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Statistics in animal breeding: angels and demons
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gianola, Daniel |
| Copyright Year | 2006 |
| Abstract | INTRODUCTION Statistical science is central to animal breeding and quantitative genetics. It would be redundant to argue why; the case has been made at least since 1974, then at the Madrid Congress. The WCGALP Proceedings contain information on how statistical ideas evolved in animal breeding. Briefly, path analysis and correlations (Wright, Lush) were replaced by linear least-squares (Harvey), the latter was recognized to be a special case of maximum likelihood under normality (Searle), random effects models came into the picture, and best linear unbiased prediction (Henderson), followed by REML (Robin Thompson) became focal points. Subsequently, non-linear specifications captured some attention (notably threshold models and survival analysis), Bayesian demons (unmentionable) became possessive, in spite of many futile Fugite partes adversae exorcisms, and longitudinal data structures begun to be exploited, primarily in dairy cattle breeding. Any serious student should also be familiar with foundational work of Fisher, Haldane and Pearson. Many other names can be added to the list (yes, we love you, even though you are not here), but these, we believe, were at the center of the stage. |
| File Format | PDF HTM / HTML |
| Volume Number | 2006 |
| Alternate Webpage(s) | http://www.wcgalp.org/system/files/proceedings/2006/statistics-animal-breeding-angels-and-demons.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
