Curvature Inspired Cosmological Scenario

Using modified gravity with non-linear terms of curvature, $R^2$ and $R^{(r +2)}$ (with $r$ being the positive real number and $R$ being the scalar curvature), cosmological scenario,beginning at the Planck scale, is obtained. Here, a unified picture of cosmology is obtained from $f(R)-$ gravity. In this scenario, universe begins with power-law inflation, followed by deceleration and acceleration in the late universe as well as possible collapse of the universe in future. It is different from $f(R)-$ dark energy models with non-linear curvature terms assumed as dark energy. Here, dark energy terms are induced by linear as well as non-linear terms of curvature in Friedmann equation being derived from modified gravity.It is also interesting to see that, in this model, dark radiation and dark matter terms emerge spontaneously from the gravitational sector. It is found that dark energy, obtained here, behaves as quintessence in the early universe and phantom in the late universe. Moreover, analogous to brane-tension in brane-gravity inspired Friedmann equation, a tension term $\lambda$ arises here being called as cosmic tension. It is found that, in the late universe, Friedmann equation (obtained here) contains a term $- \rho^2/2\lambda$ ($\rho$ being the phantom energy density) analogous to a similar term in Friedmann equation with loop quantum effects, if $\lambda > 0$ and brane-gravity correction when $\lambda < 0.$Comment: 19 Pages. To appear in Int. J. Thro. Phy

"Minimax Multivariate Empirical Bayes Estimators under Multicollinearity"

In this paper we consider the problem of estimating the matrix of regression coefficients in a multivariate linear regression model in which the design matrix is near singular. Under the assumption of normality, we propose empirical Bayes ridge regression estimators with three types of shrinkage functions,that is, scalar, componentwise and matricial shrinkage. These proposed estimators are proved to be uniformly better than the least squares estimator, that is, minimax in terms of risk under the Strawderman's loss function. Through simulation and empirical studies, they are also shown to be useful in the multicollinearity cases.

"Prediction in Multivariate Mixed Linear Models"

The multivariate mixed linear model or multivariate components of variance model with equal replications is considered.The paper addresses the problem of predicting the sum of the regression mean and the random e ects.When the feasible best linear unbiased predictors or empirical Bayes predictors are used,this prediction problem reduces to the estimation of the ratio of two covariance matrices.We propose scale invariant Stein type shrinkage estimators for the ratio of the two covariance matrices.Their dominance properties over the usual estimators including the unbiased one are established, and further domination results are shown by using information of order restriction between the two covariance matrices.It is also demonstrated that the empirical Bayes predictors that employs these improved estimators of the ratio of the two covariance matrices have uniformly smaller risks than the crude Efron-Morris type estimator in the context of estimation of a matrix mean in a xed e ects linear regression model where the components are unknown parameters.

"Estimating the Covariance Matrix: A New Approach"

In this paper, we consider the problem of estimating the covariance matrix and the generalized variance when the observations follow a nonsingular multivariate normal distribution with unknown mean. A new method is presented to obtain a truncated estimator that utilizes the information available in the sample mean matrix and dominates the James-Stein minimax estimator. Several scale equivariant minimax estimators are also given. This method is then applied to obtain new truncated and improved estimators of the generalized variance; it also provides a new proof to the results of Shorro k and Zidek (1976) and Sinha (1976).

"Minimax Empirical Bayes Ridge-Principal Component Regression Estimators"

In this paper, we consider the problem of estimating the regression parameters in a multiple linear regression model with design matrix A when the multicollinearity is present. Minimax empirical Bayes estimators are proposed under the assumption of normality and loss function (ƒÂ-s)t (At A)2 (ƒÂ- s)/ƒÐ2, where ƒÂ is an estimator of the vector s of p regression parameters, and ƒÐ2 is the unknown variance of the model. The minimax estimators are also obtained under linear constraints on s such as s = Cƒ¿ for some p ~ q known matrix C, q

"Improved Empirical Bayes Ridge Regression Estimators under Multicollinearity"

In this paper we consider the problem of estimating the regression parameters in a multiple linear regression model when the multicollinearity is present.Under the assumption of normality, we present three empirical Bayes estimators. One of them shrinks the least squares (LS) estimator towards the principal component. The second one is a hierarchical empirical Bayes estimator shrinking the LS estimator twice.The third one is obtained by choosing di erent priors for the two sets of regression parameters that arise in the case of multicollinearity;this estimator is termed decomposed empirical Bayes estimator. These proposed estimators are not only proved to be uniformly better than the LS estimator, that is,minimax in terms of risk under the Strawderman's loss function,but also shown to be useful in the multicollinearity cases through simulation and empirical studies.

Analyzing flow anisotropies with excursion sets in relativistic heavy-ion collisions

We show that flow anisotropies in relativistic heavy-ion collisions can be analyzed using a certain technique of shape analysis of excursion sets recently proposed by us for CMBR fluctuations to investigate anisotropic expansion history of the universe. The technique analyzes shapes (sizes) of patches above (below) certain threshold value for transverse energy/particle number (the excursion sets) as a function of the azimuthal angle and rapidity. Modeling flow by imparting extra anisotropic momentum to the momentum distribution of particles from HIJING, we compare the resulting distributions for excursion sets at two different azimuthal angles. Angles with maximum difference in the two distributions identify the event plane, and the magnitude of difference in the two distributions relates to the magnitude of momentum anisotropy, i.e. elliptic flow.Comment: 5 pages, 4 figure