By Gang Zheng
Analysis of Genetic organization reports is either a graduate point textbook in statistical genetics and genetic epidemiology, and a reference ebook for the research of genetic organization reviews. scholars, researchers, and pros will locate the themes brought in Analysis of Genetic organization Studies relatively correct. The ebook is appropriate to the research of records, biostatistics, genetics and genetic epidemiology.
In addition to offering derivations, the booklet makes use of genuine examples and simulations to demonstrate step by step functions. Introductory chapters on likelihood and genetic epidemiology terminology give you the reader with worthy history wisdom. The association of this paintings permits either informal reference and shut research.
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Extra resources for Analysis of Genetic Association Studies
D0 )T . The likelihood function is denoted by L(θ ) = i f (Xi |θ ). The log-likelihood function is given by l(θ ) = ni=1 log f (Xi |θ ). Under H0 , the log-likelihood function is l(θ0 ). , ∂θ1 ∂θd ∂ 2 l(θ ) ∂ 2 l(θ ) = ∂θ ∂θ T ∂θi ∂θj T , d×1 . d×d The Score function is defined as U (θ ) = l (θ ) and the observed Fisher information matrix is given by in (θ ) = −l (θ ). 2 Statistical Inference 21 and Score statistic throughout this book. This is to distinguish it from the scores used in the trend test in Chap.
2 Statistical Inference 19 allele is known, a one-sided alternative can be used and is given by either H1 : θ > 0 or H1 : θ < 0. In some cases, the parameter θ is a vector, which contains multiple parameters, for example, θ = (θ1 , θ2 )T . However, the null hypothesis may be written as H0 : θ1 = 0 where θ2 is not specified. In this case, θ2 is often called a nuisance parameter because only the parameter θ1 is of interest. A hypothesis is simple if all the values of unknown parameters are specified, while a hypothesis is composite if it does not specify all the values of the parameters.
The binomial distribution is denoted by B(n; p), where p1 = p and p2 = 1 − p. For the binomial random variable X (the number of successes in n trials) with the probability of success p, the distribution function can be written as Pr(X = x) = n! (n − x)! 0 ≤ x ≤ n. Let Xi be the number of ith outcomes of a multinomial random variable. The mean and variance of Xi are given by E(Xi ) = npi and Var(Xi ) = pi (1 − pi )/n. The covariance of two outcomes Xi and Xj is given by Cov(Xi , Xj ) = −pi pj /n for i = j .