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Abstract:
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Estimation of the covariance structure of longitudinal processes is important for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a regularized "joint penalty" (JPEN) approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. A JPEN covariance estimator is obtained using a normal penalized likelihood with L1 and variance of eigenvalues. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision and flexibility of functional mapping while preserving its biological relevance. We do extensive simulation for varying sample sizes and dimensions for a number of structured covariance matrices to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the real life application of the methodology.
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