Robust Estimation of Variance Components with High Breakdown Point in the 1-Way Random Effect Model

1997 ◽  
pp. 65-73 ◽  
Author(s):  
Steffen Uhlig
Keyword(s):  
Robust Estimation ◽  
Variance Components ◽  
Random Effect ◽  
Random Effect Model ◽  
Breakdown Point ◽  
High Breakdown Point ◽  
Effect Model ◽  
Estimation Of Variance

PSI-17 Genetic analysis of weaning weight in Hungarian Simmental cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 274-275
Author(s):  
Afees Ajasa ◽  
Barnabás Vágó ◽  
Imre Füller ◽  
István Komlósi ◽  
János Posta
Keyword(s):  
Variance Components ◽  
Random Effect ◽  
Random Effect Model ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Covariance Model ◽  
Genetic Covariance ◽  
Weaning Weight ◽  
Effect Model ◽  
Maternal Heritability

Abstract The aim of the study was to partition the total phenotypic variation in the weaning weight of Hungarian Simmental calves into their various causal components. The data used was provided by the Association of Hungarian Simmental Breeders. The dataset comprised of the weaning weight records of 44,278 calves (sire = 879, dam = 14,811) born from 1975 to 2020. A total of six models were fitted to the weaning weight data. Herd, birth year, calving order and sex were included as fixed effects in the models. Model 1 had direct genetic effect as the only random effect. Model 2 had a permanent maternal environment as an additional random effect. Model 3 had both direct and maternal genetic effects, with their covariance is being zero. Model 4 was similar to Model 3 but with non-zero direct-maternal genetic covariance. Model 5 had direct, maternal genetic and permanent environmental effects and a zero direct-maternal genetic covariance. Model 6 was similar to model 5 but the direct-maternal genetic effect was assumed to be correlated. Variance components and genetic parameters were estimated using restricted maximum likelihood method with the Wombat software. The best fit model was determined using the Log likelihood ratio test. Inclusion of direct maternal genetic covariance increased the variance components estimates dramatically which resulted in a corresponding increase in the direct and maternal heritability estimates. The best fitted model (Model 4) had direct and maternal genetic effects as the only random effects with a non-zero direct-maternal genetic covariance. The direct heritability, maternal heritability and direct-maternal genetic correlation estimate of the best model was 0.57, 0.16 and -0.78, respectively. Our result suggests the problem of (co)sampling variation in the partitioning of additive genetic effect into direct and maternal components.

Estimation of Variance Component for one-way Classification with Heteroscedastic Error

1983 ◽  
Vol 32 (1-2) ◽  
pp. 57-78
Author(s):  
Shoutir Kishore Chatterjee ◽  
Kalyan Das
Keyword(s):  
Variance Component ◽  
Random Effect ◽  
Random Effect Model ◽  
Error Variance ◽  
Class Effect ◽  
Heteroscedastic Error ◽  
Effect Model ◽  
Set Up ◽  
Estimation Of Variance ◽  
Better Than

For an unbalanced one-way classification under the random effect model where the error variance remains same within each class bttt varies over the classes, the problem of estimation of the variance component due to class effect is considered. A class of estimates including some estimates suggested from intuitive considerations is examined and it is shown that when the class size can assume only a finite number of distinct values, it is possible to set up an estimate which is asymptotically better than all the_ members of the class. Further, in the 1encral case it is shown that given any estimate belonging to a particular class, it is possible to find a corresponding estimate which is asymptotically bettor.

SIMULATED SHEWHART CONTROL CHART FOR MONITORING VARIANCE COMPONENTS

2009 ◽  
Vol 16 (01) ◽  
pp. 1-22 ◽  
Author(s):  
TZONG-RU TSAI ◽  
YI-WEI HSIEH
Keyword(s):  
Variance Components ◽  
Control Charts ◽  
Average Run Length ◽  
Random Effect ◽  
Random Effect Model ◽  
Simulation Method ◽  
Shewhart Control Charts ◽  
Effect Model ◽  
Shewhart Control ◽  
Simulation Results

Shewhart control charts based on the simulation method are proposed for monitoring separate variance components of the single-factor random effect model. Monte Carlo simulation results show that the proposed control charts have competitive performance relative to the approximate Shewhart regression control charts (ARSCCs) proposed by Chang and Gan2 in terms of the average run length (ARL). Compared with the ARSCCs, our control charts can be constructed easily with less sample resources. The application of our proposed method is illustrated with two examples.

Analisis Pengaruh Kepemilikan Blockholder, Kepemilikan Manajerial, Kepemilikan Institusional, dan Komite Audit terhadap Nilai Perusahaan

2018 ◽  
Vol 2 (1) ◽  
pp. 96-121
Author(s):  
Iwan Wirawardhana ◽  
Meco Sitardja
Keyword(s):  
Firm Value ◽  
Audit Committee ◽  
Stock Exchange ◽  
Random Effect ◽  
Random Effect Model ◽  
Institutional Ownership ◽  
Managerial Ownership ◽  
Estimation Model ◽  
Effect Model ◽  
Positive Effect

The aim of this study is to analyse the effect of Blockholder Ownership, Managerial Ownership,Institutional Ownership, and Audit Committee towards Firm Value. The background of this research isthe agency theory and ownership theory. The population in this study are 46 property companies listedon the Indonesia Stock Exchange (IDX) for the period 2012-2016. By using purposive samplingtechnique, 35 companies are qualified as data samples. This research uses the random effect model asthe estimation model and multiple regression as the method of analysis. The results of this study showsthat Institutional Ownership has a positive effect on Firm Value. Meanwhile, Blockholder Ownership,Managerial Ownership, and Audit Committee have no effect on Firm Value. Moreover, the F-testimplies that the variables, blockholder ownership, managerial ownership, institutional ownership, andaudit committee, simultaneously influence firm value.

scholarly journals Alteration of Postural Balance in Patients with Fibromyalgia Syndrome—A Systematic Review and Meta-Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 127
Author(s):  
David Núñez-Fuentes ◽  
Esteban Obrero-Gaitán ◽  
Noelia Zagalaz-Anula ◽  
Alfonso Javier Ibáñez-Vera ◽  
Alexander Achalandabaso-Ochoa ◽  
...  
Keyword(s):  
Systematic Review ◽  
Postural Balance ◽  
Fibromyalgia Syndrome ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Healthy Controls ◽  
Functional Balance ◽  
Sensory Organization ◽  
Effect Model

Balance problems are one of the most frequent symptoms in patients with Fibromyalgia Syndrome (FMS). However, the extent and nature of this balance disorder are not known. The objective of this work was to determine the best evidence for the alteration of postural balance in patients with FMS and analyze differences with healthy controls. To meet this objective, a systematic review with meta-analysis was performed. A bibliographical search was carried out in PubMed Medline, Scopus, Web of Science, CINAHL and SciELO. Observational studies that assessed postural balance in patients with FMS compared to healthy subjects in baseline conditions, were selected. In a random-effect model, the pooled effect was calculated with the Standardized Mean Difference (SMD) and its 95% confidence interval (CI). Nineteen studies reporting data of 2347 participants (95% female) were included. FMS patients showed poor balance with a large effect on static (SMD = 1.578; 95% CI = 1.164, 1.992), dynamic (SMD = 0.946; 95% CI = 0.598, 1.294), functional balance (SMD = 1.138; 95% CI = 0.689, 1.588) and on balance confidence (SMD = 1.194; 95% CI = 0.914, 1.473). Analysis of the Sensory Organization Test showed large alteration of vestibular (SMD = 1.631; 95% CI = 0.467, 2.795) and visual scores (SMD = 1.317; 95% CI = 0.153, 2.481) compared to healthy controls. Patients with FMS showed worse scores for different measures of postural balance compared to healthy controls. Concretely, FMS patients appear to have poor vestibular and visual scores with a possible somatosensory dependence.

scholarly journals Designing a Bonus-Malus system reflecting the claim size under the dependent frequency–severity model

2021 ◽  
pp. 1-25
Author(s):  
Rosy Oh ◽  
Joseph H.T. Kim ◽  
Jae Youn Ahn
Keyword(s):  
Recent Literature ◽  
Insurance Industry ◽  
Random Effect ◽  
Random Effect Model ◽  
Partial Solution ◽  
Risk Classification ◽  
Transition Rule ◽  
Auto Insurance ◽  
Dependent Frequency ◽  
Effect Model

In the auto insurance industry, a Bonus-Malus System (BMS) is commonly used as a posteriori risk classification mechanism to set the premium for the next contract period based on a policyholder's claim history. Even though the recent literature reports evidence of a significant dependence between frequency and severity, the current BMS practice is to use a frequency-based transition rule while ignoring severity information. Although Oh et al. [(2020). Bonus-Malus premiums under the dependent frequency-severity modeling. Scandinavian Actuarial Journal 2020(3): 172–195] claimed that the frequency-driven BMS transition rule can accommodate the dependence between frequency and severity, their proposal is only a partial solution, as the transition rule still completely ignores the claim severity and is unable to penalize large claims. In this study, we propose to use the BMS with a transition rule based on both frequency and size of claim, based on the bivariate random effect model, which conveniently allows dependence between frequency and severity. We analytically derive the optimal relativities under the proposed BMS framework and show that the proposed BMS outperforms the existing frequency-driven BMS. Later, numerical experiments are also provided using both hypothetical and actual datasets in order to assess the effect of various dependencies on the BMS risk classification and confirm our theoretical findings.

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