Genotype imputation has become standard practice in modern genetic studies. As sequencing-based reference panels continue to grow, increasingly more markers are being well or better imputed but at the same time, even more markers with relatively low minor allele frequency are being imputed with low imputation quality. Here, we propose new methods that incorporate imputation uncertainty for down...

In the statistics literature, a number of procedures have been proposed for testing equality of several groups’ covariance matrices when data are complete, but this problem has not been considered for incomplete data in a general setting. This paper proposes statistical tests for equality of covariance matrices when data are missing. AWald test (denoted by T1), a likelihood ratio test (LRT) (de...

Cooperative spectrum sensing has proved to be an effective method improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based soft fusion of energy measurements at radios (CRs). In these systems, likelihood ratio test (LRT) is optimal rule, but sufficient statistic depends local signal-to-noise (SNR) CRs, which are unknown most practi...

Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene-by-gene (G × G), or gene-by-environment interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity (LRT(MV)) or either effect alone (LRT(M) or LRT(V)) in the presence of covariate...

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models...

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker-positive and biomarker-negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equa...

The high rates at which digital multimedia is being generated and used makes it necessary to develop systems that can process it in an efficient manner. This can be achieved by extracting semantics from processing the video’s low-level information. We present a novel algorithm which fuses color and motion information, in order to extract semantics from the video sequence. The motion estimates a...

A pedigree file consisting of 5860 individuals, 167 sires and 1582 dams collected at Makooei sheep breeding station (MSBS) during a period of 24 years (1990 to 2013) was used to calculate the inbreeding coefficients to reveal any probable effects of inbreeding (F) on the studied traits. The studied traits were classified to the five main groups including body weight, Kleiber ratio, body measure...

We consider the problem of testing null hypotheses that include restrictions on the variance component in a linear mixed model with one variance component. We derive the finite sample and asymptotic distribution of the likelihood ratio test (LRT) and the restricted likelihood ratio test (RLRT). The spectral representations of the LRT and RLRT statistics are used as the basis of an efficient sim...

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models...