Variance estimate for the Allen activity index

1998 ◽  
Vol 25 (6) ◽  
pp. 643 ◽  
Author(s):  
Richard M. Engeman ◽  
Lee Allen ◽  
Gary O. Zerbe
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Activity Index ◽  
Likelihood Estimation ◽  
Mixed Linear Model ◽  
Variance Formula ◽  
Variance Estimate ◽  
Wildlife Populations ◽  
Variance Component Estimates ◽  
Restricted Maximum Likelihood Estimation

The Allen activity index, originally developed for monitoring dingo populations, is statistically described as a mixed linear model, from which a variance formula for the index is derived. The resulting formula requires input of variance component estimates, the estimation of which is accomplished using restricted maximum-likelihood estimation. An example is used to demonstrate the calculation of the variance components and their use in the variance formula. Application of the variance formula substantially enhances the quantitative practicality of this useful index of wildlife populations.

Bias in Variance Components Due to Nonresponse in Twin Studies

2006 ◽  
Vol 9 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Annica Dominicus ◽  
Juni Palmgren ◽  
Nancy L. Pedersen
Keyword(s):  
Cognitive Abilities ◽  
Variance Components ◽  
Variance Component ◽  
Pair Correlation ◽  
Twin Studies ◽  
Likelihood Estimation ◽  
Genetic Influences ◽  
Perceptual Speed ◽  
Environmental Variance ◽  
Variance Component Estimates

AbstractIncomplete data on trait values may bias estimates of genetic and environmental variance components obtained from twin analyses. If the nonresponse mechanism is ‘ignorable’ then methods such as full information maximum likelihood estimation will produce consistent variance component estimates. If, however, nonresponse is ‘nonignorable’, then the situation is more complicated. We demonstrate that a within-pair correlation of nonresponse, possibly different for monozygotic (MZ) and dizygotic (DZ) twins, may well be compatible with ‘ignorability’. By means of Monte Carlo simulation, we assess the potential bias in variance component estimates for different types of nonresponse mechanisms. The simulation results guide the interpretation of analyses of data on perceptual speed from the Swedish Adoption/Twin Study of Aging. The results suggest that the dramatic decrease in genetic influences on perceptual speed observed after 13 years of follow-up is not attributable solely to dropout from the study, and thus support the hypothesis that genetic influences on some cognitive abilities decrease with age in late life.

Sampling Errors of Variance Components in Small Sample Multifacet Generalizability Studies

1978 ◽  
Vol 3 (4) ◽  
pp. 319-346 ◽  
Author(s):  
Philip L. Smith
Keyword(s):  
Monte Carlo ◽  
Variance Components ◽  
Variance Component ◽  
Generalizability Theory ◽  
Small Sample ◽  
Least Square ◽  
Sampling Errors ◽  
Sample Stability ◽  
Sampling Behavior ◽  
Variance Component Estimates

The paper describes the small sample stability of least square estimates of variance components within the context of generalizability theory. Monte Carlo methods are used to generate data conforming to some selected multifacet generalizability designs to illustrate the sampling behavior of variance component estimates. Based on the findings, recommendations are made concerning the design of efficient small sample generalizability studies.

The Sensitivity of Variance Component Estimates to Underreporting: Method and Application to Substance Abuse Data

2007 ◽  
Vol 10 (5) ◽  
pp. 721-728 ◽  
Author(s):  
Paul W. Andrews ◽  
Kenneth S. Kendler ◽  
Nathan Gillespie ◽  
Michael C. Neale
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Illicit Drug ◽  
Sensitive Information ◽  
Twin Design ◽  
Psychological Constructs ◽  
Willingness To Disclose ◽  
Classical Twin Design ◽  
Illicit Drug Abuse ◽  
Variance Component Estimates

AbstractMany studies of human behavior and psychological constructs rely on subjects' willingness to disclose information about themselves. This is problematic for phenotypes that require the disclosure of sensitive information, such as sexual behavior or illicit drug use, which are likely to be underreported. We describe a method for evaluating how sensitive variance component estimates are to underreporting. The method involves estimating, by maximum likelihood, the original population proportions of the response classes, and adjusting them for a set of hypothesized underreporting parameters. If the true values of the underreporting parameters were known, the researcher could estimate the variance components based on these values. Usually, underreporting levels are not known with certainty. However, it is possible to assume a specific value for the underreporting rate, obtain response pattern proportions adjusted for this rate, and then to conduct the analyses on these revised estimates. By repeating the procedure across the range of plausible underreporting values, the researcher can assess how sensitive the variance component estimates are to variation in underreporting. We apply this method to a sample of male-male twin pairs who reported on themselves and their co-twins for illicit drug abuse and dependence (DAD). We show how underreporting influences estimates of additive genetic, common environment, and specific environment variance components (A, C, and E) obtained for DAD in a classical twin design.

THE EFFECT OF ADJUSTING FOR YEARLY SELECTION TRENDS ON VARIANCE COMPONENT ESTIMATES

1989 ◽  
Vol 69 (2) ◽  
pp. 487-490
Author(s):  
W. W. GEARHEART ◽  
M. E. DAVIS ◽  
W. R. HARVEY
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Variance Components ◽  
Variance Component ◽  
Error Variance ◽  
Component Selection ◽  
Variance Component Estimates ◽  
Selection Of

Computer-generated beef cattle data were used to investigate the effect of accounting for the yearly selection of parents on the bias and precision of sire and error variance component estimates. The adjustment for yearly selection reduced the biases of estimated sire variance components, but resulted in losses of precision of up to 25%. Key words: Beef cattle, variance component, selection

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