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An attempt was made in the present study to elucidate the (co)variance components and genetic parameters of fiber traits in Kashmir Merino sheep population using multivariate animal model. The data...

We propose a latent-lognormal variance components model for counts that is analogous to the standard normal model for continuous measurements. We analyze gauge repeatability and reproducibility stu...

This article develops a new method called iteratively reweighted least squares with random effects (IRWLSR) for maximum likelihood in generalized linear mixed effects models (GLMMs). As normal dist...

This paper considers the optimal design problem for predicting a linear combination of fixed and random effects when the variance components in the linear mixed model are known or unknown. New desi...

Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown parameters in linear mixed-effects models with high-dimensional fixed effects. The proposed method is applicable to general settings where the dimension of the random effects and the cluster sizes are possibly large. Regarding the fixed effects, we provide rate optimal estimators and valid inference procedures that do not rely on the structural information of the variance components. We also study the estimation of variance components with high-dimensional fixed effects in general settings. The algorithms are easy to implement and computationally fast. The proposed methods are assessed in various simulation settings and are applied to a real study regarding the associations between body mass index and genetic polymorphic markers in a heterogeneous stock mice population., 32 pages, 3 figures

The concordance correlation coefficient (CCC) is a common tool to assess agreement among multiple observers for continuous and discrete responses. However, previous results in the statistical literature have shown that the CCC estimators may suffer from a bias problem under a misspecified model for normal data. In order to avoid fitting data with a misspecified model, thus yielding biased CCC estimates for longitudinal overdispersed Poisson data, this research proposes new model-averaged estimators of CCC by combining the estimators of the variance components (VC) approach with model selection via corrected conditional Akaike information criterion (CAICC) and corrected conditional Bayesian information criterion (CBICC) measures under extended overdispersed three-way Poisson mixed-effects models. In simulation studies, the performance of the proposed model-averaged estimators is compared with the VC estimators with and without model selection via CAICC and CBICC and other existing model-averaged estimators for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is presented for illustration. It can be concluded that the proposed model-averaged approach is a reliable procedure yielding small mean square errors and nominal 95% coverage rates. Therefore, the new model-averaged estimator is more robust to model misspecification than other competitors.

The current study aimed to estimate (co)variance components and heritability for pre-weaning body weight traits and Kleiber ratio in Sangsari sheep. Data used in this research were collected over 30 years (1986–2016) at the breeding station of Sangsari sheep. Traits considered were birth weight (BW), weaning weight (WW), pre-weaning daily gain (PWDG), and Kleiber ratio (KR). Genetic parameters were estimated by the average information restricted maximum likelihood (AI-REML) procedure, using six animal models. The most suitable model was selected using the Akaike information criterion (AIC) for each trait. Direct heritability estimates for BW, WW, PWDG and KR were 0.30 0.04, 0.20 0.03, 0.15 0.02, and 0.13 0.05, respectively. Maternal heritability estimates varied from 0.06 0.02 for PWDG to 0.12 0.03 for BW. The maternal permanent environmental effects for the traits of KR, WW and PWDG account for about 7%, 9% and 12% of the phenotypic variance. The estimates of the direct genetic () and maternal genetic () correlations among pre-weaning growth traits were positive and ranged from 0.21 (BW-KR) to 0.98 (WW-PWDG) and 0.27 (WW-PWDG) to 0.48 (BW-WW), respectively. Therefore, to have an accurate genetic evaluation, maternal effects need to be considered in any program aimed at improving pre-weaning growth traits. In addition, due to the existence of genetic variation for early growth traits and generally positive and medium to high genetic correlations among them, it can be concluded that in the Sangsari breed of sheep, genetic progress for these traits is possible by selection.HIGHLIGHTS Genetic parameters (direct and maternal heritability) were estimated for the Sangsari breed of sheep. The low to moderate estimate of heritability for pre-weaning body weights and Kleiber ratio indicate that genetic improvement of these traits is possible by selection. Considerable genetic correlations among early body weights, pre-weaning daily weight gain, and Kleiber ratio in Sangsari sheep were estimated.

Family evaluation has been adopted to improve selection in the early stages of sugarcane breeding. However, little knowledge is available for comparing families to genotypes among diverse sugarcane...

The management of abandoned coppiced forests is an important issue for sustainable forestry. Lithocarpus edulis (Fagaceae) is a rarely-used hardwood species in Japan, although it is an abundant and...

A constrained extended Kalman filter (CEKF) based on least-squares variance component estimation (LS-VCE) is generally developed by condition equations since the proper prediction of dispersion mat...

In this paper, we consider the one-sided hypothesis testing problems for variance component functions in the unbalanced two-way random effects model. Firstly, using the Bootstrap approach and gener...

In this paper the problem of simultaneous linear estimation of fixed and random effects in a special random model with two variance components is considered. An influence of the expectation vector ...

In the recent past, the CAT procedure introduced by Pal, Lim, and Ling has been successfully applied to obtain improved testing procedures in numerous applications. Having seen such results, practi...

The South African sugar industry develops commercial sugarcane cultivars across multiple testing sites, crop-years and replications. The extent of such parameters has not been well quantified for o...

Measurement uncertainty (MU) can be estimated and calculated by different procedures, representing different aspects and intended use. It is appropriate to distinguish between uncertainty determined under repeatability and reproducibility conditions, and to distinguish causes of variation using analysis of variance components. The intra-laboratory MU is frequently determined by repeated measurements of control material(s) of one or several concentrations during a prolonged period of time. We demonstrate, based on experimental results, how such results can be used to identify the repeatability, pure reproducibility and intra-laboratory variance as the sum of the two. Native patient material was used to establish repeatability using the Dahlberg formula for random differences between measurements and an expanded Dahlberg formula if a non-random difference, e.g. bias, was expected. Repeatability and reproducibility have different clinical relevance in intensive care compared to monitoring treatment of chronic diseases, comparison with reference intervals or screening. Funding Agencies|Karolinska university laboratory; County council of Ostergotland

Testing the absence of random effects in the nested error regression model is nonstandard because it requires the null value of the random effects variance to be on the boundary of its spac...

A semiparametric mixed analysis of covariance model is postulated. This model is estimated by imbedding restricted maximum likelihood estimation and smoothing splines regression into the backfittin...

The sucrose accumulation curve represents the maturity profile of a genotype (G). Computing the accumulation curves helps determine optimal harvesting period and explore genetic variability. Howeve...

In this study, the collocation method accompanied with variance component estimation is used for least square adjustment of crossover observations in order to determine the effects of radial errors...

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Textbooks on response surface methodology generally stress the importance of lack-of-fit tests and estimation of pure error. For lack-of-fit tests to be possible and other inference to be unbiased, experiments should allow for pure-error estimation. Therefore, they should involve replicated treatments. While most textbooks focus on lack-of-fit testing in the context of completely randomized designs, many response surface experiments are not completely randomized and require block or split-plot structures. The analysis of data from blocked or split-plot experiments is generally based on a mixed regression model with two variance components instead of one. In this article, we present a novel approach to designing blocked and split-plot experiments which ensures that the two variance components can be efficiently estimated from pure error and guarantees a precise estimation of the response surface model. Our novel approach involves a new Bayesian compound D-optimal design criterion which pays attention to both the variance components and the fixed treatment effects. One part of the compound criterion (the part concerned with the treatment effects) is based on the response surface model of interest, while the other part (which is concerned with pure-error estimates of the variance components) is based on the full treatment model. We demonstrate that our new criterion yields split-plot designs that outperform existing designs from the literature both in terms of the precision of the pure-error estimates and the precision of the estimates of the factor effects.

Statistical methods are often required in the pharmaceutical industry to ensure that precision parameters of interest are kept within acceptable levels. For example, analytical methods are validate...

Variance components must be obtained to estimate genetic parameters and predict breeding values. In studies which take many traits into account, it is reasonable to use the Bayesian approach for th...

Tef [Eragrostis tef (Zucc.) Trotter L.] is an indigenous and important cereal crop in Ethiopia. Its improvement is dependent on the presence and magnitude of genetic variation among traits in a given environment. Forty-nine tef genotypes (RILs and checks) were evaluated in 2019 to assess the nature of genetic variability, broad sense heritability and genetic advance of traits using 7 × 7 simple lattice design. Analysis of variance revealed significant (P ≤ 0.05) differences among the genotypes except for thousand seed weight. About 53% of the genotypes provided higher yield than the latest improved variety (Ebba), implied that most of the studied RILs varied for grain yield. Higher phenotypic and genotypic coefficients of variation found for main shoot panicle weight, main shoot panicle grain yield, fertile tiller number and days to heading, grain-filling period, main-shoot panicle length, total-tiller number, fertile-tiller number, main-shoot panicle weight, main-shoot panicle grain yield, grain yield and harvest index, implying the presence of adequate variability within the genotypes for these traits. High values of broad sense heritability coupled with high genetic advance as percent of the mean were observed for days to heading, grain-filling period, peduncle length, fertile-tiller number and grain yield indicating the contribution of additive genes for trait expression and possibility of traits improvement through direct selection. Generally, this considerable genetic variability among the RILs confirmed the possibility of improving tef productivity, while most of these RILs should further be studied on more test locations.

A complete diallel cross was made among nine Betula pendula trees growing in a natural population and a trial was planted on agricultural soil at one site. This exceptional trial has provided estim...

The extent of variance and heritability of traits influence the response to selection in crop breeding programs. The objective of this study was to determine variance components and heritability in...

When using multilevel regression models that incorporate cluster-specific random effects, the Wald and the likelihood ratio (LR) tests are used for testing the null hypothesis that the variance of the random effects distribution is equal to zero. We conducted a series of Monte Carlo simulations to examine the effect of the number of clusters and the number of subjects per cluster on the statistical power to detect a non-null random effects variance and to compare the empirical type I error rates of the Wald and LR tests. Statistical power increased with increasing number of clusters and number of subjects per cluster. Statistical power was greater for the LR test than for the Wald test. These results applied to both the linear and logistic regressions, but were more pronounced for the latter. The use of the LR test is preferable to the use of the Wald test.

We treat a model with independent claim numbers and claim amounts, conditional on stochastic parameters. Groups are categorized into a smaller number of classes, which likely differ in risk premium...

Genotype × environment interactions (G×E) can reduce the genetic contribution to the phenotype and increase minimum detectable differences, which eventually reduce selection precision. This study w...

Recent arguments have been made that optimal design criteria should incorporate pure error degrees of freedom for estimating unknown variance components. In this paper, we incorporate pure error, a...

The inter-rater reliability of university students’ evaluations of teaching quality was examined with cross-classified multilevel models. Students (N = 480) evaluated lectures and seminars over three years with a standardised evaluation questionnaire, yielding 4224 data points. The total variance of these student evaluations was separated into the variance components of courses, teachers, students and the student/teacher interaction. The substantial variance components of teachers and courses suggest reliability. However, a similar proportion of variance was due to students, and the interaction of students and teachers was the strongest source of variance. Students’ individual perceptions of teaching and the fit of these perceptions with the particular teacher greatly influence their evaluations. This casts some doubt on the validity of student evaluations as indicators of teaching quality and suggests that aggregated evaluation scores should be used with caution.

We derive the explicit form for the asymptotic posterior distribution of the balanced nested multi-way variance components model with the assumption that the number of the main factor levels tends to infinity while the number of any specific effect factor levels remains fixed. Under the multi-way model, we also study two different parameterizations, called the standard and the centering, and the relationship between certain quadratic forms of random effects and the variance component parameters. The asymptotic results are illustrated by a three-way model and by a simulation study under a two-way case.

A lack of performance parameters is one of the factors limiting the implementation of sustainable breeding strategies for the pig industry in Kenya. The objective of this study was to estimate genetic and phenotypic parameters for growth performance of Large White pigs reared under intensive management systems in Kenya. Growth performance data of 1398 pigs with 10 428 records were obtained from the Kenya Agricultural and Livestock Research Organization (KALRO)-Naivasha. Random regression models were used to estimate variance components by fitting different orders of Legendre polynomials. Phenotypic variance increased with age from 3.43±0.28 to 2449.28±392.07, while direct heritability ranged between 0.20±0.04 and 0.52±0.08. Maternal heritability increased from 0.26±0.05 to 0.79±0.04 while permanent environmental heritability was between 0±0.01 and 0.15±0.10. Genetic correlations were greater than 0.48 between all weights and decreased with an increase in age intervals. The first three eigenvalues ...

To reduce the dimensionality of the second-order response surface design model, variance component indices under imposing and non imposing restrictions on the moment matrix toward the orthogonality are derived and presented and the same is illustrated with suitable examples in this article.

Does the Presidency of the Council of the European Union have the ability to direct the political attention of this body by emphasizing and de-emphasizing policy issues according to its own priorities? This study examines this question empirically by relying on a new dataset on the monthly meeting duration of Council working parties in different policy areas between 1995 and 2014. The results of variance component analyses show that a considerable part of the over-time variation in the relative amount of political attention devoted to a policy area is systematically related to different Presidency periods. While not negating the constraints imposed on the Presidency by inherited agendas, programming and co-ordination requirements with other actors, the findings are consistent with the view that the Presidency has substantial scope for agenda-setting by determining what issues are being discussed, when they are being discussed and how much time is devoted for their discussion.

Quantifying heritability is the first step in understanding the contribution of genetic variation to the risk architecture of complex human diseases and traits. Heritability can be estimated for univariate phenotypes from nonfamily data using linear mixed effects models. There is, however, no fully developed methodology for defining or estimating heritability from longitudinal studies. By examining longitudinal studies, researchers have the opportunity to better understand the genetic influence on the temporal development of diseases, which can be vital for populations with rapidly changing phenotypes such as children or the elderly. To define and estimate heritability for longitudinally measured phenotypes, we present a framework based on functional data analysis, FDA. While our procedures have important genetic consequences, they also represent a substantial development for FDA. In particular, we present a very general methodology for constructing optimal, unbiased estimates of variance components in fu...

The Angoff method requires experts to view every item on the test and make a probability judgment. This can be time consuming when there are large numbers of items on the test. In this study, a G-theory framework was used to determine if a subset of items can be used to make generalizable cut-score recommendations. Angoff ratings (i.e., probability judgments) from previously conducted standard setting studies were used first in a re-sampling study, followed by D-studies. For the re-sampling study, proportionally stratified subsets of items were extracted under various sampling and test-length conditions. The mean cut score, variance components, expected standard error (SE) around the mean cut score, and root-mean-squared deviation (RMSD) across 1,000 replications were estimated at each study condition. The SE and the RMSD decreased as the number of items increased, but this reduction tapered off after approximately 45 items. Subsequently, D-studies were performed on the same datasets. The expected SE was ...

Minimum detectable differences (MDD) are the smallest difference between two genotypes that can be determined accurately at a given statistical significance threshold. The effect of genotype by environment interaction reduces accuracy of determining genetic values, increases MDD and reduces selection efficiency. The objective of this study was to determine the trends in MDD and their implications on cultivar development for the Midlands and Coastal Short Cycle sugarcane breeding programmes in South Africa. Data derived from 30 genotypes planted in six advanced cultivar trials in each programme were analysed to generate variance components used to estimate the MDD. The MDD decreased from planted to second ratoon crops, indicating lower efficiency associated with selecting in the planted crops. The lower MDD with a simulated fourth replication indicated the need to increase the number of replications in cultivar trials. The higher MDD for sugar yield compared with those for sucrose content suggested a low efficiency when selecting for sugar yield. Selecting for sugar yield using the components cane yield and sucrose content may increase efficiency. The Midlands programme was more efficient and produced smaller MDD than the Coastal Short Cycle programme. The MDD will be used to benchmark minimum gains at time of release.Keywords: minimum detectable differences, selection efficiency, sugar yield, variance components

Cassava storage roots start deteriorating 1 to 3 days after harvest due to postharvest physiological deterioration (PPD) which reduces the potential of cassava as a commercial crop. The objective of this study was to examine the extent of genetic variability in cassava for PPD. Genetic variability is important in selecting suitable genotypes for crop improvement. Twelve cassava genotypes were evaluated in a randomized complete block design at three different locations in Uganda for five different harvest times, and PPD assessment was done 7 days after each harvest time. Genotype, location, harvest time, genotype × harvest time and harvest time × location were highly significantly different for PPD. Estimates of phenotypic, genotypic and environmental variance components revealed that a large portion of the phenotypic variance was accounted for by the genotypic component which is an indication of substantial genetic variability among the test genotypes for PPD. This genetic variability is important in a br...

In generalizability theory studies in large-scale testing contexts, sometimes a facet is very sparsely crossed with the object of measurement. For example, when assessments are scored by human raters, it may not be practical to have every rater score all students. Sometimes the scoring is systematically designed such that the raters are consistently grouped throughout the scoring, so that the data can be analyzed as raters nested within teams. Other times, rater pairs are randomly assigned for each student, such that each rater is paired with many other raters at different times. One possibility for this scenario is to treat the data as if raters were nested within students. Because the raters are not truly independent across all students, the resulting variance components could be somewhat biased. This study illustrates how the bias will tend to be small in large-scale studies.

Previous literature suggests that performance ratings are saturated with rater-related idiosyncratic variance. Given that modern psychometric theories relegate this source of variance to measurement error, it has not been the subject of much previous research. Of importance, identifying and estimating the variance components underlying idiosyncratic rater variance will inform our understanding of the nature of this variance. In a sample of managerial performance ratings we report on components of variance and find that the idiosyncratic rater variance component is about one third rater main effects variance, one third Rater × Ratee interaction effects variance, and one third upper-bound Rater × Ratee × Dimension interaction effects variance. Further, results indicate that variance components are moderated by the acquaintanceship time between the rater and the ratee.

The present investigation was undertaken to estimate the (co)variance components and genetic parameters for different growth traits in Marwari flock comprising records of 1649 animals distributed over a period of 12 years (1999–2010), maintained at Arid Region Campus of Central Sheep and Wool Research Institute, Bikaner, Rajasthan, India. The estimation was done by restricted maximum likelihood procedures, fitting six animal models with various combinations of direct and maternal effects. As per likelihood ratio test, direct heritability estimates from the best model for body weight at birth, weaning, 6, 9 and 12 months of age, and average daily gain during birth to weaning, weaning to 6 and 6–12 months of age were 0.28 ± 0.058, 0.27 ± 0.050, 0.28 ± 0.049, 0.30 ± 0.080, 0.29, 0.26 ± 0.050, 0.16 ± 0.040 and 0.31, respectively. Maternal genetic effect declined from 4% at 6 months weight to 1% at 9 months and was zero at 12 months of age. Maternal genetic effect on the post-weaning traits was a carryover eff...

The Best Linear Unbiased Predictor (BLUP) in mixed models is a function of the variance components and they are estimated using maximum likelihood (ML) or restricted ML methods. Nonconvergence of BLUP would occur due to a drawback of the standard likelihood-based approaches. In such situations, ML and REML either do not provide any BLUPs or all become equal. To overcome this drawback, we provide a generalized estimate (GE) of BLUP that does not suffer from the problem of negative or zero variance components, and compare its performance against the ML and REML estimates of BLUP. Simulated and published data are used to compare BLUP.

Instructions for use of clinical chemical assays and other instruments commonly include figures for the precision at different levels of the measurand. Frequently neither the variance nor the coefficient of variation (CV) of the measure is constant across the range of measurand values, making such tables difficult to summarize or use. A more flexible variance model fills in the gap between the constant variance and constant CV models, leading to compact summaries of these precision tables and allowing interpolation to levels not included in the study. Likelihood ratio tests show when the more flexible model is needed.

We consider the Fuller–Battese model where random effects are allowed to be from non-normal universes. The asymptotic distribution of the F-statistic in this model is derived as the number of groups tends to infinity (is large) and sample size from any group is either fixed or large. The result is used to establish an approximate test for the significance of the random effect variance component. Robustness of the established approximate test is given.

This article addresses derivation and existence of quadratic forms that were suggested by Burch (2007) for procedures for inference on variance components in mixed linear models in combination with generalized fiducial inference. A relatively simple algorithm leading to the required quadratic forms in a general 3-variance-component model is stated and designs for two-way ANOVA models without interactions that permit Burch's procedure are characterized. This complements developments in the original article by Burch.

In this article, the Bayes estimators of variance components are derived and the parametric empirical Bayes estimators (PEBE) for the balanced one-way classification random effects model are constructed. The superiorities of the PEBE over the analysis of variance (ANOVA) estimators are investigated under the mean square error (MSE) criterion, some simulation results for the PEBE are obtained. Finally, a remark for the main results is given.

The present study was conducted to estimate variance components and genetic parameters for growth traits of Iran-Black sheep, maintained at the Abbasabad sheep breeding station, located in northeast of Mashhad, (Razavi Khorasan province of Iran) over a period of 24 years (1981 through 2004). Records of 4607 lambs descended from 155 rams, and 1227 ewes were used in the study. Traits included for the present study were birth weight (BW), weaning weight (WW), weight at 6 months (W6), weight at 9 months (W9), and yearling weight (YW). Analyses were carried out by restricted maximum likelihood (REML) fitting an animal model and ignoring or including maternal genetic or permanent environmental effects. Six different animal models were fitted for all traits. The most appropriate model was chosen after testing the improvement of the log-likelihood values. Heritability estimates for weight at birth; weaning; and 6, 9, and 12 months of age were 0.04, 0.14, 0.16, 0.19, and 0.18, respectively. Maternal heritability o...

Previous research indicates that three-level modeling is a valid statistical method to make inferences from unstandardized data from a set of single-subject experimental studies, especially when a homogeneous set of at least 30 studies are included ( Moeyaert, Ugille, Ferron, Beretvas,Van den Noortgate, 2013a ). When single-subject data from multiple studies are combined, however, it often occurs that the dependent variable is measured on a different scale, requiring standardization of the data before combining them over studies. One approach is to divide the dependent variable by the residual standard deviation. In this study we use Monte Carlo methods to evaluate this approach. We examine how well the fixed effects (e.g., immediate treatment effect and treatment effect on the time trend) and the variance components (the between- and within-subject variance) are estimated under a number of realistic conditions. The three-level synthesis of standardized single-subject data is found appropriate for the estimation of the treatment effects, especially when many studies (30 or more) and many measurement occasions within subjects (20 or more) are included and when the studies are rather homogeneous (with small between-study variance). The estimates of the variance components are less accurate.

This paper reports on a study that compares estimates of the reliability of a suite of workplace based assessment forms as employed to formatively assess the progress of trainee obstetricians and gynaecologists. The use of such forms of assessment is growing nationally and internationally in many specialties, but there is little research evidence on comparisons by procedure/competency and form-type across an entire specialty. Generalisability theory combined with a multilevel modelling approach is used to estimate variance components, G-coefficients and standard errors of measurement across 13 procedures and three form-types (mini-CEX, OSATS and CbD). The main finding is that there are wide variations in the estimates of reliability across the forms, and that therefore the guidance on assessment within the specialty does not always allow for enough forms per trainee to ensure that the levels of reliability of the process is adequate. There is, however, little evidence that reliability varies systematically by form-type. Methodologically, the problems of accurately estimating reliability in these contexts through the calculation of variance components and, crucially, their associated standard errors are considered. The importance of the use of appropriate methods in such calculations is emphasised, and the unavoidable limitations of research in naturalistic settings are discussed.