Table of Contents

• Part I: Statistical Methods in Animal Improvement
  • Statistical Methods in Animal Improvement: Historical Overview
  • Mixed Model Methodology and the Box–Cox Theory of Transformations: A Bayesian Approach
  • Models for Discrimination Between Alternative Modes of Inheritance
• Part II: Design of Experiments and Breeding Programs
  • Considerations in the Design of Animal Breeding Experiments
  • Use of Mixed Model Methodology in Analysis of Designed Experiments
  • Statistical Aspects of Design of Animal Breeding Programs
  • Optimal Designs for Sire Evaluation Schemes
• Part III: Estimation of Genetic Parameters
  • Computational Aspects of Likelihood-Based Inference for Variance Components
  • Parameter Estimation in Variance Component Models for Binary Response Data
  • Estimation of Genetic Parameters in Non-Linear Models
• Part IV: Prediction and Estimation of Genetic Merit
  • A Framework for Prediction of Breeding Value
  • BLUP (Best Linear Unbiased Prediction) and Beyond
  • Connectedness in Genetic Evaluation
• Part V: Prediction and Estimation in Non-Linear Models
  • Generalized Linear Models and Applications to Animal Breeding
  • Analysis of Linear and Non-Linear Growth Models with Random Parameters
  • Survival, Endurance and Censored Observations in Animal Breeding
  • Genetic Evaluation for Discrete Polygenic Traits
• Part VI: Selection and Non-Random Mating
  • Accounting for Selection and Mating Biases in Genetic Evaluations
  • Statistical Inferences in Populations Undergoing Selection or Non-Random Mating
  • Problems in the Use of the Relationship Matrix in Animal Breeding
• Part VII: Statistics and New Genetic Technology
  • Identification of Genes with Large Effects
  • A General Linkage Method for the Detection of Major Genes
  • Reproductive Technology and Genetic Evaluation