Quantile dispersion graphs for analysis of variance estimates of variance components

1997 ◽  
Vol 24 (6) ◽  
pp. 711-722 ◽  
Author(s):  
A. I. Khuri
Keyword(s):  
Analysis Of Variance ◽  
Variance Components ◽  
Variance Estimates ◽  
Quantile Dispersion Graphs

On Describing an Incidence Sample

1982 ◽  
Vol 7 (4) ◽  
pp. 311-331 ◽  
Author(s):  
Gwyneth M. Boodoo
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Standard Errors ◽  
Eighth Grade ◽  
Eighth Grade Students ◽  
Competency Based ◽  
Mean And Variance ◽  
The Mean ◽  
Using Data ◽  
Variance Estimates

Parameters used to describe an incidence sample are estimated using the theory of generalized symmetric means and generalizability theory. The former is used to compute estimates of the mean and variance components in an ANOVA framework, while the latter is used in obtaining generalizability coefficients. Standard errors of the variance estimates are obtained. The procedure is illustrated using data from two competency-based tests given to eighth grade students in mathematics and reading.

scholarly journals Analysis of variance components in gene expression data

2004 ◽  
Vol 20 (9) ◽  
pp. 1436-1446 ◽  
Author(s):  
J. J. Chen ◽  
R. R. Delongchamp ◽  
C.-A. Tsai ◽  
H.-m. Hsueh ◽  
F. Sistare ◽  
...  
Keyword(s):  
Gene Expression ◽  
Analysis Of Variance ◽  
Gene Expression Data ◽  
Variance Components ◽  
Expression Data

scholarly journals Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler

2000 ◽  
Vol 76 (2) ◽  
pp. 187-198 ◽  
Author(s):  
F.-X. DU ◽  
I. HOESCHELE
Keyword(s):  
Variance Components ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Effects ◽  
Single Site ◽  
Phenotypic Data ◽  
Variance Components Estimation ◽  
Locus Number ◽  
Variance Estimates ◽  
Finite Locus

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

An Alternative Method for Estimating Variance Components in Generalizability Theory

1990 ◽  
Vol 66 (2) ◽  
pp. 379-386 ◽  
Author(s):  
George A. Marcoulides
Keyword(s):  
Maximum Likelihood ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Generalizability Theory ◽  
Simulated Data ◽  
Restricted Maximum Likelihood ◽  
Alternative Method ◽  
Sample Data

This study compares, using simulated data, two methods for estimating variance components in generalizability (G) studies. Traditionally variance components are estimated from an analysis of variance of sample data. The alternative method for estimating variance components is restricted maximum likelihood (REML). The results indicate that REML provides estimates for the components in the various designs that are closer to the true parameters than the estimates from analysis of variance.

A Note on inbreeding and genetic relationships among British tested pigs

1978 ◽  
Vol 27 (1) ◽  
pp. 125-128 ◽  
Author(s):  
C. Smith ◽  
C. H. C. Jordan ◽  
D. E. Steane ◽  
M. B. Sweeney
Keyword(s):  
Analysis Of Variance ◽  
Variance Components ◽  
Genetic Relationships ◽  
Large White ◽  
Genetic Contributions

Five samples from tested pig herds (Large White 1972, 1975, and 1976, British Landrace 1976 and Welsh 1976) were used to estimate the current rate of inbreeding in British pig testing herds. The annual rates of inbreeding (%) were estimated at 0·32, 0·19, 0·24, 0·33 and 0·34 respectively in the five samples. Overall average estimates of 0·49 to 0·52% per generation are similar to estimates from other pig populations reported in the literature. Coefficients of relationship within farms were calculated for various sib and non-sib groups and these were used to estimate the genetic contributions to the variance components in the analysis of variance of test records.

scholarly journals Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples

2021 ◽  
Author(s):  
Felix D. Schönbrodt ◽  
Caroline Zygar-Hoffmann ◽  
Steffen Nestler ◽  
Sebastian Pusch ◽  
Birk Hagemeyer
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Relative Proportion ◽  
Experience Sampling ◽  
Process Models ◽  
Sampling Studies ◽  
Reliable Assessment ◽  
Relationship Processes ◽  
Level Scale ◽  
Variance Estimates

AbstractThe investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

scholarly journals Nonparametric confidence interval estimators for heritability and expected selection response.

Genetics ◽  
1989 ◽  
Vol 121 (4) ◽  
pp. 891-898
Author(s):  
S J Knapp ◽  
W C Bridges-Jr ◽  
M H Yang
Keyword(s):  
Analysis Of Variance ◽  
Hypothesis Test ◽  
Selection Response ◽  
Family Effects ◽  
Variance Functions ◽  
Point Estimates ◽  
Estimation Problems ◽  
Interval Estimators ◽  
Variance Estimates ◽  
Normally Distributed

Abstract Statistical methods have not been described for comparing estimates of family-mean heritability (H) or expected selection response (R), nor have consistently valid methods been described for estimating R intervals. Nonparametric methods, e.g., delete-one jackknifing, may be used to estimate variances, intervals, and hypothesis test statistics in estimation problems where parametric methods are unsuitable, nonrobust, or undefinable. Our objective was to evaluate normal-approximation jackknife interval estimators for H and R using Monte Carlo simulation. Simulations were done using normally distributed within-family effects and normally, uniformly, and exponentially distributed between-family effects. Realized coverage probabilities for jackknife interval (2) and parametric interval (5) for H were not significantly different from stated probabilities when between-family effects were normally distributed. Coverages for jackknife intervals (3) and (4) for R were not significantly different from stated coverages when between-family effects were normally distributed. Coverages for interval (3) for R were occasionally significantly less than stated when between-family effects were uniformly or exponentially distributed. Coverages for interval (2) for H were occasionally significantly less than stated when between-family effects were exponentially distributed. Thus, intervals (3) and (4) for R and (2) for H were robust. Means of analysis of variance estimates of R were often significantly less than parametric values when the number of families evaluated was 60 or less. Means of analysis of variance estimates of H were consistently significantly less than parametric values. Means of jackknife estimates of H calculated from log transformed point estimates and R calculated from untransformed or log transformed point estimates were not significantly different from parametric values. Thus, jackknife estimators of H and R were unbiased. Delete-one jackknifing is a robust, versatile, and effective statistical method when applied to estimation problems involving variance functions. Jackknifing is especially valuable in hypothesis test estimation problems where the objective is comparing estimates from different populations.

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