Semiparametric methods for incomplete longitudinal count data with an application to health and retirement study.

In this paper, we propose and explore a novel semiparametric approach to analyzing longitudinal count data. We address the issue of missingness in longitudinal data and propose a weighted generalized estimations equations approach to fitting marginal mean response models for count responses with dropouts. Also, we investigate a spline regression approach to approximating the curvilinear relationship between the mean response and covariates. The asymptotic properties of the proposed estimators are studied in some detail. The empirical properties of the estimators are investigated using Monte Carlo simulations. An application is also provided using actual survey data obtained from the Health and Retirement Study (HRS).

A comparative study of estimators in multilevel linear models.

Multilevel Models are widely used in organizational research, educational research, epidemiology, psychology, biology and medical fields. In this paper, we recommend the situations where Bootstrap procedures through Minimum Norm Quadratic Unbiased Estimator (MINQUE) can be extremely handy than that of Restricted Maximum Likelihood (REML) in multilevel level linear regression models. In our simulation study the bootstrap by means of MINQUE is superior to REML in conditions where normality does not hold. Moreover, the real data application also supports our findings in terms of accuracy of estimates and their standard errors.

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments.

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.

Rotating flow assessment of magnetized mixture fluid suspended with hybrid nanoparticles and chemical reactions of species.

The current study characterizes the effects of Hall current, Arrhenius activation energy and binary chemical reaction on the rotating flow of hybrid nanofluid in two double disks. By the use of suitable similarity transformations, the system of partial differential equations and boundary conditions for hybrid nanofluid are transformed to ordinary differential equations which are solved through optimal homotopy analysis method. The intensified magnetic field and hybrid nanofluid performances are represented in three dimensional model with flow, heat and mass transfer. Radial velocity decreases and tangential velocity increases with the Hall parameter. Temperature rises with high values of rotation parameter while it decreases with the Prandtl number. Nanoparticles concentration enhances with the increments in Arrhenius activation energy parameter and stretching parameter due to lower disk. There exists a close and favorable harmony in the results of present and published work.