The objective of this study was to estimate the genetic variance for within-litter variation of birth weight (BW0) using genomic (GRM) or pedigree relationship matrices (PRM) and to compare the accuracy of estimated breeding values (EBV) for within-litter variation of BW0 using GRM and PRM. The BW0 and residual variance of BW0 were modeled by the double hierarchical generalized linear model usi...

In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor’s observation noise variance and II) using the ...

For a general regression model with n independent observations we consider the variance of the estimate of a quantity of interest under two scenarios. One scenario is where all the parameters are estimated from the data, the other scenario is where a subset of the parameters are assumed known at their true values and the remaining parameters are estimated. We focus on quantities of interest whi...

This thesis centers on nonparametric inferences of the cumulative hazard function of a right truncated variable. We present three variance estimators for the Nelson-Aalen estimator of the cumulative hazard function and conduct a simulation study to investigate their performances. A close match between the sampling standard deviation and the estimated standard error is observed when an estimated...

Heritability is a population parameter of importance in evolution, plant and animal breeding, and human medical genetics. It can be estimated using pedigree designs and, more recently, using relationships estimated from markers. We derive the sampling variance of the estimate of heritability for a wide range of experimental designs, assuming that estimation is by maximum likelihood and that the...

We consider estimates of the parameters of GARCH models obtained using auxiliary information on latent variance which may be available from higher-frequency data, for example from an estimate of the daily quadratic variation such as the realized variance. We obtain consistent estimates of the parameters of the infinite ARCH representation via a regression using the estimated quadratic variation...

The response of a linear time-invariant process on a stochastic input signal is characterized by the transfer function. Unknown past inputs and puture output are s0u1'ces of inaccuracy in relating a finite segment of an output signal via an estimated transfer function to the corresponding input segment. These end effects are usually characterized with error bounds on the Fourier transform of th...

Transform-both-sides nonlinear models have proved useful in many experimental applications including those in pharmaceutical sciences and biochemistry. The maximum likelihood method is commonly used to fit transform-both-sides nonlinear models, where the regression and transformation parameters are estimated simultaneously. In this paper, an analysis of variance-based method is described in det...

The correction is in two parts. See [3] for more information on the correction. 1. The LPEadj method discontinues the LPE practice of setting all variances below the maximum variance in the ordered distribution of variances to the maximum variance. In certain cases this practice can set many variances to the maximum and lower the performance of this algorithm. If the assumption that there are o...

We examine methods to estimate the average and variance of test error rates over a set of classi ers We begin with the process of drawing a classi er at random for each example Given validation data the average test error rate can be estimated as if validating a single classi er Given the test example inputs the variance can be computed exactly Next we consider the process of drawing a classi e...