It is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed-effects model. Typical approaches perform selection separately on each of the fixed and random effect components. However, changing the structure of one set of effects can lead to different choices of variables for the other set of effects. We propose simultaneous selection of the fixed and random factors in a linear mixed-effects model using a modified Cholesky decomposition. Our method is based on a penalized joint log-likelihood with an adaptive penalty for the selection and estimation of both the fixed and random effects. It performs model selection by allowing fixed effects or standard deviations of random effects to be exactly zero. A constrained EM algorithm is then used to obtain the final estimates. It is further shown that the proposed penalized estimator enjoys the Oracle property, in that, asymptotically it performs as well as if the true model was known beforehand. We demonstrate the performance of our method based on a simulation study and a real data example.

The problem of selecting the correct subset of predictors within a linear model has received much attention in recent literature. Within the Bayesian framework, a popular choice of prior has been Zellner's g-prior which is based on the inverse of empirical covariance matrix of the predictors. An extension of the Zellner's prior is proposed in this article which allow for a power parameter on the empirical covariance of the predictors. The power parameter helps control the degree to which correlated predictors are smoothed towards or away from one another. In addition, the empirical covariance of the predictors is used to obtain suitable priors over model space. In this manner, the power parameter also helps to determine whether models containing highly collinear predictors are preferred or avoided. The proposed power parameter can be chosen via an empirical Bayes method which leads to a data adaptive choice of prior. Simulation studies and a real data example are presented to show how the power parameter is well determined from the degree of cross-correlation within predictors. The proposed modification compares favorably to the standard use of Zellner's prior and an intrinsic prior in these examples.

Abstract-The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by several entities, yet because the increasing range of image and video documents showing on the Internet, have attracted vital analysis efforts in providing tools for effective retrieval and management of visual data. So the need for image retrieval system arose. Out of many existing systems "ROTATION INVARIANT CONTENT-BASED IMAGE RETRIEVAL SYSTEM" is the most efficient and accurate one.Effective texture feature is an essential component in any CBIR system. In the past, spectral features like Gabor and Wavelet have shown superior retrieval performance than most statistical and structural options. Recent researches on multi-resolution analysis have found that curvelet captures texture properties like curves, lines and edges with additional accuracy than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance considerably, particularly in cases where there are many similar images with different orientations. We analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. The new system which uses curvelet transform for extracting texture features includes rotation invariant.