Statistical Learning in Genetics
An Introduction Using R
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Chapter
Digression on Multiple Testing: False Discovery Rates
A classical single hypothesis test proceeds by specifying α \(\alpha \) , th...
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Chapter
Overview
Suppose there is a set of data consisting of observations in humans on forced expiratory volume (FEV, a measure of lung function; lung function is a predictor of health and a low lung function is a risk factor...
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Chapter
Bayesian Prediction and Model Checking
Aspects of Bayesian prediction have been addressed in previous chapters. In particular, Chaps. 7 and 9 show a Baye...
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Chapter
Computing the Likelihood
Estimation using the likelihood function proceeds by solving for θ \(\theta \) ...
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Exercises
In a binomial experiment with n trials and probability of success θ \(\theta \) ...
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McMC in Practice
This chapter illustrates applications of McMC in a Bayesian context. The treatment is mostly schematic; the objective is to present the mechanics of McMC in different modelling scenarios. Many of the examples,...
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Shrinkage Methods
Expression (6.51) indicates how prediction ability is governed by bias and variance. As models become more complex, local noise can be captured, but coefficient es...
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Chapter
Binary Data
Many of the results derived under the assumption that observations are continuously distributed extend to dichotomous and categorical responses. There are some technical details that must be observed that are ...
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Book
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Chapter
Likelihood
A central problem in statistics is the estimation of parameters that index a probability model proposed to describe aspects of the state of nature. In the classical approach to inference, these parameters have...
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Chapter
Nonparametric Methods: A Selected Overview
Throughout this book a phrase like “assume the data have been generated by the following probability model” has been abundantly used. Indeed, the standard parametric assumption is that observed data represent ...
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Chapter
Bayesian Methods
In classical likelihood, an important goal is to learn about a parameter θ \(\theta \) ...
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Solution to Exercises
The result of using the transformed parameter (13.8) translates into a more symmetric likelihood function, as displayed in Fig. 13.1. This in turn has consequences for the quality of inferences.
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Chapter
Fundamentals of Prediction
This chapter provides an overview of prediction with examples taken from quantitative genetics. The first part summarises best prediction and best linear prediction and offers a brief tour of the standard linear ...
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Article
Optimization of a dissimilar platinum to niobium microresistance weld: a structure–processing–property study
Dissimilar metal resistance spot welds, critical to the manufacture of medical devices, typically form brittle intermetallic compounds that are prone to failure. Here, a case study of biocompatible metals plat...
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Article
Open AccessEfficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice
Genomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and p...
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Article
Developments in statistical analysis in quantitative genetics
A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and comp...
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Article
Open AccessA comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics
In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with ge...
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Book
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Article
Mixture model for inferring susceptibility to mastitis in dairy cattle: a procedure for likelihood-based inference
A Gaussian mixture model with a finite number of components and correlated random effects is described. The ultimate objective is to model somatic cell count information in dairy cattle and to develop criteria...
