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Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.


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TL;DR: In this article, the authors proposed maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components, where the heterogeneity in the panel is incorporated via an error component specification.
Abstract: This paper proposes maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange Multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrates these methods. The proposed specification uses a system of three SUR equations corresponding to three types of flats within 80 districts of Paris over the period 1990-2003. We test for spatial effects and heterogeneity and find reasonable estimates of the shadow prices for housing characteristics.

75 citations

Journal ArticleDOI
31 Dec 2013-PLOS ONE
TL;DR: The present meta-analysis provides the statistical evidence that the association between prostatitis and prostate cancer is significant, under both fixed and random effects model.
Abstract: ObjectiveUse systematic review methods to quantify the association between prostatitis and prostate cancer, under both fixed and random effects model. Evidence AcquisitionCase control studies of prostate cancer with information on prostatitis history. All studies published between 1990-2012, were collected to calculate a pooled odds ratio. Selection criteria: the selection criteria are as follows: human case control studies; published from May 1990 to July 2012; containing number of prostatitis, and prostate cancer cases. Evidence SynthesisIn total, 20 case control studies were included. A significant association between prostatitis and prostate cancer was found, under both fixed effect model (pooled OR=1.50, 95%CI: 1.39-1.62), and random effects model (OR=1.64, 95%CI: 1.36-1.98). Personal interview based case control studies showed a high level of association (fixed effect model: pooled OR=1.59, 95%CI: 1.47-1.73, random effects model: pooled OR= 1.87, 95%CI: 1.52-2.29), compared with clinical based studies (fixed effect model: pooled OR=1.05, 95%CI: 0.86-1.28, random effects model: pooled OR= 0.98, 95%CI: 0.67-1.45). Additionally, pooled ORs, were calculated for each decade. In a fixed effect model: 1990’s: OR=1.58, 95% CI: 1.35-1.84; 2000’s: OR=1.59, 95% CI: 1.40-1.79; 2010’s: OR=1.37, 95% CI: 1.22-1.56. In a random effects model: 1990’s: OR=1.98, 95% CI: 1.08-3.62; 2000’s: OR=1.64, 95% CI: 1.23-2.19; 2010’s: OR=1.34, 95% CI: 1.03-1.73. Finally a meta-analysis stratified by each country was conducted. In fixed effect models, U.S: pooled OR =1.45, 95%CI: 1.34-1.57; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90. In random effects model, U.S: pooled OR=1.50, 95%CI: 1.25-1.80; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90.CONCLUSIONS: the present meta-analysis provides the statistical evidence that the association between prostatitis and prostate cancer is significant.

75 citations

Journal ArticleDOI
TL;DR: A random-effects model appears to be most suitable for the analysis ofSelf-reported disability in older women when the influence of time and age is analysed and when individual risk factors are studied in an aetiological perspective.
Abstract: Longitudinal studies with binary repeated outcomes are now widespread in epidemiology The statistical analysis of these studies presents difficulties and standard methods are inadequate We consider strategies for modelling binary repeated responses and focus on two specific issues: the choice between marginal and random-effects models, and the choice of the time point origin These issues are addressed using the example of self-reported disability in older women assessed annually for 6 years The indicator of disability "needing help to go outdoors or home-confined" is used In view of the observed associations between the responses for consecutive years, the baseline response was considered as a covariate We compared the marginal and random-effects models first when only the influence of time and age is analysed and second when individual risk factors are studied in an aetiological perspective There were substantial differences between the parameter estimates They were due to differences between specific concepts related to the two models and the large between-individual heterogeneity revealed by the analysis A random-effects model appears to be most suitable for the analysis of self-reported disability in older women

75 citations

Journal ArticleDOI
TL;DR: In this paper, a random effects model that takes into consideration the correlation of data recorded by a single seismic event is employed to develop empirical attenuation relationships for the geometric mean of horizontal peak ground acceleration and 5-percent damped spectral acceleration.

75 citations

Journal ArticleDOI
TL;DR: In this article, a total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed.

75 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023198
2022433
2021409
2020380
2019404