Outputs (26)

Geometric multiaxial representation of N-qubit mixed symmetric separable states (2017)
Journal Article
Suma, S., Sirsi, S., Hegde, S., & Bharath, K. (2017). Geometric multiaxial representation of N-qubit mixed symmetric separable states. Physical Review A, 96(2), Article 022328. https://doi.org/10.1103/PhysRevA.96.022328

Study of an N qubit mixed symmetric separable states is a long standing challenging problem as there exist no unique separability criterion. In this regard, we take up the N-qubit mixed symmetric separable states for a detailed study as these states... Read More about Geometric multiaxial representation of N-qubit mixed symmetric separable states.

Statistical tests for large tree-structured data (2017)
Journal Article
Bharath, K., Kambadur, P., Dey, D. K., Rao, A., & Baladandayuthapani, V. (in press). Statistical tests for large tree-structured data. Journal of the American Statistical Association, 112(520), https://doi.org/10.1080/01621459.2016.1240081

We develop a general statistical framework for the analysis and inference of large tree-structured data, with a focus on developing asymptotic goodness-of-fit tests. We first propose a consistent statistical model for binary trees, from which we deve... Read More about Statistical tests for large tree-structured data.

A geometric approach to visualization of variability in functional data (2016)
Journal Article
Xie, W., Kurtek, S., Bharath, K., & Sun, Y. (in press). A geometric approach to visualization of variability in functional data. Journal of the American Statistical Association, 112(519), https://doi.org/10.1080/01621459.2016.1256813

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose o... Read More about A geometric approach to visualization of variability in functional data.

DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer (2016)
Journal Article
Saha, A., Banerjee, S., Kurtek, S., Narang, S., Lee, J., Rao, G., Martinez, J., Bharath, K., & Baladandayuthapani, V. (in press). DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. NeuroImage: Clinical, 12, https://doi.org/10.1016/j.nicl.2016.05.012

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data... Read More about DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer.

Bayesian sensitivity analysis with the Fisher–Rao metric (2015)
Journal Article
Kurtek, S., & Bharath, K. (2015). Bayesian sensitivity analysis with the Fisher–Rao metric. Biometrika, 102(3), https://doi.org/10.1093/biomet/asv026

We propose a geometric framework to assess sensitivity of Bayesian procedures to modelling assumptions based on the nonparametric Fisher–Rao metric. While the framework is general, the focus of this article is on assessing local and global robustness... Read More about Bayesian sensitivity analysis with the Fisher–Rao metric.

Spacings around and order statistic (2014)
Journal Article
Nagaraja, H. N., Bharath, K., & Zhang, F. (2015). Spacings around and order statistic. Annals of the Institute of Statistical Mathematics, 67(3), https://doi.org/10.1007/s10463-014-0466-9

We determine the joint limiting distribution of adjacent spacings around a central, intermediate, or an extreme order statistic Xk:n of a random sample of size n from a continuous distribution F. For central and intermediate cases, normalized spacing... Read More about Spacings around and order statistic.