| dc.contributor.author | Li, Xiaowei. | en_US |
| dc.date.accessioned | 2014-10-21T12:37:35Z | |
| dc.date.available | 1996 | |
| dc.date.issued | 1996 | en_US |
| dc.identifier.other | AAINN15859 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/55118 | |
| dc.description | A method to estimate variance components with missing data is presented. A typical application is in aquaculture genetics, in which breeding procedure may produce thousands of individuals. This method enables us to estimate genetic variance components when only a small proportion of individuals, those with extreme phenotypes, have been identified. In aquaculture populations the individuals available for measurement will often be selected, i.e. will come from the upper tail of a size-at-age distribution, or the lower tail of an age-at-maturity distribution etc. | en_US |
| dc.description | Standard analysis of variance or maximum likelihood estimation cannot be used when missing data is not missing at random because of the biased nature of the estimates. In our model-based procedure a full likelihood function is defined, in which the missing information has been taken into account. The likelihood function is transformed into a computable function which is maximized to get the estimates. The computational methodology is outlined and a program is available. | en_US |
| dc.description | This method is applied to simulated data an aquacultural data. The results obtained are significantly and uniformly more accurate than those obtained by any of the standard methods. Different issues concerning the method (such as the existence, uniqueness, confidence intervals, robust procedure, and random effects estimation) have been discussed in the thesis. | en_US |
| dc.description | Thesis (Ph.D.)--Dalhousie University (Canada), 1996. | en_US |
| dc.language | eng | en_US |
| dc.publisher | Dalhousie University | en_US |
| dc.publisher | | en_US |
| dc.subject | Biology, Biostatistics. | en_US |
| dc.subject | Statistics. | en_US |
| dc.title | Estimation of variance components with missing data. | en_US |
| dc.type | text | en_US |
| dc.contributor.degree | Ph.D. | en_US |
