzie, Nikhilesh Chawla, and Hanqing Jiang. "The Effect of Random Voids in the Modified Gurson Model." Journal of Electronic Materials 41, no. 2 (December 1, 2011): 177–83. http://dx.doi.org/10.1007/s11664-011-1816-5.

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Journal articles on the topic 'Random effect model'

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