The empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty of EBLUP. To obtain a second-order unbiased estimator of the MSE, the second-order bias correction has been derived mainly based on Taylor series expansions. However, thi...

We derive an analytic expression for the bias, to O(n) of the maximum likelihood estimator of the scale parameter in the half-logistic distribution. Using this expression to bias-correct the estimator is shown to be very effective in terms of bias reduction, without adverse consequences for the estimator’s precision. The analytic bias-corrected estimator is also shown to be dramatically superio...

A short history is given of the development of the correction for observation selection bias inherent in the calibration of absolute magnitudes using trigonometric parallaxes. The developments have been due to Eddington, Jeffreys, Trumpler and Weaver, Wallerstein, Ljunggren and Oja, West, Lutz and Kelker after whom the bias is named, Turon Lacarrieu and Crézé, Hanson, Smith, and many others. As...

Estimation of the Saupe tensor is central to the determination of molecular structures from residual dipolar couplings (RDC) or chemical shift anisotropies. Assuming a given template structure, the singular value decomposition (SVD) method proposed in [15] has been used traditionally to estimate the Saupe tensor. Despite its simplicity, whenever the template structure has large structural noise...

M. Nezamzadeh, I. Cameron Physics, Carleton University, Ottawa, ON, Canada, Radiology, The Ottawa Hospital, Ottawa, ON, Canada Introduction: It is well known that pixel intensities of magnitude MR images are biased by Rician noise [1-4]. This is particularly noticeable for SNR ≤ 2. Rician noise bias corrections from the literature [2,3] work well for SNR > 2 but not for SNR < 2. Furthermore, th...

In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In...

Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a MR scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called...

Jianqing Fan Department of Statistics University of North Carolina Chapel Hill, N.C. 27514 Kernel density estimates are frequently used, based on a second order kernel. Thus, the bias inherent to the estimates has an order of O(h~). In this note, a method of corr~cting the bias in the kernel density estimates is provided, which reduces the bias to a smaller order. Effectively, this method produ...

We present a method to automatically estimate and remove the bias eld of MR images where there is a single dominant tissue class. Assuming that a multi-class image is corrupted by a multiplicative, lowfrequency bias eld, the method evaluates the bias eld on a single tissue class, and extends it to the whole image. The algorithm works iteratively, interleaving tissue class domain and bias eld es...

Bias field signal is a low-frequency and very smooth signal that corrupts MRI images specially those produced by old MRI (Magnetic Resonance Imaging) machines. Image processing algorithms such as segmentation, texture analysis or classification that use the graylevel values of image pixels will not produce satisfactory results. A pre-processing step is needed to correct for the bias field signa...