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Relevant bibliographies by topics / Random effects

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Academic literature on the topic 'Random effects'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Random effects"

1

Kang, Dasom, Bo Ok Kim, and Keunbaik Lee. "Marginalized random effects models with ARMA random effects covariance matrix." Journal of the Korean Data And Information Science Sociaty 29, no. 2 (March 31, 2018): 501–12. http://dx.doi.org/10.7465/jkdi.2018.29.2.501.

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Lee, Keunbaik, and Seolhwa Kim. "Modeling of random effects covariance matrix in marginalized random effects models." Journal of the Korean Data and Information Science Society 27, no. 3 (May 31, 2016): 815–25. http://dx.doi.org/10.7465/jkdi.2016.27.3.815.

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Galvao and Poirier. "Quantile Regression Random Effects." Annals of Economics and Statistics, no. 134 (2019): 109. http://dx.doi.org/10.15609/annaeconstat2009.134.0109.

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Stanek, Edward J., and James R. OHearn. "Estimating realized random effects." Communications in Statistics - Theory and Methods 27, no. 5 (January 1998): 1021–48. http://dx.doi.org/10.1080/03610929808832143.

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Serghiou, Stylianos, and Steven N. Goodman. "Random-Effects Meta-analysis." JAMA 321, no. 3 (January 22, 2019): 301. http://dx.doi.org/10.1001/jama.2018.19684.

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Kim, Jiyeong, Insuk Sohn, and Keunbaik Lee. "Bayesian cumulative logit random effects models with ARMA random effects covariance matrix." Journal of the Korean Statistical Society 49, no. 1 (January 1, 2020): 32–54. http://dx.doi.org/10.1007/s42952-019-00003-1.

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Lee, Keunbaik, Hoimin Jung, and Jae Keun Yoo. "Modeling of the ARMA random effects covariance matrix in logistic random effects models." Statistical Methods & Applications 28, no. 2 (September 22, 2018): 281–99. http://dx.doi.org/10.1007/s10260-018-00440-y.

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Lander, David. "Random-Effects Meta-analysis of Inconsistent Effects." Annals of Internal Medicine 161, no. 5 (September 2, 2014): 379. http://dx.doi.org/10.7326/l14-5017-7.

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Keum, NaNa, Chung-Cheng Hsieh, and Nancy Cook. "Random-Effects Meta-analysis of Inconsistent Effects." Annals of Internal Medicine 161, no. 5 (September 2, 2014): 379. http://dx.doi.org/10.7326/l14-5017-8.

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Cornell, John E. "Random-Effects Meta-analysis of Inconsistent Effects." Annals of Internal Medicine 161, no. 5 (September 2, 2014): 380. http://dx.doi.org/10.7326/l14-5017-9.

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Dissertations / Theses on the topic "Random effects"

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Putcha, Venkata Rama Prasad. "Random effects in survival analysis." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312431.

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Lee, Sungwook. "Semiparametric regression with random effects /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842547.

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Hunt, Colleen Helen. "Inference for general random effects models." Title page, table of contents and abstract only, 2003. http://web4.library.adelaide.edu.au/theses/09SM/09smh9394.pdf.

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"October 13, 2003" Bibliography: leaves 102-105. This work describes methods associated with general random effects models. Part one describes a technique for investigating mean-variance relationships in random effects models. Part two derives and approximation to the likelihood function using a Laplace expansion to the fourth order.

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Sanogo, Kakotan. "Tolerance Intervals in Random-Effects Models." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1661.

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In the pharmaceutical setting, it is often necessary to establish the shelf life of a drug product and sometimes suitable to assess the risk of product failure at the desired expiry period. The current statistical methodology use confidence intervals for the predicted mean to establish the expiry period and prediction intervals for a predicted new assay value or a tolerance interval for a proportion of the population for use in a risk assessment. A major concern is that most methodology treat a homogeneous subpopulation, say batch, either as a fixed effect and therefore uses a fixed-effects regression model (Graybill, 1976) or as a mixed-effects model limited to balanced data structures (Jonsson, 2003). However, batch is definitely a random effect as this fact has been reflected by some recent methodology [Altan, Cabrera and Shoung (2005), Hoffman and Kringle (2005)]. Thus, to assess the risk of product failure at expiry, it is necessary to use tolerance intervals since they provide an estimate of the proportion of assay values and/or batches failing at the expiry period. In this thesis, we illustrate the methodology described by Jonsson (2003) to construct β-expectation tolerance limits for longitudinal data in a random-effects setting. We underline the limitations of Jonsson’s approach to constructing tolerance intervals and highlight the need for a better methodology.

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Skoglund, Jimmy. "Essays on random effects models and GARCH." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (Ekonomiska forskningsinstitutet vid Handelshögsk.) (EFI), 2001. http://www.hhs.se/efi.summary/553.htm.

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Higgins, Julian P. T. "Exploiting information in random effects meta-analysis." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387704.

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Kerekes, Andrea. "Random effects on the solar f-mode." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444227.

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Zhu, Chang Qing. "Statistical methods for Weibull based random effects models." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/876/.

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Marques-da-Silva, Antonio Hermes. "Gradient test under non-parametric random effects models." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12645/.

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The gradient test proposed by Terrell (2002) is an alternative to the likelihood ratio, Wald and Rao tests. The gradient statistic is the result of the inner product of two vectors — the gradient of the likelihood under null hypothesis (hence the name) and the result of the difference between the estimate under alternative hypothesis and the estimate under null hypothesis. Therefore the gradient statistic is computationally less expensive than Wald and Rao statistics as it does not require matrix operations in its formula. Under some regularity conditions, the gradient statistic has χ2 distribution under null hypothesis. The generalised linear model (GLM) introduced by Nelder & Wedderburn (1972) is one of the most important classes of statistical models. It incorporates the classical regression modelling and analysis of variance either for continuous response and categorical response variables under the exponential family. The random effects model extends the standard GLM for situations where the model does not describe appropriately the variability in the data (overdispersion) (Aitkin, 1996a). We propose a new unified notation for GLM with random effects and the gradient statistic formula for testing fixed effects parameters on these models. We also develop the Fisher information formulae used to obtain the Rao and Wald statistics. Our main interest in this thesis is to investigate the finite sample performance of the gradient test on generalised linear models with random effects. For this we propose and extensive simulation experiment to study the type I error and the local power of the gradient test using the methodology developed by Peers (1971) and Hayakawa (1975). We also compare the local power of the test with the local power of the tests of the likelihood ratio, of Wald and Rao tests.

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Meddings, D. P. "Statistical inference in mixture models with random effects." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1455733/.

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There is currently no existing asymptotic theory for statistical inference on the maximum likelihood estimators of the parameters in a mixture of linear mixed models (MLMMs). Despite this many researchers assume the estimators are asymptotically normally distributed with covariance matrix given by the inverse of the information matrix. Mixture models create new identifability problems that are not inherited from the underlying linear mixed model (LMM), and this subject has not been investigated for these models. Since identifability is a prerequisite for the existence of a consistent estimator of the model parameters, then this is an important area of research that has been neglected. MLMMs are mixture models with random effects, and they are typically used in medical and genetics settings where random heterogeneity in repeated measures data are observed between measurement units (people, genes), but where it is assumed the units belong to one and only one of a finite number of sub-populations or components. This is expressed probabalistically by using a sub-population specific probability distribution function which are often called the component distribution functions. This thesis is motivated by the belief that the use of MLMMs in applied settings such as these is being held back by the lack of development of the statistical inference framework. Specifically this thesis has the following primary objectives; i To investigate the quality of statistical inference provided by different information matrix based methods of confidence interval construction. ii To investigate the impact of component distribution function separation on the quality of statistical inference, and to propose a new method to quantify this separation. iii To determine sufficient conditions for identifiability of MLMMs.

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Books on the topic "Random effects"

1

Lee, Youngjo. Generalized Linear Models with Random Effects. Second edition. | Boca Raton, Florida : CRC Press, [2017] |: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315119953.

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Linear and nonlinear models: Fixed effects, random effects, and mixed models. Berlin: Walter de Gruyter, 2006.

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Ha, Il Do, Jong-Hyeon Jeong, and Youngjo Lee. Statistical Modelling of Survival Data with Random Effects. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6557-6.

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Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Andrews, Larry C. Laser beam propagation through random media. Bellingham, WA: SPIE Optical Engineering Press, 1998.

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Andrews, Larry C. Laser beam propagation through random media. 2nd ed. Bellingham, WA: SPIE Press, 2006.

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L, Phillips Ronald, ed. Laser beam propagation through random media. 2nd ed. Bellingham, Wash: SPIE Press, 2005.

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Sacerdote, Bruce. Peer effects with random assignment: Results for Dartmouth roommates. Cambridge, MA: National Bureau of Economic Research, 2000.

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1973-, Warzel Simone, ed. Random operators: Disorder effects on quantum spectra and dynamics. Providence, Rhode Island: American Mathematical Society, 2015.

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Arulampalam, Wiji. A note on estimated coefficients in random effects probit models. Coventry: University of Warwick, Department of Economics, 1998.

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Book chapters on the topic "Random effects"

1

Lindman, Harold R. "Random Effects." In Springer Texts in Statistics, 127–58. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9722-9_6.

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Li, Yi. "Random Effects." In Springer Handbook of Engineering Statistics, 687–703. London: Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-288-1_38.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Random Effects." In Machine Learning in Medicine, 81–94. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7869-6_9.

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Hand, David, and Martin Crowder. "Random effects models." In Practical Longitudinal Data Analysis, 65–72. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3033-0_5.

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Fahrmeir, Ludwig, and Gerhard Tutz. "Random Effects Models." In Multivariate Statistical Modelling Based on Generalized Linear Models, 283–329. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3454-6_7.

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Balazsi, Laszlo, Badi H. Baltagi, Laszlo Matyas, and Daria Pus. "Random Effects Models." In Advanced Studies in Theoretical and Applied Econometrics, 35–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60783-2_2.

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Molina, Kristine M., Kristine M. Molina, Heather Honoré Goltz, Marc A. Kowalkouski, Stacey L. Hart, David Latini, J. Rick Turner, et al. "Random-Effects Modeling." In Encyclopedia of Behavioral Medicine, 1618. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_101434.

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Fahrmeir, Ludwig, and Gerhard Tutz. "Random effects models." In Multivariate Statistical Modelling Based on Generalized Linear Models, 219–55. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4899-0010-4_7.

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Balázsi, László, Badi H. Baltagi, László Mátyás, and Daria Pus. "Random Effects Models." In Advanced Studies in Theoretical and Applied Econometrics, 61–98. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-49849-7_3.

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Baltagi, Badi H. "Fixed Effects and Random Effects." In The New Palgrave Dictionary of Economics, 4786–90. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2713.

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Conference papers on the topic "Random effects"

1

Kuzmin, V. L., D. Y. Churmakov, and I. V. Meglinski. "Stochastic Modeling of Coherent Effects in Multiple Scattering." In Photonic Metamaterials: From Random to Periodic. Washington, D.C.: OSA, 2006. http://dx.doi.org/10.1364/meta.2006.thb5.

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Valencia, C. I., and E. R. Méndez. "Weak Localization Effects in Second-Harmonic Light Scattering." In Photonic Metamaterials: From Random to Periodic. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/meta.2007.tub32.

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Chang, Chi Ching, Guang Wei Hu, Ching Yang Lin, and Kendra L. Russell. "Encrypted holographic memory using rotationally random phase encoding." In Photorefractive Effects, Materials, and Devices. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/pemd.2001.188.

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Markov, Vladimir. "High-Density Holographic Memory with Random Encoded Reference Beam." In Photorefractive Effects, Materials, and Devices. Washington, D.C.: OSA, 2003. http://dx.doi.org/10.1364/pemd.2003.671.

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Matoba, Osamu, Xiaodi Tan, Tsutomu Shimura, Kazuo Kuroda, and Bahrain Javidi. "Data Security in Holographic Memory using Double Random Polarization Encryption." In Photorefractive Effects, Materials, and Devices. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/pemd.2001.131.

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Tomita, Satoshi, Takeshi Kato, Shigeru Tsunashima, Satoshi Iwata, Minoru Fujii, and Shinji Hayashi. "Magneto-optical Kerr effects of magnetic garnet thin films including plasmonic noble-metal nanoparticles." In Photonic Metamaterials: From Random to Periodic. Washington, D.C.: OSA, 2006. http://dx.doi.org/10.1364/meta.2006.wd18.

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Smedby, Örjan, Mats Fredrikson, Jakob De Geer, and Michael Sandborg. "Visual grading regression with random effects." In SPIE Medical Imaging, edited by Craig K. Abbey and Claudia R. Mello-Thoms. SPIE, 2012. http://dx.doi.org/10.1117/12.913650.

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Borcea, L., F. Gonzales del Cueto, G. Papanicolaou, and C. Tsogka. "Imaging Random Media Effects for Imaging." In 72nd EAGE Conference and Exhibition incorporating SPE EUROPEC 2010. European Association of Geoscientists & Engineers, 2010. http://dx.doi.org/10.3997/2214-4609.201400840.

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Adell, Philippe C., Slaven Moro, Lionel Gouyet, Christian Chatry, and Bert Vermeire. "Single Event Effect Assessment of a 1-Mbit Commercial Magneto-resistive Random Access Memory (MRAM)." In 2017 IEEE Nuclear & Space Radiation Effects Conference (NSREC): Radiation Effects Data Workshop (REDW). IEEE, 2017. http://dx.doi.org/10.1109/nsrec.2017.8115456.

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Dahl, B. A., J. Cruz-Colon, R. C. Baumann, J. A. Rodriguez, C. Zhou, J. Rodriguez-Latorre, S. Khan, T. San, and T. Trinh. "Radiation Evaluation of Ferroelectric Random Access Memory Embedded in 180nm CMOS Technology." In 2015 IEEE Radiation Effects Data Workshop (REDW). IEEE, 2015. http://dx.doi.org/10.1109/redw.2015.7336729.

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Reports on the topic "Random effects"

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Parzen, G. Linear Random Quadrupole Effects and Their Correction. Office of Scientific and Technical Information (OSTI), November 1988. http://dx.doi.org/10.2172/1119089.

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Jordà, Òscar, Moritz Schularick, and Alan Taylor. The effects of quasi-random monetary experiments. Cambridge, MA: National Bureau of Economic Research, January 2017. http://dx.doi.org/10.3386/w23074.

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Parzen, George. Effects of Random Quadrupole Field Errors and Their Correction. Office of Scientific and Technical Information (OSTI), September 1987. http://dx.doi.org/10.2172/1119070.

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Sigeti, David Edward, and Scott Alan Vander Wiel. Doubly-Hierarchical One-Way Random Effects Model: Multivariate Data. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1329823.

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Sacerdote, Bruce. Peer Effects with Random Assignment: Results for Dartmouth Roommates. Cambridge, MA: National Bureau of Economic Research, January 2000. http://dx.doi.org/10.3386/w7469.

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Zhang, Zhen. Multilevel SEM with Random Slopes and Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/5a63kcb9bqqqq469.

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This seminar introduces the Mplus multilevel latent variable modeling framework and describes topics including multilevel variance and effect decomposition, and random and fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM, including approaches for handling strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.

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Popovic, Milorad, Trevor Butler, and Doug Moehs. Effects on the Linac beam from random noise in quads. Office of Scientific and Technical Information (OSTI), May 2005. http://dx.doi.org/10.2172/15020256.

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Giacomini, Raffaella, Sokbae Lee, and Silvia Sarpietro. A Robust Method for Microforecasting and Estimation of Random Effects. Federal Reserve Bank of Chicago, 2023. http://dx.doi.org/10.21033/wp-2023-26.

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Milutinovic, J., and A. G. Ruggiero. Analysis of effects of closed orbit errors, quadrupole: Random errors and random quadrupole rotation errors for the SSC LEB. Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/1118924.

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Parzen, George. Effects of Random Quadrupole Field Errors in RHIC and Their Correction. Office of Scientific and Technical Information (OSTI), February 1986. http://dx.doi.org/10.2172/1119045.

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